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  • 1.
    Abbaspour, Sara
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Surface EMG signal processing: Removing ECG interferences and classifying hand movements2017In: Medicinteknikdagarna 2017 MTD 2017, Västerås, Sweden, 2017Conference paper (Refereed)
  • 2.
    Abbaspour, Sara
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Electromyography signal analysis: Electrocardiogram artifact removal and classifying hand movements2018In: World Congress on Medical Physics and Biomedical Engineering IUPESM, 2018Conference paper (Refereed)
  • 3.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Personalized Health-Monitoring System for Elderly by Combining Rules and Case-based Reasoning2015In: Studies in Health Technology and Informatics, Volume 21: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, p. 249-254Conference paper (Refereed)
    Abstract [en]

    Health-monitoring system for elderly in home environment is a promising solution to provide efficient medical services that increasingly interest by the researchers within this area. It is often more challenging when the system is self-served and functioning as personalized provision. This paper proposed a personalized self-served health-monitoring system for elderly in home environment by combining general rules with a case-based reasoning approach. Here, the system generates feedback, recommendation and alarm in a personalized manner based on elderly’s medical information and health parameters such as blood pressure, blood glucose, weight, activity, pulse, etc. A set of general rules has used to classify individual health parameters. The case-based reasoning approach is used to combine all different health parameters, which generates an overall classification of health condition. According to the evaluation result considering 323 cases and k=2 i.e., top 2 most similar retrieved cases, the sensitivity, specificity and overall accuracy are achieved as 90%, 97% and 96% respectively. The preliminary result of the system is acceptable since the feedback; recommendation and alarm messages are personalized and differ from the general messages. Thus, this approach could be possibly adapted for other situations in personalized elderly monitoring.

  • 4.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. IS (Embedded Systems).
    Big Data Analytics in Health Monitoring at Home2017In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper (Refereed)
    Abstract [en]

    This paper proposed a big data analytics approach applied in the projects ESS-H and E-care@home in the context of biomedical and health informatics with the advancement of information fusion, data abstraction, data mining, knowledge discovery, learning, and reasoning [1][2]. Data are collected through the projects, considering both the health parameters, e.g. temperature, bio-impedance, skin conductance, heart sound, blood pressure, pulse, respiration, weight, BMI, BFP, movement, activity, oxygen saturation, blood glucose, heart rate, medication compliance, ECG, EMG, and EEG, and the environmental parameters e.g. force/pressure, infrared (IR), light/luminosity, photoelectric, room-temperature, room-humidity, electrical usage, water usage, RFID localization and accelerometers. They are collected as semi-structured/unstructured, continuous/periodic, digital/paper record, single/multiple patients, once/several-times, etc. and stored in a central could server [5]. Thus, with the help of embedded system, digital technologies, wireless communication, Internet of Things (IoT) and smart sensors, massive quantities of data (so called ‘Big Data’) with value, volume, velocity, variety, veracity and variability are achieved [2]. The data analysis work in the following three steps. In Step1, pre-processing, future extraction and selection are performed based on a combination of statistical, machine learning and signal processing techniques. A novel strategy to fuse the data at feature level and as well as at data level considers a defined fusion mechanism [3]. In Step2, a combination of potential sequences in the learning and search procedure is investigated. Data mining and knowledge discovery, using the refined data from the above for rule extraction and knowledge mining, with support for anomaly detection, pattern recognition and regression are also explored here [4]. In Step3, adaptation of knowledge representation approaches is achieved by combining different artificial intelligence methods [3] [4]. To provide decision support a hybrid approach is applied utilizing different machine learning algorithms, e.g. case-based reasoning, and clustering [4]. The approach offers several data analytics tasks, e.g. information fusion, anomaly detection, rules and knowledge extraction, clustering, pattern identification, correlation analysis, linear regression, logic regression, decision trees, etc. Thus, the approach assist in decision support, early detection of symptoms, context awareness and patient’s health status in a personal environment.

  • 5.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering.
    Xiong, Ning
    Mälardalen University, School of Innovation, Design and Engineering.
    von Schéele, Bo
    Mälardalen University, School of Innovation, Design and Engineering.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering.
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering.
    Intelligent Stress Management System2009In: Medicinteknikdagarna 2009, 2009Conference paper (Refereed)
    Abstract [en]

    Today, in our daily life we are subjected to a wide range of pressures. When the pressures exceed the extent that we are able to deal with then stress is trigged. High level of stress may cause serious health problems i.e. it reduces awareness of bodily symptoms. So, people may first notice it weeks or months later meanwhile the stress could cause more serious effect in the body and health. A difficult issue in stress management is to use biomedical sensor signals in the diagnosis and treatment of stress. This paper presents a case-based system that assists a clinician in diagnosis and treatment of stress. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques such as textual information retrieval, rule-based reasoning (RBR), and fuzzy logic have been combined together with case-based reasoning to enable more reliable and efficient diagnosis and treatment of stress. The performance has been validated implementing a research prototype and close collaboration with experts.

  • 6.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kerstis, Birgitta
    Mälardalen University, School of Health, Care and Social Welfare, Health and Welfare.
    Petrovic, Nikola
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sandborgh, Maria
    Mälardalen University, School of Health, Care and Social Welfare, Health and Welfare.
    Third Eye: An Intelligent Assisting Aid for Visual Impairment Elderly2016In: Medicinteknikdagarna 2016 MTF, 2016Conference paper (Refereed)
    Abstract [en]

    Background Visually impaired older persons need support in daily activities, e.g. moving around inside the house; making and eating food and taking medicine independently. A system that simulates the environment based on both dynamic and static objects, identify obstacles, navigates and translates sensory information in voice would be valuable to support their daily activities. Today several sensors and camera-based systems are popular as ambient-assisted living tools for older adults. However, intelligent assisting aid (IAA) to support older individuals with a recently acquired visual impairment is limited. The proposed system ‘Third Eye’ focuses on the advanced research and development of an IAA to support older individuals with a recently acquired visual impairment. The main goal in this system is to provide a usable, feasible and cost-effective solution for older persons to support their daily activities using intelligent sensor based system. Method The system consists of the following five phases to meet several central challenges in developing IAA in such domain. • User-perspective, focuses on user-driven technical development, investigating needs of potential users. The study will have a participatory design with focus group interviews of lead users. • Sensor-based system, focuses on the identification obstacles based on ultrasounds and/or radio frequencies embedded in white-cane or weaker. • Camera-based system, focuses on image based information translation into voice embedded in white-cane or weaker or glasses. • System of systems, focuses on integration of above systems where knowledge is engineered and suitable representations are learned and reasoning for decisions are made [9]. • Experimental, focuses on usability and feasibility of the IAA, with idiographic and group studies Results The initial results have shown the necessity of the proposed AAI systems for older individuals with a recently acquired visual impairment. However, more extension work e.g., process and analyze the information and synthesize it with existing literature for developing the system is ongoing.

  • 7.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Causevic, Aida
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fotouhi, Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An Overview on the Internet of Things for Health Monitoring Systems2015In: 2nd EAI International Conference on IoT Technologies for HealthCare HealthyIoT2015, 2015Conference paper (Refereed)
    Abstract [en]

    The aging population and the increasing healthcare cost in hospitals are spurring the advent of remote health monitoring systems. Advances in physiological sensing devices and the emergence of reliable low-power wireless network technologies have enabled the design of remote health monitoring systems. The next generation Internet, commonly referred to as Internet of Things (IoT), depicts a world populated by devices that are able to sense, process and react via the Internet. Thus, we envision health monitoring systems that support Internet connection and use this connectivity to enable better and more reliable services. This paper presents an overview on existing health monitoring systems, considering the IoT vision. We focus on recent trends and the development of health monitoring systems in terms of: (1) health parameters, (2) frameworks, (3) wireless communication, and (4) security issues. We also identify the main limitations, requirements and advantages within these systems.

  • 8.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Generic System-level Framework for Self-Serve Health Monitoring System through Internet of Things(IoT)2015In: Studies in Health Technology and Informatics, Volume 211: Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health, 2–4 June 2015, Västerås, Sweden, 2015, Vol. 211, p. 305-307Conference paper (Refereed)
    Abstract [en]

    Sensor data are traveling from sensors to a remote server, data is analysed remotely in a distributed manner, and health status of a user is presented in real-time. This paper presents a generic system-level framework for a self-served health monitoring system through the Internet of Things (IoT) to facilities an efficient sensor data management.

  • 9.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, Sweden.
    Espinosa, Jesica Rivero
    Technosite. Fundosa Group. R& D. Madrid, Spain.
    Reissner, Alenka
    Zveza Društev Upokojencev Slovenije Ljubljana, Slovenia.
    Domingo, Àlex
    Universitat Autònoma de Barcelona, Spain.
    Banaee, Hadi
    Örebro University, Sweden.
    Loutfi, Amy
    Örebro University, Sweden.
    Rafael-Palou, Xavier
    Barcelona Digital Technology Centre Spain.
    Self-Serve ICT-based Health Monitoring to Support Active Ageing2015In: 8th International Conference on Health Informatics HEALTHINF, 2015Conference paper (Refereed)
    Abstract [en]

    Today, the healthcare monitoring is not limited to take place in primary care facilities simply due to deployment of ICT. However, to support an ICT-based health monitoring, proper health parameters, sensor devices, data communications, approaches, methods and their combination are still open challenges. This paper presents a self-serve ICT-based health monitoring system to support active ageing by assisting seniors to participate in regular monitoring of elderly’s health condition. Here, the main objective is to facilitate a number of healthcare services to enable good health outcomes of healthy active living. Therefore, the proposed approach is identified and constructed three different kinds of healthcare services: 1) real time feedback generation service, 2) historical summary calculation service and 3) recommendation generation service. These services are implemented considering a number of health parameters, such as, 1) blood pressure, 2) blood glucose, 3) medication compliance, 4) weight monitoring, 5) physical activity, 6) pulse monitoring etc. The services are evaluated in Spain and Slovenia through 2 prototypical systems, i.e. year2prototype (Y2P) and year3prototype (Y3P) by 46 subjects (40 for Y2P and 6 for Y3P). The evaluation results show the necessity and competence of the proposed healthcare services. In addition, the prototypical system (i.e. Y3P) is found very much accepted and useful by most of the users.

  • 10.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fotouhi, Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Köckemann, Uwe
    Örebro University, Sweden.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomasic, Ivan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Run-Time Assurance for the E-care@home System2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 107-110Conference paper (Refereed)
    Abstract [en]

    This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care@home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care@home.

  • 11.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Healthcare Service at Home: An Intelligent Health Monitoring System for Elderly2015In: Medicinteknikdagarna 2015 MFT 2015, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents an intelligent healthcare service to support active ageing by assisting seniors to participate in regular monitoring of elderly’s health condition. The proposed system is applicable to use in home environment and offers a self-service approach to monitor elderly’s health condition. According to the evaluation, the proposed system shows its necessity, competence and usefulness.

  • 12.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Multi-parameter Sensing Platform in ESS-H and E-care@home2017In: Joint conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) EMBEC & NBC’17, 2017Conference paper (Refereed)
    Abstract [en]

    Considering the population of ageing, health monitoring of elderly at home have the possibility for a person to keep track on his/her health status, e.g. decreased mobility in a personal environment. This also shows the potential of real-time decision support, early detection of symptoms, following of health trends and context awareness [1]. The ongoing projects Embedded Sensor for Health (ESS-H)1 and E-care@home2 are focusing on health monitoring of elderly at home. This paper presents the implementation of multi-parameter sensing on an Android platform. The objectives are, both to follow health trends and to enabling real time monitoring.

  • 13.
    Ask, P.
    et al.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Ekstrand, K.
    ?.
    Hult, P.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Pettersson, N. -E
    Örebro County Council, Sweden.
    NovaMedTech - A regional program for supporting new medical technologies in personalized health care2012In: Studies in Health Technology and Informatics, 2012, p. 71-75Conference paper (Refereed)
    Abstract [en]

    NovaMedTech is an initiative funded from EU structural funds for supporting new medical technologies for personalized health care. It aims at bringing these technologies into clinical use and to the health care market. The program has participants from health care, industry and academia in East middle Sweden. The first three year period of the program was successful in terms of product concepts tried clinically, and number of products brought to a commercialization phase. Further, the program has led to a large number of scientific publications. Among projects supported, we can mention: Intelligent sensor networks; A digital pen to collect medical information about health status from patients; A web-based intelligent stethoscope; Methodologies to measure local blood flow and nutrition using optical techniques; Blood flow assessment from ankle pressure measurements; Technologies for pressure ulcer prevention; An IR thermometer for improved accuracy; A technique that identifies individuals prone to commit suicide among depressed patients; Detection of infectious disease using an electronic nose; Identification of the lactate threshold from breath; Obesity measurements using special software and MR camera; and An optical probe guided tumor resection. During the present three years period emphasis will be on entrepreneurial activities supporting the commercialization and bringing products to the market.

  • 14.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Gholamhosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Connolly, M. J.
    University of Auckland, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Advanced decision support system for older adults2015In: Studies in Health Technology and Informatics, vol. 211, 2015, p. 235-240Conference paper (Refereed)
    Abstract [en]

    Decision support systems are rapidly becoming part of today's healthcare delivery. The paradigm has shifted from traditional and manual recording to computer-based electronic records and, further, to handheld devices as versatile and innovative healthcare monitoring systems. The current study focuses on interpreting multiple physical signs and early warning for hospitalized older adults so that severe consequences can be minimized. Data from a total of 30 patients have been collated in New Zealand Hospitals under local and national ethics approvals. The system records blood pressure, heart rate (pulse), oxygen saturation (SpO2), ear temperature and blood glucose levels from hospitalized patients and transfers this information to a web-based software application for remote monitoring and further interpretation. Ultimately, this system is aimed to achieve a high level of agreement with clinicians' interpretation when assessing specific physical signs such as bradycardia, tachycardia, hypertension, hypotension, hypoxemia, fever and hypothermia and to generate early warnings. 

  • 15.
    Baig, M. M.
    et al.
    Auckland University of Technology, New Zealand.
    GholamHosseini, H.
    Auckland University of Technology, New Zealand.
    Moqeem, A. A.
    Auckland University of Technology, New Zealand.
    Mirza, F.
    Auckland University of Technology, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Systematic Review of Wearable Patient Monitoring Systems – Current Challenges and Opportunities for Clinical Adoption2017In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 41, no 7, article id 115Article in journal (Refereed)
    Abstract [en]

    The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and ‘silo’ solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology. 

  • 16.
    Baig, M. M.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Hosseini, H. G.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Machine learning-based clinical decision support system for early diagnosis from real-time physiological data2017In: Proceedings/TENCON, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 2943-2946, article id 7848584Conference paper (Refereed)
    Abstract [en]

    This research aims to design a self-organizing decision support system for early diagnosis of key physiological events. The proposed system consists of pre-processing, clustering and diagnostic system, based on self-organizing fuzzy logic modeling. The clustering technique was employed with empirical pattern analysis, particularly when the information available is incomplete or the data model is affected by vagueness, which is mostly the case with medical/clinical data. Clustering module can be viewed as unsupervised learning from a given dataset. This module partitions the patient vital signs to identify the key relationships, patterns and clusters among the medical data. Secondly, it uses self-organizing fuzzy logic modeling for early symptom and event detection. Based on the clustering outcome, when detecting abnormal signs, a high level of agreement was observed between system interpretation and human expert diagnosis of the physiological events and signs. © 2016 IEEE.

  • 17.
    Baig, M.M.
    et al.
    Auckland University of Technology.
    GholamHosseini, H.
    Auckland University of Technology.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tablet-based Patient Monitoring and Decision Support Systems in Hospital Care2015In: 2015 37TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2015, p. 1215-1218Conference paper (Refereed)
    Abstract [en]

    Remote patient monitoring with evidence-based decision support is revolutionizing healthcare. This novel approach could enable both patients and healthcare providers to improve quality of care and reduce costs. Clinicians can also view patients' data within the hospital network on tablet computers as well as other ubiquitous devices. Today, a wide range of applications are available on tablet computers which are increasingly integrating into the healthcare mainstream as clinical decision support systems. Despite the benefits of table-based healthcare applications, there are concerns around the accuracy, security and stability of such applications. In this study, we developed five tablet-based application screens for remote patient monitoring at hospital care settings and identified related issues and challenges. The ultimate aim of this research is to integrate decision support algorithms into the monitoring system in order to improve inpatient care and the effectiveness of such applications.

  • 18.
    Barua, Shaibal
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Begum, Shahina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Distributed Multivariate Physiological Signal Analytics for Driver´s Mental State Monitoring2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 26-33Conference paper (Refereed)
    Abstract [en]

    This paper presents a distributed data analytics approach for drivers’ mental state monitoring using multivariate physiological signals. Driver’s mental states such as cognitive distraction, sleepiness, stress, etc. can be fatal contributing factors and to prevent car crashes these factors need to be understood. Here, a cloud-based approach with heterogeneous sensor sources that generates extremely large data sets of physiological signals need to be handled and analyzed in a big data scenario. In the proposed physiological big data analytics approach, for driver state monitoring, heterogeneous data coming from multiple sources i.e., multivariate physiological signals are used, processed and analyzed to aware impaired vehicle drivers. Here, in a distributed big data environment, multi-agent case-based reasoning facilitates parallel case similarity matching and handles data that are coming from single and multiple physiological signal sources.

  • 19.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Ahmed, Mobyen Uddin
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Fusion Based System for Physiological Sensor Signal Classification2014In: Medicinteknikdagarna 2014 MTD10, 2014Conference paper (Refereed)
    Abstract [en]

    Today, usage of physiological sensor signals is essential in medical applications for diagnoses and classification of diseases. Clinicians often rely on information collected from several physiological sensor signals to diagnose a patient. However, sensor signals are mostly non-stationary and noisy, and single sensor signal could easily be contaminated by uncertain noises and interferences that could cause miscalculation of measurements and reduce clinical usefulness. Therefore, an apparent choice is to use multiple sensor signals that could provide more robust and reliable decision. Therefore, a physiological signal classification approach is presented based on sensor signal fusion and case-based reasoning. To classify Stressed and Relaxed individuals from physiological signals, data level and decision level fusion are performed and case-based reasoning is applied as classification algorithm. Five physiological sensor signals i.e., Heart Rate (HR), Finger Temperature (FT), Respiration Rate (RR), Carbon dioxide (CO2) and Oxygen Saturation (SpO2) are collected during the data collection phase. Here, data level fusion is performed using Multivariate Multiscale Entropy (MMSE) and extracted features are then used to build a case- library. Decision level fusion is performed on the features extracted using traditional time and frequency domain analysis. Case-Based Reasoning (CBR) is applied for the classification of the signals. The experimental result shows that the proposed system could classify Stressed or Relaxed individual 87.5% accurately compare to an expert in the domain. So, it shows promising result in the psychophysiological domain and could be possible to adapt this approach to other relevant healthcare systems.

  • 20.
    Begum, Shahina
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Kerstis, Birgitta
    Mälardalen University, School of Health, Care and Social Welfare, Health and Welfare.
    Barua, Shaibal
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Westerlund, Hanna
    Camanio Care AB, Sweden.
    Hjortsberg, Cecilia
    Västerås stad, Sweden.
    Food4You: A Personalized System for Adaptive Mealtime Situations for Elderly2017In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper (Refereed)
  • 21.
    Begum, Shahina
    et al.
    Mälardalen University, Department of Computer Science and Electronics. Dalarna University, Borlänge, Sweden .
    Westin, Jerker
    Dalarna University, Borlänge, Sweden .
    Funk, Peter
    Mälardalen University, Department of Computer Science and Electronics.
    Dougherty, Mark
    Dalarna University, Borlänge, Sweden .
    Induction of an Adaptive Neuro-Fuzzy Inference System for Investigating Fluctuation in Parkinson´s Disease: The 23rd Annual Workshop of the Swedish Artificial Intelligence Society Umeå, May 10-12, 20062006In: Proceedings of SAIS 2006, 2006, p. 67-72Conference paper (Refereed)
    Abstract [en]

    This paper presents a methodology to formulate natural language rules for an adaptive neuro-fuzzy system based on discovered knowledge, supported by prior knowledge and statistical modeling. These rules could be improved using statistical methods and neural nets. This gives clinicians a valuable tool to explore the importance of different variables and their relations in a disease and could aid treatment selection. A prototype using the proposed methodology has been used to induce an Adaptive Neuro Fuzzy Inference Model that has been used to "discover" relationships between fluctuation, treatment and disease severity in Parkinson. Preliminary results from this project are promising and show that Neuro-fuzzy techniques in combination with statistical methods may offer medical research and medical applications a useful combination of methods.

  • 22. Blobel, Bernd
    et al.
    Lindén, MariaMälardalen University, School of Innovation, Design and Engineering, Embedded Systems.Ahmed, Mobyen UddinMälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Proceedings of the 12th International Conference on Wearable Micro and Nano Technologies for Personalized Health: pHealth20152015Conference proceedings (editor) (Other academic)
  • 23.
    Brolin, Sandra
    Mälardalen University, School of Sustainable Development of Society and Technology.
    Global Regulatory Requirements for Medical Devices2008Independent thesis Basic level (professional degree), 20 points / 30 hpStudent thesis
    Abstract [en]

    Medical devices are becoming more important in the health care sector. One of the major issues for companies developing and producing medical devices is to be updated on the regulatory requirements and implement them in the process. This thesis examines the regulatory requirements for medical devices in Argentina, Australia, Brazil, Canada, India, Japan, Mexico, Russia, South Korea and Taiwan and compares them with the requirements in the European Union.

    The conclusion of this thesis is that most countries have similar requirements for registration of medical devices and are striving to harmonize with the GHTF guidelines. A company goes far by following the requirements in EU, USA or the GHTF guidelines.

  • 24.
    Bruch, Jessica
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Rösiö, Carin
    Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.
    Support for successful Production System Development: Handbook2015Report (Other academic)
    Abstract [en]

    For Swedish manufacturing companies active on the global market, high-performance production systems that contribute to the growth and competitiveness of the company are essential and one way to keep production in Sweden. Among a wide range of Swedish manufacturing companies it is becoming increasingly acknowledged that superior production system capabilities are crucial for competitive success. This being said, major attention has been paid to improving the operational performance of the production system. The focus in industry is mostly on the serial making of products, rather than on the prior development of the corresponding production system. At the end of the day, the real root cause of many problems and losses in production stem from issues that emanate from the development process of the production system. The potential of gaining a competitive edge by improving both the way the production system is developed and the way it is operated is hence ignored, even though it is a well-known fact that it is during the design phase that the most important decisions are made. In today’s industry, production system development is often still made ad hoc on the basis of past experiences and without any long-term perspective. If the production system is not designed in a proper way, it will eventually result in disturbances during both start-up and serial production. This leads to low capacity utilization, high production cost, and hence low profitability. To succeed, commitment is required as well as a shift in attention from the operations phase to the under-utilized potential of the design of production systems. The ideal outcome of production system development is the best possible production system that can easily be realized and is high-performing in operation. This will contribute to the growth and competitiveness of the company. To stay competitive, a shift in mind-set is required at many Swedish industries. Production system development is not only something that should work; it must be regarded as a competitive means and consequently be worked with systematically.

  • 25.
    Causevic, Aida
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Fotouhi, Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lundqvist, Kristina
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Data Security and Privacy in Cyber-Physical Systems for Healthcare2017In: Security and Privacy in Cyber-Physical Systems: Foundations, Principles, and Applications / [ed] Houbing Song D, Glenn A. Fink PhD, and Sabina Jeschke Dr. rer. nat., Wiley-IEEE Press , 2017, p. 305-320Chapter in book (Other academic)
  • 26.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Motion Control i Västerås AB, Västerås.
    Gerdtman, C.
    Motion Control i Västerås AB, Västerås.
    Gharehbaghi, Arash
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A signal processing algorithm for improving the performance of a gyroscopic head-borne computer mouse2017In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 35, p. 30-37Article in journal (Refereed)
    Abstract [en]

    This paper presents a signal processing algorithm to remove different types of noise from a gyroscopic head-borne computer mouse. The proposed algorithm is a combination of a Kalman filter (KF), a Weighted-frequency Fourier Linear Combiner (WFLC) and a threshold with delay method (TWD). The gyroscopic head-borne mouse was developed to assist persons with movement disorders. However, since MEMS-gyroscopes are usually sensitive to environmental disturbances such as shock, vibration and temperature change, a large portion of noise is added at the same time as the head movement is sensed by the MEMS-gyroscope. The combined method is applied to the specially adapted mouse, to filter out different types of noise together with the offset and drift, with marginal need of the calculation capacity. The method is examined with both static state tests and movement operation tests. Angular position is used to evaluate the errors. The results demonstrate that the combined method improved the head motion signal substantially, with 100.0% error reduction during the static state, 98.2% position error correction in the case of movements without drift and 99.9% with drift. The proposed combination in this paper improved the static stability and position accuracy of the gyroscopic head-borne mouse system by reducing noise, offset and drift, and also has the potential to be used in other gyroscopic sensor systems to improve the accuracy of signals. 

  • 27.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Development of a MEMS-sensor based motion analysis system for human movement rehabilitation2017In: International conference on movement: brain, body, cognition Movement2017, 2017Conference paper (Refereed)
  • 28.
    Du, Jiaying
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Gerdtman, Christer
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Signal processing to improve the MEMS sensor signal in a small embedded sensor system for health2017In: Medicinteknikdagarna 2017 MTD 2017, 2017Conference paper (Refereed)
  • 29.
    Ehn, Maria
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Carlén Eriksson, Lennie
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Åkerberg, Nina
    Västerås Municipality, Västerås, Sweden.
    Johansson, Ann-Christin
    Mälardalen University, School of Health, Care and Social Welfare, Health and Welfare.
    Activity Monitors as Support for Older Persons’ Physical Activity in Daily Life: Qualitative Study of the Users’ Experiences2018In: JMIR mhealth and uhealth, E-ISSN 2291-5222, Vol. 6, no 2, article id e34Article in journal (Refereed)
    Abstract [en]

    Background

    Falls are a major threat to the health and independence of seniors. Regular physical activity (PA) can prevent 40% of all fall injuries. The challenge is to motivate and support seniors to be physically active. Persuasive systems can constitute valuable support for persons aiming at establishing and maintaining healthy habits. However, these systems need to support effective behavior change techniques (BCTs) for increasing older adults’ PA and meet the senior users’ requirements and preferences. Therefore, involving users as codesigners of new systems can be fruitful. Prestudies of the user’s experience with similar solutions can facilitate future user-centered design of novel persuasive systems.

    Objective

    The aim of this study was to investigate how seniors experience using activity monitors (AMs) as support for PA in daily life. The addressed research questions are as follows: (1) What are the overall experiences of senior persons, of different age and balance function, in using wearable AMs in daily life?; (2) Which aspects did the users perceive relevant to make the measurements as meaningful and useful in the long-term perspective?; and (3) What needs and requirements did the users perceive as more relevant for the activity monitors to be useful in a long-term perspective?

    Methods

    This qualitative interview study included 8 community-dwelling older adults (median age: 83 years). The participants’ experiences in using two commercial AMs together with tablet-based apps for 9 days were investigated. Activity diaries during the usage and interviews after the usage were exploited to gather user experience. Comments in diaries were summarized, and interviews were analyzed by inductive content analysis.

    Results

    The users (n=8) perceived that, by using the AMs, their awareness of own PA had increased. However, the AMs’ impact on the users’ motivation for PA and activity behavior varied between participants. The diaries showed that self-estimated physical effort varied between participants and varied for each individual over time. Additionally, participants reported different types of accomplished activities; talking walks was most frequently reported. To be meaningful, measurements need to provide the user with a reliable receipt of whether his or her current activity behavior is sufficient for reaching an activity goal. Moreover, praise when reaching a goal was described as motivating feedback. To be useful, the devices must be easy to handle. In this study, the users perceived wearables as easy to handle, whereas tablets were perceived difficult to maneuver. Users reported in the diaries that the devices had been functional 78% (58/74) of the total test days.

    Conclusions

    Activity monitors can be valuable for supporting seniors’ PA. However, the potential of the solutions for a broader group of seniors can significantly be increased. Areas of improvement include reliability, usability, and content supporting effective BCTs with respect to increasing older adults’ PA.

  • 30.
    Ehn, Maria
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Hansson, P.
    SICS Swedish ICT, Kista, Sweden.
    Sjölinder, M.
    SICS Swedish ICT, Kista, Sweden.
    Boman, I. -L
    Karolinska Institutet, Solna, Sweden .
    Folke, Mia
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sommerfeld, D.
    SICS Swedish ICT, Kista, Sweden.
    Borg, J.
    SICS Swedish ICT, Kista, Sweden.
    Palmcrantz, S.
    SICS Swedish ICT, Kista, Sweden.
    Users perspectives on interactive distance technology enabling home-based motor training for stroke patients2015In: Studies in Health Technology and Informatics, vol. 211, 2015, p. 145-152Conference paper (Refereed)
    Abstract [en]

    The aim of this work has been to develop a technical support enabling home-based motor training after stroke. The basis for the work plan has been to develop an interactive technical solution supporting three different groups of stroke patients: (1) patients with stroke discharged from hospital with support from neuro team; (2) patients with stroke whose support from neuro team will be phased out and (3) patients living with impaired motor functions long-term. The technology has been developed in close collaboration with end-users using a method earlier evaluated and described [12]. This paper describes the main functions of the developed technology. Further, results from early user-tests with end-users, performed to identify needs for improvements to be carried out during further technical development. The developed technology will be tested further in a pilot study of the safety and, usefulness of the technology when applied as a support for motor training in three different phases of the post-stroke rehabilitation process. 

  • 31.
    Folke, Mia
    Mälardalen University, Department of Computer Science and Electronics.
    A system for optimizing an athletes performance2006Conference paper (Refereed)
  • 32.
    Folke, Mia
    Mälardalen University, Department of Computer Science and Electronics.
    Ett system som optimerar prestationsförmågan hos en idrottare2007Conference paper (Refereed)
  • 33.
    Folke, Mia
    Mälardalen University, Department of Computer Science and Electronics.
    Utveckling av ett system för identifiering av laktattröskeln2007Conference paper (Refereed)
  • 34.
    Folke, Mia
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Granstedt, Fredrik
    Hök, Bertil
    Hök Instrument AB, Västerås, Sweden .
    Scheer, Håkan
    Västerås Central Hospital, Sweden.
    Comparative Provocation Test of Respiratory Monitoring Methods2002In: Journal of Clinical Monitoring and Computing, ISSN 1387-1307, Vol. 17, no 2, p. 97-103Article in journal (Refereed)
    Abstract [en]

    Objective. The aim of this study was to compare clinically relevant performance of: 1) a prototype respiratory sensor based on capnometry with two alternative signal receptor fixations, 2) a fiberoptic humidity sensor and 3) human visual observation. Comparative provocation tests were performed on volunteers at the Post-Anesthesia Care Unit at Västerås Central Hospital. Methods. The experimental tests involved 10 healthy, voluntary test subjects, instructed to intersperse normal breathing with protocol provocations of breath holding, limb and head movements, and nasal oxygen supplement. The signal outputs from the three respiratory monitoring methods were recorded on a personal computer. The signal analysis included visual categorising of the signals and counting breath events. Recognising that none of the methods could act as reference, events were classified as "unanimous," "majority" or "minority" events depending on how many of the three methods that detected a breath. Results. The average total recording time was 37 minutes per subject. The respiratory rates varied from 6.5 to 19 breaths per minute, with a mean value of 11.4 breaths/minute. The breath hold duration ranged from 18 to 50 seconds. Discrepancies between the three methods were found in more than 20% of the marked events. The most frequent majority events were due to events not recorded by the observer who, on the other hand, contributed the least to minority events. The provocations made by the subjects during the measurement did not increase the rates of majority and minority events, compared to periods of no provocation. The fiberoptic device exhibited a large count of minority events but a smaller contribution to majority events than the capnometry prototype. Conclusions. The capnometry and fiberoptic sensors exhibit differences in responses that may be understood from basic principles. The importance of the physical application of the sensor to the patient was clearly observed. The optimum design remains to be found.

  • 35.
    Funk, Peter
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Why hybrid case-based reasoning will change the future of health science and healthcare2015In: CEUR Workshop Proceedings, 2015, p. 199-204Conference paper (Refereed)
    Abstract [en]

    The rapid development of the medical field makes it impossible even for experts in the field to keep up with new treatments and experience. Already in 2010 all medical knowledge doubled in 3,5 years, to keep up to date with all development even in a narrow field is today far beyond human capacity. The need for decision support is increasingly important to ensure optimal treatment of patients, especially if patients are not "standard patients" matching a gold standard treatment. By ensuring confidentiality and collecting structured cases on a large scale will enable clinical decision support far beyond what is possible today and will be a major leap in healthcare. 

  • 36.
    Funk, Peter
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Olsson, Erik
    Mälardalen University, Department of Computer Science and Electronics.
    Bengtsson, Marcus
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Case-Based Experience Reuse and Agents for Efficient Health Monitoring, Prevention and Corrective Actions2006In: Proceedings of the 19th International Congress on Condition, COMADEM 2006, Luleå, Sweden, 2006, p. 445-453Conference paper (Refereed)
    Abstract [en]

    Experienced staffs acquire their experience during many years of practice, and sometimes also through expensive mistakes. This knowledge is often lost when technicians retire, or if companies need to downsize during periods of reduced sale. When scaling up production, new staff requires training and may repeat similar mistakes. Another issue that may be costly is when monitoring systems repeatedly give false alarms, causing expensive loss of production capacity and resulting in technicians losing trust in the systems and in worst case, switch them off. If monitoring systems could learn from previous experience for both correct and false alarms, the reliability and trust in the monitoring systems would increase. Moreover, connecting alarms to either equipment taking automatic actions or recommend actions based on the current situations and previous experience would be valuable.

    An engineer repeating the same task a second time is often able to perform the task in 1/3 of the time it took at the first time. Most corrective and preventive actions for a particular machine type have been carried out before. This past experience holds a large potential for time savings, predictability and reduced risk if an efficient experience transfer can be accomplished. But building large complex support system is not always the ideal way. We propose instead localized intelligent agents, able to either autonomously perform the necessary actions or aid a human in the decision making process by providing the necessary information needed to make an informed and validated decision.

  • 37.
    Funk, Peter
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Xiong, Ning
    Mälardalen University, Department of Computer Science and Electronics.
    Discovering Knowledge about Key Sequences for Indexing Time Series Cases2006In: Advances in Case-Based Reasoning: 8th European Conference, ECCBR 2006 Fethiye, Turkey, September 4-7, 2006 Proceedings, 2006, p. 474-488Conference paper (Refereed)
    Abstract [en]

    Coping with time series cases is becoming an important issue in case based reasoning. This paper develops a knowledge discovery approach to discovering significant sequences for depicting symbolic time series cases. The input is a case library containing time series cases consisting of consecutive discrete patterns. The proposed approach is able to find from the given case library all qualified sequences that are non-redundant and indicative. A sequence as such is termed as a key sequence. It is shown that the key sequences discovered are highly usable in case characterization to capture important properties while ignoring random trivialities. The main idea is to transform an original (lengthy) time series into a more concise representation in terms of the detected occurrences of key sequences. Three alternate ways to develop case indexes based on key sequences are suggested. These indexes are simply vectors of numbers that are easily usable when matching two time series cases for case retrieval.

  • 38.
    Gardasevic, Gordana
    et al.
    University of Banja Luka, Bosnia-Herzegovina.
    Fotouhi, Hossein
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Tomasic, Ivan
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Vahabi, Maryam
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Björkman, Mats
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Heterogeneous IoT-based Architecture for Remote Monitoring of Physiological and Environmental Parameters2018In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 48-53Conference paper (Refereed)
    Abstract [en]

    A heterogeneous Internet of Things (IoT) architecture for remote health monitoring (RHM) is proposed, that employs Bluetooth and IEEE 802.15.4 wireless connectivity. The RHM system encompasses Shimmer physiological sensors with Bluetooth radio, and OpenMote environmental sensors with IEEE 802.15.4 radio. This system architecture collects measurements in a relational database in a local server to implement a Fog node for fast data analysis as well as in a remote server in the Cloud. 

  • 39.
    Gerdtman, Christer
    et al.
    Motion Control AB, Västerås, Sweden.
    Lindén, Maria
    Mälardalen University, Department of Computer Science and Electronics.
    A MEMS-gyro based computer mouse for disabled2006Conference paper (Refereed)
    Abstract [en]

    Computers are an important part of our daily lifes, and to be able to control them is in many cases necessary to be able to work, communicate or take part in our modern society of today. Therefore are persons that can not use an ordinary computer mouse in need of an alternative mouse. The reason of why an ordinary mouse can not be used is very different from person to person, depending on their abilities and needs. The need can also vary over time, not all persons are permanent handicapped, it can be a disability or bodily injury that can be healed. From these aspects, a computer mouse that can be used by a wide range of people with different types of disabilties and that can be applied on different body parts has been developed and evaulated.

  • 40.
    Gerdtman, Christer
    et al.
    Mälardalen University, Department of Computer Science and Electronics.
    Lindén, Maria
    Mälardalen University, Department of Computer Science and Electronics.
    Utveckling av en gyrobaserad datormus for funktionshindrade med begränsad rörelseförmåga2006Conference paper (Refereed)
    Abstract [en]

    Att kunna anvanda en dator ar ofta en forutsattning idag for att en person ska kunna utfora sitt arbete, ta del av information, kommunicera med andra och pa andra satt ta del av vart moderna samhalle. Darfor behover personer som inte kan anvanda en vanlig datormus, en alternativ inmatningsenhet for att kunna styra datorn. Personer med begransad rorelseformaga har behov av en mycket kanslig mus, som kan kanna av aven sma rorelser. Syftet med denna studie var att undersoka vilka kriterier som kan stallas pa en alternativ mus jamfort med en vanlig datormus som styrs via ena handen, att utveckla en prototyp samt att lata anvandarna prova och utvardera prototypen.

  • 41.
    Ghareh Baghi, Arash
    et al.
    Linköping University, Sweden.
    Ask, P.
    Linköping University, Sweden.
    Babic, A.
    Linköping University, Sweden.
    A pattern recognition framework for detecting dynamic changes on cyclic time series2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 3, p. 696-708Article, review/survey (Refereed)
    Abstract [en]

    This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DEBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for the classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.

  • 42.
    Ghareh Baghi, Arash
    et al.
    Linköping University, Linköping, Sweden.
    Borga, Magnus
    Linköping University, Linköping, Sweden.
    Janerot Sjöberg, Birgitta
    Linköping University, Linköping, Sweden.
    Ask, Per
    Linköping University, Linköping, Sweden.
    A novel method for discrimination between innocent and pathological heart murmurs2015In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 37, no 7, p. 674-682Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.

  • 43.
    Ghareh Baghi, Arash
    et al.
    Linköping University, Linköping, Sweden.
    Dutoit, Thierry
    Mons University, Mons, Belgium.
    Ask, Per
    Linköping University, Linköping, Sweden.
    Sörnmo, Leif
    Lund University, Lund, Sweden .
    Detection of systolic ejection click using time growing neural network2014In: Medical Engineering and Physics, ISSN 1350-4533, E-ISSN 1873-4030, Vol. 36, no 4, p. 477-483Article in journal (Refereed)
    Abstract [en]

    In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.

  • 44.
    Ghareh Baghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Linköping University, Linköping, Sweden.
    Ekman, I.
    Linköping University, Linköping, Sweden.
    Ask, P.
    Linköping University, Linköping, Sweden.
    Nylander, E.
    Linköping University, Linköping, Sweden.
    Janerot-Sjoberg, B.
    Karolinska University Hospital, Stockholm, Sweden.
    Assessment of aortic valve stenosis severity using intelligent phonocardiography2015In: International Journal of Cardiology, ISSN 0167-5273, E-ISSN 1874-1754, Vol. 198, p. 58-60Article in journal (Other academic)
  • 45.
    Gharehbaghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Linköping University, Linköping, Sweden .
    Ask, P.
    Linköping University, Linköping, Sweden .
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Babic, A.
    Linköping University, Linköping, Sweden.
    A Novel Model for Screening Aortic Stenosis Using Phonocardiogram2014In: IFMBE Proceedings, Volume 48, Springer , 2014, Vol. 48, p. 48-51Conference paper (Other academic)
    Abstract [en]

    This study presents an algorithm for screening aortic stenosis, based on heart sound signal processing. It benefits from an artificial intelligent-based (AI-based) model using a multi-layer perceptron neural network. The AI-based model learns disease related murmurs using non-stationary features of the murmurs. Performance of the model is statistically evaluated using two different databases, one of children and the other of elderly volunteers with normal heart condition and aortic stenosis. Results showed a 95% confidence interval of the high accuracy/sensitivity thus exhibiting a superior performance to a cardiologist who relies on the conventional auscultation. The study suggests including the heart sound signal in the clinical decision making due to its potential to improve the screening accuracy.

  • 46.
    Gharehbaghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Dutoit, T.
    Mons University, Mons, Belgium .
    Sepehri, A. A.
    CAPIS Biomedical Research and Department Center, Mons, Belgium.
    Kocharian, A.
    Tehran University of Medical Sciences, Tehran, Iran.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve2015In: Cardiovascular Engineering and Technology, ISSN 1869-408X, E-ISSN 1869-4098, Vol. 6, no 4, p. 546-556Article in journal (Refereed)
    Abstract [en]

    This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy. 

  • 47.
    Gharehbaghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An Internet-Based Tool for Pediatric Cardiac Disease Diagnosis using Intelligent Phonocardiography2016In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2016, Vol. 169, p. 443-447Conference paper (Refereed)
    Abstract [en]

    This paper suggests an internet-based tool for cardiac diagnosis in children. The main focus of the paper is the intelligent algorithms for processing heart sounds that are implementable on an internet platform. The algorithms are based on the statistical classification methods, tailored for the heart sound signal processing. The algorithms, applied to 55 healthy and 45 children with congenital heart diseases. The accuracy of the algorithm is estimated to be 86.0 % in screening the children with pathological murmurs, and 95.7 %, 92.9 % and 91.4 % in detecting the children with aortic stenosis, pulmonary stenosis and mitral regurgitation, respectively, showing an acceptable performance to be employed as a decision support tool.

  • 48.
    Gharehbaghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sepehri, A. A.
    CAPIS Biomedical Research and Department Center, Mons, Belgium.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Babic, A.
    Linköping University, Sweden.
    A hybrid machine learning method for detecting cardiac ejection murmurs2017In: IFMBE Proceedings, Springer Verlag , 2017, p. 787-790Conference paper (Refereed)
    Abstract [en]

    This paper presents a novel method for detecting cardiac ejection murmurs from other pathological and physiological heart murmurs in children. The proposed method combines a hybrid model and a time growing neural network for an improved detection even in mild condition. Children with aortic stenosis and pulmonary stenosis comprised the patient category against the reference category containing mitral regurgitation, ventricular septal defect, innocent murmur and normal (no murmur) conditions. In total, 120 referrals to a children University hospital participated to the study after giving their informed consent. Confidence interval of the accuracy, sensitivity and specificity is estimated to be 87.2% ̶ 88.8%, 83.4% ̶ 86.9% and 88.3% ̶ 90.0%, respectively. 

  • 49.
    Gharehbaghi, Arash
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Sepehri, A. A.
    CAPIS Biomedical Research and Development Center, Mon, Belgium.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Babic, A.
    Linköping University, Sweden.
    Intelligent phonocardiography for screening ventricular septal defect using time growing neural network2017In: Studies in Health Technology and Informatics, vol 238, IOS Press , 2017, Vol. 238, p. 108-111Chapter in book (Refereed)
    Abstract [en]

    This paper presents results of a study on the applicability of the intelligent phonocardiography in discriminating between Ventricular Spetal Defect (VSD) and regurgitation of the atrioventricular valves. An original machine learning method, based on the Time Growing Neural Network (TGNN), is employed for classifying the phonocardiographic recordings collected from the pediatric referrals to a children hospital. 90 individuals, 30 VSD, 30 with the valvular regurgitation, and 30 healthy subjects, participated in the study after obtaining the informed consents. The accuracy and sensitivity of the approach is estimated to be 86.7% and 83.3%, respectively, showing a good performance to be used as a decision support system. .

  • 50.
    Gholamhosseini, H.
    et al.
    Auckland University of Technology, Department of Electrical and Electronic Engineering, Auckland, New Zealand.
    Baig, M. M.
    Auckland University of Technology, Department of Electrical and Electronic Engineering, Auckland, New Zealand.
    Connolly, M. J.
    Freemasons' Department of Geriatric Medicine, University of Auckland and North Shore Hospital, Takapuna, Auckland, New Zealand.
    Lindén, Maria
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    A multifactorial falls risk prediction model for hospitalized older adults2014In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014, 2014, p. 3484-3487Conference paper (Refereed)
    Abstract [en]

    Ageing population worldwide has grown fast with more cases of chronic illnesses and co-morbidity, involving higher healthcare costs. Falls are one of the leading causes of unintentional injury-related deaths in older adults. The aim of this study was to develop a robust multifactorial model toward the falls risk prediction. The proposed model employs real-time vital signs, motion data, falls history and muscle strength. Moreover, it identifies high-risk individuals for the development falls in their activity of daily living (ADL). The falls risk prediction model has been tested at a controlled-environment in hospital with 30 patients and compared with the results from the Morse fall scale. The simulated results show the proposed algorithm achieved an accuracy of 98%, sensitivity of 96% and specificity of 100% among a total of 80 intentional falls and 40 ADLs. The ultimate aim of this study is to extend the application to elderly home care and monitoring.

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