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Publications (10 of 143) Show all publications
Lindén, M. & Björkman, M. (2019). Experience from industrial graduate (PhD) schools. In: IFMBE Proceedings: . Paper presented at World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018 (pp. 731-733). Springer Verlag (3)
Open this publication in new window or tab >>Experience from industrial graduate (PhD) schools
2019 (English)In: IFMBE Proceedings, Springer Verlag , 2019, no 3, p. 731-733Conference paper, Published paper (Refereed)
Abstract [en]

Traditionally, research education is performed within the universities, and the PhD students are working within a research group. However, technical development and also research is performed within companies, and the need to keep up with the latest findings in research and to strengthen the competence within the private business sector is increasing. At Mälardalen University, we have experience from working in several Industrial Graduate Schools. The collaboration with the companies gets intensified and deepened trough such programmes, and the university tends to keep the good contact with previous PhD students and their companies also many years after their graduation. The Graduate Schools also give the companies good insight in the university world. Presently, we are involved in two Graduate Schools, and several of the PhD projects are focusing within Biomedical Engineering. Further, one of the graduate schools is linked to the research profile Embedded Sensor Systems for Health, which is supported from the same financier. Companies are involved also in the research profile, and through these activities, the Industrial PhD students form a critical mass and can exchange both experience and knowledge with other companies and with university researchers.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Collaboration with industry, Industrial graduate school, Biomedical engineering, Embedded systems, Knowledge management, Students, Business sector, Collaboration with industries, Critical mass, Embedded sensors, Graduate schools, Research groups, Technical development, University researchers, Industrial research
National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-39978 (URN)10.1007/978-981-10-9023-3_132 (DOI)2-s2.0-85048251456 (Scopus ID)
Conference
World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2018-06-21Bibliographically approved
Tomasic, I., Rashkovska, A., Trobec, R. & Lindén, M. (2019). The implications of the lead theory on the patch ECG devices positioning and measurement. In: IFMBE Proceedings: . Paper presented at World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018 (pp. 693-696). Springer Verlag (1)
Open this publication in new window or tab >>The implications of the lead theory on the patch ECG devices positioning and measurement
2019 (English)In: IFMBE Proceedings, Springer Verlag , 2019, no 1, p. 693-696Conference paper, Published paper (Refereed)
Abstract [en]

Currently we are witnessing fast development of patch ECG devices, some of which have already been extensively evaluated and shown to be useful for detecting arrhythmias. The research about using the patch ECG devices for purposes other than arrhythmia detection has been scarce. The efficiency of patch electrocardiography for a specific purpose can depend on the devices location on the body surface. It is still an open question where to position the ECG patch devices, and should the position depend on the specific purpose and perhaps even be personalized. We present the lead theory of differential leads (ECG leads obtained by patch ECG devices) and discuss its implications on the patch ECG devices positioning on the body surface.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Bipolar lead, Differential lead, ECG, Electrocardiography, Lead theory, Patch monitors, Remote monitoring, Telemonitoring, Biomedical engineering, Arrhythmia detection, Body surface, ECG devices, Ecg patches, Tele-monitoring
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-39976 (URN)10.1007/978-981-10-9035-6_128 (DOI)2-s2.0-85048259555 (Scopus ID)
Conference
World Congress on Medical Physics and Biomedical Engineering, WC 2018, 3 June 2018 through 8 June 2018
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2018-06-21Bibliographically approved
Ghareh Baghi, A. & Lindén, M. (2018). A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network. IEEE Transactions on Neural Networks and Learning Systems, 29(9), 4102-4115, Article ID 8066455.
Open this publication in new window or tab >>A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network
2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 9, p. 4102-4115, article id 8066455Article in journal (Refereed) Published
Abstract [en]

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40920 (URN)10.1109/TNNLS.2017.2754294 (DOI)000443083700014 ()2-s2.0-85052718715 (Scopus ID)
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2018-09-13Bibliographically approved
Gardasevic, G., Fotouhi, H., Tomasic, I., Vahabi, M., Björkman, M. & Lindén, M. (2018). A Heterogeneous IoT-based Architecture for Remote Monitoring of Physiological and Environmental Parameters. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 48-53).
Open this publication in new window or tab >>A Heterogeneous IoT-based Architecture for Remote Monitoring of Physiological and Environmental Parameters
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 48-53Conference paper, Published 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. 

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37078 (URN)10.1007/978-3-319-76213-5_7 (DOI)2-s2.0-85042550307 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
ESS-H - Embedded Sensor Systems for Health Research ProfileREADY - Research Environment for Advancing Low Latency Internetecare@homeFuture factories in the Cloud
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-03-08Bibliographically approved
Gholamhosseini, H., Baig, M., Rastegar, S. & Lindén, M. (2018). Cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio. In: Studies in Health Technology and Informatics, vol 249: . Paper presented at 15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018 (pp. 77-83). IOS Press
Open this publication in new window or tab >>Cuffless blood pressure estimation using pulse transit time and photoplethysmogram intensity ratio
2018 (English)In: Studies in Health Technology and Informatics, vol 249, IOS Press , 2018, p. 77-83Conference paper, Published paper (Refereed)
Abstract [en]

High blood pressure (BP) is one of the common risk factors for heart disease, stroke, congestive heart failure, and kidney disease. An accurate, continuous and cuffless BP monitoring technique could help clinicians improve the rate of prevention, detection, and treatment of hypertension and related diseases. Pulse transit time (PTT) has attracted interest as an index of BP changes for cuffless BP measurement techniques. Currently, PPT-based BP measurement approaches have improved and are able to relieve the discomfort associated with an inflated cuff such as that used in auscultatory and oscillometric BP measurement techniques. However, PTT can only track the BP variation in high frequency (HF) which limits the true representation of BP changes. This paper presents a continuous and cuffless BP monitoring method based on multiparameter fusion. We used photoplethysmogram (PPG) and a two-lead electrocardiogram (ECG) and employed an algorithm based on PTT and the PPG intensity ratio (PIR) to continuously track BP in both high and low frequencies and estimate systolic and diastolic BP. 

Place, publisher, year, edition, pages
IOS Press, 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40198 (URN)10.3233/978-1-61499-868-6-77 (DOI)2-s2.0-85049035743 (Scopus ID)9781614998679 (ISBN)
Conference
15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2018-07-05Bibliographically approved
Abbaspour, S. & Lindén, M. (2018). Electromyography signal analysis: Electrocardiogram artifact removal and classifying hand movements. In: World Congress on Medical Physics and Biomedical Engineering IUPESM: . Paper presented at World Congress on Medical Physics and Biomedical Engineering IUPESM, 03 Jun 2018, Prague, Czech Republic.
Open this publication in new window or tab >>Electromyography signal analysis: Electrocardiogram artifact removal and classifying hand movements
2018 (English)In: World Congress on Medical Physics and Biomedical Engineering IUPESM, 2018Conference paper, Published paper (Refereed)
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-38634 (URN)
Conference
World Congress on Medical Physics and Biomedical Engineering IUPESM, 03 Jun 2018, Prague, Czech Republic
Projects
ESS-H - Embedded Sensor Systems for Health Research Profile
Available from: 2018-03-02 Created: 2018-03-02 Last updated: 2018-03-02Bibliographically approved
Lindén, M. & Björkman, M. (2018). Embedded sensor systems for health-A step towards personalized health. In: Stud. Health Technol. Informatics: . Paper presented at 12 June 2018 through 14 June 2018 (pp. 69-74). IOS Press
Open this publication in new window or tab >>Embedded sensor systems for health-A step towards personalized health
2018 (English)In: Stud. Health Technol. Informatics, IOS Press , 2018, p. 69-74Conference paper, Published paper (Refereed)
Abstract [en]

The demography is changing towards older people, and the challenge to provide an appropriate care is well known. Sensor systems, combined with IT solutions are recognized as one of the major tools to handle this situation. Embedded Sensor Systems for Health (ESS-H) is a research profile at Mälardalen University in Sweden, focusing on embedded sensor systems for health technology applications. The research addresses several important issues: To provide sensor systems for health monitoring at home, to provide sensor systems for health monitoring at work, to provide safe and secure infrastructure and software testing methods for physiological data management. The user perspective is important in order to solve real problems and to develop systems that are easy and intuitive to use. One of the overall aims is to enable health trend monitoring in home environments, thus being able to detect early deterioration of a patient. Sensor systems, signal processing algorithms, and decision support algorithms have been developed. Work on development of safe and secure infrastructure and software testing methods are important for an embedded sensor system aimed for health monitoring, both in home and in work applications. Patient data must be sent and received in a safe and secure manner, also fulfilling the integrity criteria.

Place, publisher, year, edition, pages
IOS Press, 2018
Keywords
Chronic disease, Embedded sensor system, Multi diseased, Multi-sensor
National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-40239 (URN)10.3233/978-1-61499-868-6-69 (DOI)2-s2.0-85049011954 (Scopus ID)9781614998679 (ISBN)
Conference
12 June 2018 through 14 June 2018
Available from: 2018-07-12 Created: 2018-07-12 Last updated: 2018-07-12Bibliographically approved
Tomasic, I., Khosraviani, K., Rosengren, P., Jornten-Karlsson, M. & Lindén, M. (2018). Enabling IoT based monitoring of patients' environmental parameters: Experiences from using OpenMote with OpenWSN and Contiki-NG. In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings: . Paper presented at 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018, 21 May 2018 through 25 May 2018 (pp. 330-334).
Open this publication in new window or tab >>Enabling IoT based monitoring of patients' environmental parameters: Experiences from using OpenMote with OpenWSN and Contiki-NG
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2018 (English)In: 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018 - Proceedings, 2018, p. 330-334Conference paper, Published paper (Refereed)
Abstract [en]

Remote health monitoring can be leveraged by the IoT paradigm. We tested OpenMotes, state-of-the-art IoT devices featuring IEEE 802.15.4 protocol, with Contiki-NG and OpenWSN, two of the most popular IoT operating systems, for the purpose of obtaining data from the OpenMote's sensors. The procedure is not strait forward and requires additional programing of the operating systems. All the steps necessary to make the OpenMote work with Contiki-NG are presented in detail. We also describe how to use Copper Firefox add-on to access the sensors data over CoAP, as well as how to design a functional web interface in Node-RED for the sensors data. 

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40365 (URN)10.23919/MIPRO.2018.8400063 (DOI)2-s2.0-85050245897 (Scopus ID)9789532330977 (ISBN)
Conference
41st International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2018, 21 May 2018 through 25 May 2018
Available from: 2018-08-17 Created: 2018-08-17 Last updated: 2018-08-17Bibliographically approved
Moqeem, A., Baig, M., Gholamhosseini, H., Mirza, F. & Lindén, M. (2018). Medical device integrated vital signs monitoring application with real-time clinical decision support. In: Studies in Health Technology and Informatics, vol. 249: . Paper presented at 15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018 (pp. 189-193). IOS Press
Open this publication in new window or tab >>Medical device integrated vital signs monitoring application with real-time clinical decision support
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2018 (English)In: Studies in Health Technology and Informatics, vol. 249, IOS Press , 2018, p. 189-193Conference paper, Published paper (Refereed)
Abstract [en]

This research involves the design and development of a novel Android smartphone application for real-time vital signs monitoring and decision support. The proposed application integrates market available, wireless and Bluetooth connected medical devices for collecting vital signs. The medical device data collected by the app includes heart rate, oxygen saturation and electrocardiograph (ECG). The collated data is streamed/displayed on the smartphone in real-time. This application was designed by adopting six screens approach (6S) mobile development framework and focused on user-centered approach and considered clinicians-as-a-user. The clinical engagement, consultations, feedback and usability of the application in the everyday practices were considered critical from the initial phase of the design and development. Furthermore, the proposed application is capable to deliver rich clinical decision support in real-time using the integrated medical device data.

Place, publisher, year, edition, pages
IOS Press, 2018
National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-40199 (URN)10.3233/978-1-61499-868-6-189 (DOI)2-s2.0-85049039213 (Scopus ID)9781614998679 (ISBN)
Conference
15th International Conference on Wearable Micro and Nano Technologies for Personalized Health, pHealth 2018; Gjovik; Norway; 12 June 2018 through 14 June 2018
Available from: 2018-07-05 Created: 2018-07-05 Last updated: 2018-07-05Bibliographically approved
Ahmed, M. U., Fotouhi, H., Köckemann, U., Lindén, M., Tomasic, I., Tsiftes, N. & Voigt, T. (2018). Run-Time Assurance for the E-care@home System. In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225: . Paper presented at 4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France (pp. 107-110).
Open this publication in new window or tab >>Run-Time Assurance for the E-care@home System
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2018 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Volume 225, 2018, p. 107-110Conference paper, Published 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.

National Category
Medical Engineering
Identifiers
urn:nbn:se:mdh:diva-37086 (URN)10.1007/978-3-319-76213-5_15 (DOI)2-s2.0-85042521264 (Scopus ID)9783319762128 (ISBN)
Conference
4th EAI International Conference on IoT Technologies for HealthCare HealthyIOT'17, 24 Oct 2017, Angers, France
Projects
ESS-H - Embedded Sensor Systems for Health Research Profileecare@home
Available from: 2017-10-30 Created: 2017-10-30 Last updated: 2018-03-08Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-1940-1747

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