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  • 1.
    Afifi, S.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Gholamhosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Sinha, R.
    Auckland University of Technology, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    A Novel Medical Device for Early Detection of Melanoma2019Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 261, s. 122-127Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.

  • 2.
    Barua, Shaibal
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Ahmed, Mobyen Uddin
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Begum, Shahina
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Classifying drivers' cognitive load using EEG signals2017Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 237, s. 99-106Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A growing traffic safety issue is the effect of cognitive loading activities on traffic safety and driving performance. To monitor drivers' mental state, understanding cognitive load is important since while driving, performing cognitively loading secondary tasks, for example talking on the phone, can affect the performance in the primary task, i.e. driving. Electroencephalography (EEG) is one of the reliable measures of cognitive load that can detect the changes in instantaneous load and effect of cognitively loading secondary task. In this driving simulator study, 1-back task is carried out while the driver performs three different simulated driving scenarios. This paper presents an EEG based approach to classify a drivers' level of cognitive load using Case-Based Reasoning (CBR). The results show that for each individual scenario as well as using data combined from the different scenarios, CBR based system achieved approximately over 70% of classification accuracy. 

  • 3.
    Gharehbaghi, Arash
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Babic, A.
    Department of Biomedical Engineering, Linköping University, Sweden.
    Structural Risk Evaluation of a Deep Neural Network and a Markov Model in Extracting Medical Information from Phonocardiography2018Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 251, s. 157-160Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents a method for exploring structural risk of any artificial intelligence-based method in bioinformatics, the A-Test method. This method provides a way to not only quantitate the structural risk associated with a classification method, but provides a graphical representation to compare the learning capacity of different classification methods. Two different methods, Deep Time Growing Neural Network (DTGNN) and Hidden Markov Model (HMM), are selected as two classification methods for comparison. Time series of heart sound signals are employed as the case study where the classifiers are trained to learn the disease-related changes. Results showed that the DTGNN offers a superior performance both in terms of the capacity and the structural risk. The A-Test method can be especially employed in comparing the learning methods with small data size.

  • 4.
    Gholamhosseini, H.
    et al.
    Auckland University of Technology, New Zealand.
    Baig, M.
    Auckland University of Technology, New Zealand.
    Maratas, J.
    Auckland University of Technology, New Zealand.
    Mirza, F.
    Auckland University of Technology, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile2019Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 261, s. 91-96Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.

  • 5.
    Hagblad, Jimmie
    et al.
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Folke, Mia
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Lindén, Maria
    Mälardalens högskola, Akademin för hälsa, vård och välfärd.
    Long term monitoring of blood flow at multiple depths - observations of changes.2012Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 177, s. 107-112Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Detecting reduced circulation, which is a major factor in the development of pressure ulcers, can be done using optical methods. PPG and LDF can be combined and used to evaluate blood flow at different depths. In this study the use of a probe combining PPG and LDF to monitor multiple tissue depths is evaluated. The effects on blood flow and temperature without additional provocation was examined. Measurements were performed during 60 min and the use of an active probe was compared with the use of a semi-active probe turned off a major part of the time. Changes in temperature and blood flow using these probe configurations (active and semi-active probe) are compared; four different 5 min segments during a 60 min measurement. A general increase in both temperature and blood flow is found but this increase could not be concluded to occur due to the light sources of the probe.

  • 6.
    Hollmark, Malin
    et al.
    Uppsala universitet, Industriell teknik.
    Lefevre Skjöldebrand, Anna
    Andersson, Christoffer
    Uppsala universitet, Industriell teknik.
    Lindblad, Ragnar
    Technology Ready to be Launched, but is there a Payer?: Challenges for Implementing eHealth in Sweden2015Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 211, s. 57-68Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The development of a sustainable, high-quality, affordable health care is today a high priority for many actors in the society. This is to ensure that we will continue to afford to care for the growing portion of elderly in our population. One solution is to enable the individual's power over her own health or illness, and participation in her own care. There are evidently opportunities with the rapid development of eHealth and wearable sensors. Tracking and measuring vital data can help to keep people out of the hospital. Loads of data is generated to help us understand disease, to provide us with early diagnostics and warnings. It is providing us with possibilities to collect and capture the true health status of individuals. Successful technologies demonstrate savings, acceptance among users and improved access to healthcare. But there are also challenges. Implementing new technologies in health care is difficult. Researchers from around the world are reporting on similar problems, such as reimbursement, interoperability, usability and regulatory issues. This paper will discuss a few of these implementation challenges as well as a few of the efforts in meeting them. To conclude, eHealth solutions can contribute to patient empowerment and a sustainable health care. Our assumption is however, that as long as we do not face the implementation challenges and invest in overcoming the pressing obstacles, society will not be able, or willing, to pay for the solutions.

  • 7.
    Martin, Lene
    Stockholm University, Sweden.
    'Shared care' and computer assistance in glaucoma management1997Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 46, s. 288-290Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Primary open angle glaucoma afflicts 1-2% of people over 50 years of age. The diagnosis relies on a number of examinations, many of them performed by ophthalmic nurses, and the care of glaucoma patients has become one of their main tasks. A knowledge-based system for decision support in glaucoma management has been developed and validated. The aim of the current study is to evaluate the influence of computerised decision support on a 'shared care' organisation for the management of glaucoma patients.

  • 8.
    Rastegar, S.
    et al.
    Auckland University of Technology, Auckland, New Zealand.
    Gholamhosseini, H.
    Auckland University of Technology, Auckland, New Zealand.
    Lowe, A.
    Auckland University of Technology, Auckland, New Zealand.
    Mehdipour, F.
    Otago Polytechnic, Auckland, New Zealand.
    Lindén, Maria
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Estimating Systolic Blood Pressure Using Convolutional Neural Networks2019Ingår i: Studies in Health Technology and Informatics, ISSN 0926-9630, E-ISSN 1879-8365, Vol. 261, s. 143-149Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.

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