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  • 1.
    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. 

  • 2.
    Sepehri, A. A.
    et al.
    CAPIS Biomedical Research and Development Department, Mons, Belgium.
    Kocharian, A.
    Tehran University of Medical Sciences, Tehran, Iran.
    Janani, A.
    AIMS Industrial Group, Tehran, Iran.
    Gharehbaghi, Arash
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases2016In: Journal of medical systems, ISSN 0148-5598, E-ISSN 1573-689X, Vol. 40, no 1, p. 1-10, article id 16Article in journal (Refereed)
    Abstract [en]

    This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % – 92.47 %) / (76.01 % – 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation. 

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