eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers

Research Article

CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement

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  • @INPROCEEDINGS{10.1007/978-3-319-49655-9_47,
        author={Arijit Ukil and Soma Bandyopadhyay and Chetanya Puri and Rituraj Singh and Arpan Pal and K. Mandana},
        title={CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement},
        proceedings={eHealth 360°. International Summit on eHealth, Budapest, Hungary, June 14-16, 2016, Revised Selected Papers},
        proceedings_a={EHEALTH360},
        year={2017},
        month={1},
        keywords={},
        doi={10.1007/978-3-319-49655-9_47}
    }
    
  • Arijit Ukil
    Soma Bandyopadhyay
    Chetanya Puri
    Rituraj Singh
    Arpan Pal
    K. Mandana
    Year: 2017
    CardioFit: Affordable Cardiac Healthcare Analytics for Clinical Utility Enhancement
    EHEALTH360
    Springer
    DOI: 10.1007/978-3-319-49655-9_47
Arijit Ukil1,*, Soma Bandyopadhyay1,*, Chetanya Puri1,*, Rituraj Singh1,*, Arpan Pal1,*, K. Mandana2,*
  • 1: Tata Consultancy Services
  • 2: Fortis Healthcare Limited
*Contact email: arijit.ukil@tcs.com, soma.bandyopadhyay@tcs.com, chetanya.puri@tcs.com, singh.rituraj@tcs.com, arpan.pal@tcs.com, kmmandana@gmail.com

Abstract

In this paper, we present CardioFit, a completely noninvasive cardiac condition monitoring system that enhances the clinical utility of health care analytics like lowering false detection of cardiac arrhythmia condition, higher accuracy in heart rate variability (HRV) computation. It performs powerful local analysis to enable accurate as well as easy-to-use, round-the-clock in-house, remote or mobile cardiac health checking. Here, photoplethysmogram (PPG) is the sole physiological signal considered for cardiac health management. It is to be noted that PPG carries significant necessary features what is available from electrocardiogram (ECG) signal. Unlike ECG, extraction of PPG is noninvasive, easy and affordable using smartphone or other low cost sensors. However, PPG is frequently contaminated with various kinds of motion artifacts and noise. Our robust concoction of signal processing and machine learning techniques exhibit higher accuracy in the detection and removal of the corrupt PPG signal segments. The proposed mechanism substantially improves the detection capability of the cardiac condition. Efficacy of our scheme is depicted using publicly available MIT-Physionet database as well as through our own field-collected real-life PPG data.