Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings

Research Article

A Portable Real Time ECG Device for Arrhythmia Detection Using Raspberry Pi

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  • @INPROCEEDINGS{10.1007/978-3-319-58877-3_24,
        author={C. Valliappan and Advait Balaji and Sai Thandayam and Piyush Dhingra and Veeky Baths},
        title={A Portable Real Time ECG Device for Arrhythmia Detection Using Raspberry Pi},
        proceedings={Wireless Mobile Communication and Healthcare. 6th International Conference, MobiHealth 2016, Milan, Italy, November 14-16, 2016, Proceedings},
        proceedings_a={MOBIHEALTH},
        year={2017},
        month={6},
        keywords={Arrhythmia Portable Costeffective Wireless communication Mobile health},
        doi={10.1007/978-3-319-58877-3_24}
    }
    
  • C. Valliappan
    Advait Balaji
    Sai Thandayam
    Piyush Dhingra
    Veeky Baths
    Year: 2017
    A Portable Real Time ECG Device for Arrhythmia Detection Using Raspberry Pi
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-319-58877-3_24
C. Valliappan1,*, Advait Balaji1,*, Sai Thandayam1,*, Piyush Dhingra1,*, Veeky Baths1,*
  • 1: BITS Pilani K.K.Birla Goa Campus
*Contact email: f2013478@goa.bits-pilani.ac.in, f2013656@goa.bits-pilani.ac.in, f2013489@goa.bits-pilani.ac.in, f2013710@goa.bits-pilani.ac.in, veeky@goa.bits-pilani.ac.in

Abstract

Arrhythmia related disorders are one of the leading causes of cardiac deaths in the world. Previous studies have shown that Arrhythmia can further lead to major cardiac diseases like the Sudden Cardiac Death (SCD) syndrome. The difficulty in detecting Arrhythmia in the early stages often results in poor prognosis and presents the need for a costefficient diagnostic device. To this end, we propose a realtime portable ECG device with special emphasis on Arrhythmia detection and classification. The device is centered on a Raspberry Pi 3 (RasPi) module. RasPi with its signal processing and wireless transfer capabilities acts like an adapter between the sensors and a personalized mobile device application that is used for tracking the ECG. A highly sensitive peak detection algorithm was used by RasPi to detect and extract features from the ECG signals at real time. The peak detection algorithm was tested on the standard MITBIH arrhythmia database and reported an accuracy of greater than 95%. Hence, we propose a novel low cost approach towards arrhythmia monitoring and detection with wide applications in mobile health systems.