Chapter 10 - Multichannel ECG reconstruction based on joint compressed sensing for healthcare applications

Abstract

Recently, continuous remote healthcare monitoring has been revolutionized by the application of affordable wearable personal health systems based on wireless body area networks (WBAN). These are battery-driven devices and more commonly used for electrocardiogram (ECG) signal storing, processing and transmission; essential for efficient and convenient clinical use. The multi-channel electrocardiogram (MECG) provides significant diagnostic information to be compared to single-lead ECG. The biggest challenge is to minimize the energy as most of the WBAN-based systems run on batteries for ambulatory monitoring. Application of compressed sensing (CS) reduces the required number of measurements of the signal and in turn save energy significantly to transmit the signal through any wireless communication system. Furthermore, it can improve patient care in general as state-of-the-art continuous healthcare services can be provided now at his/her own place at an affordable price and medical experts may share their views as and when sought by them. However, the field of CS in healthcare is still in its infancy. In this chapter, we evaluate different compression and reconstruction methods by exploiting the spatial, temporal and spatio-temporal correlation of MECG data in the wavelet domain and suggest the optimal method that is most suitable for energy efficient WBAN systems. Different methods are tested on the Physikalish-TechnischeBundesanstalt (PTB) diagnostic MECG database. Experimental results show that the proposed method is able to achieve high compression ratio (CR) at low level of distortion and less computational time for the signal recovery than the state-of-the-art; it shows the suitability of the technique for supporting distant clinical health care.