Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

Abstract

Abstract-Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWTbased counterpart for "good" reconstruction quality.

Opportunities and challenges for ultra low power signal processing in wearable healthcare

“…Many applications in healthcare are highly variable between different people, and within the same person over time. For example, recent compressive sensing results have highlighted that the level of algorithm success is dominated by the variance in performance over time, not the average performance level [4,29]. Similarly, Fig.…”

Design exploration of energy-performance trade-offs for wireless sensor networks

“…We envision a scenario that can take place in a hospital, where N patients (in this example, N = 6) are wearing a node that is connected to a central base station. The nodes reduce the size of the output stream by applying one of the two available data compression techniques, i.e., digital wavelet transform (DWT) [23] and compressed sensing (CS) [13]. The two techniques have different properties in terms of complexity, signal quality and hardware requirements: for the sake of illustration, we assume that half of the nodes employ DWT, and the remaining ones execute CS.…”

“…In particular, the sampling frequency is determined by the nature of the ECG signal and is fixed to fs=250Hz, and the resolution LADC of the A/D converter is set to 12 bits, thus generating a constant input stream φin = 375 B/s. The contribution of the 10kB memory block is also constant, as the memory accesses are determined by the Shimmer -specific implementations of the DWT and CS algorithms [13]. At the radio level, the power of the carrier signal has been set to a sufficient level in order to minimize the probability of a packet error, thus avoiding an increment of φout due to retransmission.…”

“…By analyzing the execution, we can define the resource usage function as k(φin, χ node ) = (kDW T , kCS) = (2265.6/fµC , 388.8/fµC ). To estimate the quality of the application, we select the percentage root-mean-square difference (P RD) [13], which quantifies the difference between the original ECG and the one reconstructed by the coordinator. Although the actual P RD value can only be determined by measuring or simulating the actual reconstructed signal, we computed an analytical estimation using two fifth-order polynomial functions P (DW T ) 5…”

High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis

“…Among them, the wavelet transforms [15,25,13] have been extensively used because of their properties of good location in time and frequency domains. Nowadays a new paradigm, namely Compressed Sensing (CS), has 15 gained increasing attention, having proven its effectiveness [16,14]. Researches in this field have been focused on two key aspects, namely the sparse coding techniques and the dictionary construction.…”