Exploiting similar prior knowledge for compressing ECG signals
Graphical abstract
Introduction
A wireless body area network (WBAN) connects independent nodes (e.g. sensors and actuators) that are placed on the body or under the skin of a person. The network typically expands over the whole human body and the nodes are connected through a wireless communication channel [1]. WBANs serve a variety of applications like sports, defense, emotion detection, personal health monitoring, posture detection, medical consumer electronics [2]. Personal health monitoring is an ongoing trend, and the latest consumer electronic products confirm that. It is a developing method in personalized medicine and home-based e-health, enabling real-time monitoring of biological signals, among which electrocardiogram (ECG) signals for heart activity monitoring. ECG signals are used to detect heart rate variability (HRV), the position of normal and abnormal heartbeats, which can be caused by damage to heart muscle cells, the damage to the conduction system, the effects of cardiac drugs or the effects of an implanted pacemaker. The sensors are equipped with a limited amount of power supply and in most cases, replacing or recharging the battery is difficult, so energy consumption presents a challenge for the extensive use of WBANs. Sensors consume energy for sensing, processing and most for data transmission. To reduce the impact of data transmission on energy consumption, various data compression techniques can be implemented, but using such a technique comes at the expense of signal quality.
The study in this paper focuses on using similarity between ECG pulses because they tend to be similar to each other [3]. To the best of our knowledge, this is the first time high likeness between consecutive ECG frames is exploited for sparsifying frames prior to compressed sensing (CS). The proposed method can be used to detect abnormalities reliably because the abnormal frames that could be signs of heart diseases are not similar to normal frames. When abnormal frames are detected, they are sent intact.
The proposed redundancy removal-based method was applied to several ECG records at several compression ratios to check the capability of this technique for detecting HRV. The Pan-Tompkins [4] detection algorithm was also applied to the whole reconstructed signal for performance assessment. Experimental results show that the proposed technique provides little increase in the sensors’ processing time compared to plain CS, with the advantage of very good performance over all compression ratios (CR) and small errors for CRs above 3.5 compared to other techniques.
The main contribution of the paper is the data compression scheme that exploits the likeness between consecutive ECG frames for frame sparsification prior to compressed sensing and determines on the fly whether a frame is abnormal. While redundancy is removed between similar frames for sparsification, abnormal frames are sent without using CS technique, to ensure relevant signal characteristics are preserved for medical diagnosis without over-consuming energy.
In the rest of this paper, after a brief review of related work in Section 2, a description of ECG signals and compressed sensing is given in Section 3. Section 4 describes the proposed scheme. Section 5 presents an evaluation of the proposed work and compares it with several state-of-the-art techniques.
Section snippets
Related work
The energy consumption of physiological sensors, particularly ECG sensors, has always been a challenging issue, so a variety of lossy and lossless techniques that reduce energy expended in the transmission of ECG frames have been introduced. Lossless compression techniques, mostly extract static redundancies existence in the signal to reduce the total bit length. Codebook-based approaches are a popular format of these techniques, where values according to their frequency of occurrence are
Background
We begin this section by providing a brief introduction to ECG. We then cover some important aspects of the theory of compressed sensing. Throughout this paper, we use bold lower-case letters to denote a vector e.g. , and bold upper-case letters for matrices, e.g., . Scalar values are denoted by italic symbols, such as . Additionally, denotes the estimated/recovered value of vector . Table 1 defines some frequently used symbols in this paper.
Proposed scheme
The aim of this research is to improve energy-efficiency while maintaining the detection accuracy of HRV and heart problems. The workflow of the proposed scheme is summarized in Fig. 4. The workflow of the proposed technique starts with segmenting the digitized signal into 512 sample, enough to contain at least one QRS complex. After applying a simple R peak-detection on the segment, frame of aligned R-peak with previous R-peak is provided. According to the similarity between current frame and
Simulation and results
In order to validate the proposed technique, clinical ECG signals were used. These signals contained baseline wander and motion artifacts for accurate performance evaluation.
Conclusions
Compressed sensing theory is an attractive solution for WBAN, as it lowers power consumption in sensors by reducing the data size. Due to non-sparse sensed signals in WBAN, this area has not gained much benefit. In order to use compressed sensing the signal should be sparse or made sparse by transforming it to another domain using a sparsifying matrix. Most of the natural signals such as ECG are not sparse. So this research is aimed at using compressed sensing for these signals by removing the
Author contributions
Fahimeh Nasimi: Conceptualization, methodology, software, validation, investigation, writing – original draft. Mohammad Reza Khayyambashi: Resources, writing – review & editing, supervision. Naser Movahhedinia: Conceptualization, validation, formal analysis, writing – review & editing. Yee Wei Law: Conceptualization, validation, writing – review & editing.
Conflict of interest
The authors declare that there is no conflict of interest.
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