A Dynamic Compression Scheme for Energy-Efficient Real-Time Wireless Electrocardiogram Biosensors

Wireless body sensor networks enabled electrocardiogram (ECG) biosensors are a novel solution for patient-centric telecardiology. With this solution, the prevention and early diagnosis of cardiovascular diseases can be effectively improved. However, the energy efficiency of the present wireless ECG biosensors still needs to be improved. In this paper, a dynamic compression scheme is proposed to deal with the challenge of ultralow power and real-time wireless ECG application. This compression scheme consists of a digital integrate-and-fire sampler and a lossless entropy encoder, which can reduce airtime over energy-hungry wireless links and improve the energy efficiency of the biosensors. The efficiency improvement is evidenced by the experiments using the MIT-BIH arrhythmia database in MICAz node. The lifetime of dc-implemented MICAz node can be extended up to 76.60% with high signal recovery quality. This scheme is also compared with the digital wavelet transform-based and compressed sensing-based compression schemes. All experimental results indicate that the proposed scheme has high-energy efficiency, low computational complexity, less resource consumption, and rapid time response.