Exploiting similar prior knowledge for compressing ECG signals

Abstract

Background and objectivesData compression techniques have been used in order to reduce power consumption when transmitting electrocardiogram (ECG) signals in wireless body area networks (WBAN). Among these techniques, compressed sensing allows sparse or compressible signals to be encoded with only a small number of measurements. Although ECG signals are not sparse, they can be made sparse in another domain. Numerous sparsifying techniques are available, but when signal quality and energy consumption are important, existing techniques leave room for improvements. MethodsTo leverage compressed sensing, we increased the sparsity of an ECG frame by removing the redundancy in a normal frame. In this study, by framing a signal according to the detected QRS complex (R peaks), consecutive frames of the signal become highly similar. This helps remove redundancy and consequently makes each frame sparse. In order to increase detection performance, different frames that symptomize a cardiovascular disease are sent uncompressed. ResultsFor evaluating and comparing our proposed technique with different state-of-the-art techniques two datasets that contained normal and abnormal ECG: MIT-BIH Arrhythmia Database and MIT-BIH Long Term Database were used. For performance evaluation, we performed heart rate variability (HRV) analysis as well as energy-based distortion analysis. The proposed method reaches an accuracy of 99.9%, for a compression ratio of 25. For MIT-BIH Long Term Database, the average percentage root-mean squared difference (PRD) is less than 10 for all compression ratios. ConclusionRemoving the redundancy between successive similar frames and exact transmission of dissimilar frames, the proposed method proves to be appropriate for heart rate variability analysis and abnormality detection.