Lightweight Lossy/Lossless ECG Compression for Medical IoT Systems

Monitoring patients with heart disease can be done by analyzing the Electrocardiogram (ECG). However, the large amount of data poses a burden for a system that is implemented as an IoT system with limited memory and computation capabilities. Traditionally, lossless compression methods have been favored to reduce the memory requirements due to the critical nature of the application. However, if the reconstruction of a lossy signal does not significantly affect diagnosis capability, then those methods may become attractive due to their larger compression ratios. In this paper, we propose a hybrid lossy/lossless compression system with good signal fidelity and compression ratio characteristics. The performance is evaluated after decompression using Deep Neural Networks (DNNs) that have been shown to have good classification capabilities. For the Clinical Outcomes in Digital Electrocardiology (CODE) dataset, the proposed hybrid compressor can achieve an average compression ratio of 5.18 with a mean squared error of 0.20, and DNN-based diagnosis of the decompressed waveforms has, on average, only 0.8 additional erroneous diagnoses out of a total of 402 cases compared to using the original ECG data. For the PTB-XL dataset, the hybrid compressor can achieve a high average compression ratio of 4.91 with a mean squared error of 0.01. In addition, the decompressed ECGs have only a 2.46% lower macro averaged Area Under the receiver operating characteristic Curve (AUC) score than when using the original ECGs.