An Efficient ECG Lossless Compression System for Embedded Platforms With Telemedicine Applications

This paper presents a method for wireless ECG compression and zero lossless decompression using a combination of three different techniques in order to increase storage space while reducing transmission time. The first technique used in the proposed algorithm is an adaptive linear prediction; it achieves high sensitivity and positive prediction. The second technique is content-adaptive Golomb-Rice coding, used with a window size to encode the residual of prediction error. The third technique is the use of a suitable packing format; this enables the real-time decoding process. The proposed algorithm is evaluated and verified using over 48 recordings from the MIT-BIH arrhythmia database, and it shown to be able to achieve a lossless bit compression rate of 2.83{\times} in Lead V1 and 2.77{\times} in Lead V2. The proposed algorithm shows better performance results in comparison to previous lossless ECG compression studies in real time; it can be used in data transmission methods for superior biomedical signals for bounded bandwidth across e-health devices. The overall compression system is also built with an ARM M4 processor, which ensures high accuracy performance and consistent results in the timing operation of the system.