Efficient lossless compression scheme for multi-channel ECG signal processing

Electrocardiogram (ECG) represents the recording of the heart's electrical activity and is used to diagnose heart disease nowadays. The diagnosis requires huge time consumption for acquiring enough multi-channel data. The storage and transmission of 12 lead ECG data results in massive cost. In this work, we propose a multi-channel ECG lossless compression which uses the adaptive linear prediction for intra and inter channel decorrelation. We also use the adaptive Golomb-Rice codec for entropy coding. The proposed technique for adaptive linear prediction and Golomb-Rice codec is based on the performance of passed samples. Thus, the coefficient of linear prediction and Golomb-Rice codec will make self-adjustments during the process. We evaluate the proposed algorithm with MIT-BIH Arrhythmia database for single-channel compression, and Physikalisch-Technische Bundesanstalt database (PTB) for multi-channel compression. The overall compression scheme is also implemented in embedded system with an ARM Cortex-M4 processor for real-time demonstration.