Efficient lossless compression scheme for multi-channel ECG signal processing

Tsung Han Tsai, Fong Lin Tsai

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Article number101879
JournalBiomedical Signal Processing and Control
StatePublished - May 2020


  • Golomb-Rice codec
  • Linear prediction
  • Lossless compression
  • Multi-channel ECG signal
  • Telemedicine


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