Signal Acquisition-Independent Lossless Electrocardiogram Compression Using Adaptive Linear Prediction

Krittapat Bannajak, Nipon Theera-Umpon () and Sansanee Auephanwiriyakul
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Krittapat Bannajak: Department of Electrical Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Nipon Theera-Umpon: Department of Electrical Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Sansanee Auephanwiriyakul: Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand

IJERPH, 2023, vol. 20, issue 3, 1-15

Abstract: In this paper, we propose a lossless electrocardiogram (ECG) compression method using a prediction error-based adaptive linear prediction technique. This method combines the adaptive linear prediction, which minimizes the prediction error in the ECG signal prediction, and the modified Golomb–Rice coding, which encodes the prediction error to the binary code as the compressed data. We used the PTB Diagnostic ECG database, the European ST-T database, and the MIT-BIH Arrhythmia database for the evaluation and achieved the average compression ratios for single-lead ECG signals of 3.16, 3.75, and 3.52, respectively, despite different signal acquisition setup in each database. As the prediction order is very crucial for this particular problem, we also investigate the validity of the popular linear prediction coefficients that are generally used in ECG compression by determining the prediction coefficients from the three databases using the autocorrelation method. The findings are in agreement with the previous works in that the second-order linear prediction is suitable for the ECG compression application.

Keywords: electrocardiogram (ECG); lossless compression; linear prediction; Golomb–Rice coding (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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