12 lead electrocardiography signals compression by a new genetic programming based mathematical modeling algorithm

• Published in: Biomedical signal processing and control 2019-09, Vol.54, p.101596, Article 101596
• Main Authors: feli, Mohammad, Abdali-Mohammadi, Fardin
• Format: Article
• Language: English
• Subjects: Compression; Electrocardiograph; Genetic programming; Mathematical modeling
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2019.101596

[Display omitted] •1-In this paper a compression problem is converted to a form of regression problem.•2-In the proposed method, ECG signal is modeled with a piecewise function as the compressed signal.•The algorithm tries to model the signal with a number of models with the least possible text length using genetic programming.•In order to evaluate the proposed method, MIT-BIH arrhythmia database has been employed. Telemedicine refers to a group of modern medical services that are provided on the platform of advanced telecommunication technologies. One of these services is the screening for heart diseases, which are the leading cause of mortality across the world. But the development of telemedicine systems for cardiac screening faces multiple challenges. One of these challenges is the large volume of ECG signals, which makes them difficult to store and transfer. Of the many algorithms proposed for the compression of ECG signals, most rely on the processing of data as discrete numerical values. The alternative approach followed in this study is to model the signal compression problem into a regression problem and then convert it into a text compression problem. Using this approach, the paper presents a new genetic programming based method for the compression of ECG signals. The proposed method starts with denoising and smoothing the ECG signal with discrete wavelet transform and then constructing its mathematical model with a genetic programming based algorithm. This model is a piecewise mathematical function where each sub-function models one part of the signal. Next, the model is converted to a character string and regular expressions are used to extract the function coefficients and encode the symbols contained in the string. Finally, the strings and coefficients are compressed using the LZW and arithmetic encoding methods, respectively. The efficiency of the algorithm is evaluated through compression ratio (CR), percent root-mean-square difference (PRD), root-mean-square-error (RMSE) and quality score (QS) on MIT-BIH Arrhythmia Database records. The evaluation results demonstrate the good performance of the proposed method in comparison with other state-of-the-art techniques.