ECG data compression using a neural network model based on multi-objective optimization

• Published in: PloS one Vol. 12; no. 10; p. e0182500
• Main Authors: Zhang, Bo, Zhao, Jiasheng, Chen, Xiao, Wu, Jianhuang
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.10.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial neural networks; Biology and Life Sciences; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - physiopathology; Compression; Compression ratio; Compression tests; Computer and Information Sciences; Computer applications; Data analysis; Data compression; Data Compression - methods; Data processing; Diagnosis; Echocardiography; EKG; Electrocardiography; Electrocardiography - methods; Feature extraction; Fourier transforms; Humans; Information management; Information processing; Longitudinal waves; Mathematical functions; Methods; Multiple objective analysis; Neural networks; Neural Networks (Computer); Optimization; Physical Sciences; Real time; Research and Analysis Methods; Signal processing; Signal Processing, Computer-Assisted; Time compression; Wavelet transforms
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0182500

Electrocardiogram (ECG) data analysis is of great significance to the diagnosis of cardiovascular disease. ECG compression should be processed in real time, and the data should be based on lossless compression and have high predictability. In terms of the real time aspect, short-time Fourier transformation is applied to the processing of signal wave for reducing computational time. For the lossless compression requirement, wavelet-transformation that is a coding algorithm can be used to avoid loss of data. In practice, compression is required to avoid storing redundant recording data that are not useful in the diagnosis platform. The obtained data can be preprocessed to remove noise by using wavelet transform, and then a multi-objective optimize neural network model is used to extract feature information. Compared with the existing traditional methods such as direct data processing method and transform method, our proposed compression model has self-learning ability to achieve high data compression ratio at 1:19 without losing important ECG information and compromising quality. Upon testing, we demonstrated that the proposed ECG data compression method based on multi-objective optimization neural network is effective and efficient in clinical practice.