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

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 transfo...

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Bibliographic Details
Published in:PloS one 2017-10, Vol.12 (10), p.e0182500-e0182500
Main Authors: Zhang, Bo, Zhao, Jiasheng, Chen, Xiao, Wu, Jianhuang
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0182500