Abstract
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.
Highlights
Electrocardiogram (ECG) is widely used in modern medicine as a diagnostic parameter
The outputs of hidden neurons correspond to the implicit feature information of each ECG waveform
The sampling data of input ECG waveform and output neurons corresponds to the data of reconstructed ECG waveform, which is acquired using the weight between hidden neurons, output neurons, and the output of hidden neuron
Summary
Electrocardiogram (ECG) is widely used in modern medicine as a diagnostic parameter. Medical experts has to record huge chunks of such clinical data, and if these data cannot be compressed, it will increase storage cost due to large hard-disk space required. ECG data compression has these characteristics: 1) real time, lossless compression and high compression rate, and 2) the compression data can be used directly without full decompression. Electrocardiogram (ECG) that is recorded by automatic monitoring has significance to the diagnosis of cardiovascular disease. It usually takes a long time to record ECG data. A large amount of electrocardiogram data is required to be analyzed and stored, while some of the meaningful feature information in these data is useful to diagnose.
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