An efficient compression of ECG signals using deep convolutional autoencoders

[Display omitted] The block representation of the proposed CAE model for ECG compression. •An efficient ECG compression method based on deep convolutional autoencoders (CAE).•A deep network structure of 27 layers consisting of encoder and decoder parts.•Comprehensive experiments were performed on a...

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Veröffentlicht in:Cognitive systems research Jg. 52; S. 198 - 211
Hauptverfasser: Yildirim, Ozal, Tan, Ru San, Acharya, U. Rajendra
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2018
Schlagworte:
Autoencoders
Deep learning
ECG signals
Signal compression
Deep learning
Signal compression
ECG signals
Autoencoders
ISSN:1389-0417, 1389-0417
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Zusammenfassung:[Display omitted] The block representation of the proposed CAE model for ECG compression. •An efficient ECG compression method based on deep convolutional autoencoders (CAE).•A deep network structure of 27 layers consisting of encoder and decoder parts.•Comprehensive experiments were performed on a large scale ECG database.•Compression of ECG signals with minimum loss, low dimension and securely.•This method can be used in the telemetry, e-health applications and Holter systems. Advances in information technology have facilitated the retrieval and processing of biomedical data. Especially with wearable technologies and mobile platforms, we are able to follow our healthcare data, such as electrocardiograms (ECG), in real time. However, the hardware resources of these technologies are limited. For this reason, the optimal storage and safe transmission of the personal health data is critical. This study proposes a new deep convolutional autoencoder (CAE) model for compressing ECG signals. In this paper, a deep network structure of 27 layers consisting of encoder and decoder parts is designed. In the encoder section of this model, the signals are reduced to low-dimensional vectors; and in the decoder section, the signals are reconstructed. The deep learning approach provides the representations of the low and high levels of signals in the hidden layers of the model. Hence, the original signal can be reconstructed with minimal loss. Very different from traditional linear transformation methods, a deep compression approach implies that it can learn to use different ECG records automatically. The performance was evaluated on an experimental data set comprising 4800 ECG fragments from 48 unique clinical patients. The compression rate (CR) of the proposed model was 32.25, and the average PRD value was 2.73%. These favourable observation suggest that our deep model can allow secure data transfer in a low-dimensional form to remote medical centers. We present an effective compression approach that can potentially be used in wearable devices, e-health applications, telemetry and Holter systems.
ISSN:1389-0417
1389-0417
DOI:10.1016/j.cogsys.2018.07.004