Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach

Through wearable technology, several chronic diseases are diagnosed by long-term monitoring of vital signs specifically ECG, EMG, EEG biosignals. Such prolonged monitoring and transmitting these multiple recordings may decline the battery power of wireless wearable device. This work aims at preservi...

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Veröffentlicht in:Circuits, systems, and signal processing Jg. 41; H. 11; S. 6152 - 6181
Hauptverfasser: Dasan, Evangelin, Gnanaraj, Rajakumar
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.11.2022
Springer Nature B.V
Schlagworte:
Algorithms
Bias
Chronic illnesses
Circuits and Systems
Communication
Compressing
Data transmission
Datasets
Dictionaries
Efficiency
Electrical Engineering
Electrocardiography
Electroencephalography
Electromyography
Electronics and Microelectronics
Engineering
Instrumentation
Literature reviews
Methods
Monitoring
Power
Receivers & amplifiers
Reconstruction
Sensors
Signal processing
Signal,Image and Speech Processing
Telemedicine
Wearable computers
Wearable technology
Incremental learning
Joint multi-modality compression
Multi-sensor wearable system
Multimodal Autoencoder
Wireless wearable device
Biomedical signal processing
ISSN:0278-081X, 1531-5878
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Zusammenfassung:Through wearable technology, several chronic diseases are diagnosed by long-term monitoring of vital signs specifically ECG, EMG, EEG biosignals. Such prolonged monitoring and transmitting these multiple recordings may decline the battery power of wireless wearable device. This work aims at preserving the battery power of wireless wearables by jointly compressing ECG–EMG–EEG signals before sending to the receiver. This work proposes multimodal deep denoising convolutional autoencoder architecture for joint compression (encoding) and reconstruction (decoding) of ECG–EMG–EEG biosignals. In addition, the system may encounter new data stream in future with varying range of statistics in this real-time scenario; hence, it is required to remodel the system. But these wearables are memory constrained, so the model’s learned optimized parameters should not increase in size when it is remodeled or updated. The incremental learning addresses this issue by reusing the previously learned weights as initial weight for retraining the model for new dataset and avoids random weight initialization thereby maintaining the space and time complexity. The experimental result shows that the proposed model achieves better compression efficiency of 99.8% with highest reconstruction Quality Score of 156, 254 & 149.4 for ECG, EMG & EEG signals, respectively, than state-of-the-art methods, and it is observed that the computation time is low for joint compression than compressing each signal individually.
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ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-022-02071-x