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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Vydáno v:	Circuits, systems, and signal processing Ročník 41; číslo 11; s. 6152 - 6181
Hlavní autoři:	Dasan, Evangelin, Gnanaraj, Rajakumar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.11.2022 Springer Nature B.V
Témata:	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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	0278-081X 1531-5878
DOI:	10.1007/s00034-022-02071-x