Compression of EMG Signals Using Deep Convolutional Autoencoders

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dc.contributor.authorDinashi, Kimia
dc.contributor.authorAmeri, Ali
dc.contributor.authorAkhaee, Mohammad Ali
dc.contributor.authorEnglehart, Kevin
dc.contributor.authorScheme, Erik
dc.date.accessioned2023-06-19T13:07:09Z
dc.date.available2023-06-19T13:07:09Z
dc.date.issued2022
dc.description.abstractEfficient storage and transmission of electromyogram (EMG) data are important for emerging applications such as telemedicine and big data, as a vital tool for further advancement of the field. However, due to limitations in internet speed and hardware resources, transmission and storage of EMG data are challenging. As a solution, this work proposes a new method for EMG data compression using deep convolutional autoencoders (CAE). Eight-channel EMG data from 10 subjects, and high-density EMG data from 18 subjects, were investigated for compression. The CAE architecture was designed to extract an abstract data representation that is heavily compressed, but from which the salient information for classification can be effectively reconstructed. The proposed method attained efficient compression; for CR = 1600, the average PRDN (percentage RMS difference normalized) was 31.5% and the wrist motions classification accuracy (CA) reduced roughly 5%. The CAE substantially outperformed the state-of-the-art high-efficiency video coding and a well-known wavelet-thresholding compression technique. Moreover, by reducing the bit-resolution of the CAE's compressed data from 24 bits to 6 bits, an additional 4-fold compression was achieved without significant degradation of the reconstruction performance. Furthermore, the CAE's inter-subject performance was promising; e.g., for CR = 1600, the PRDN for the inter-subject case was only 2.6% less than that of the within-subject performance. The powerful EMG compression performance with remarkable reconstruction results reflects the CAEs potential as an automatic end-to-end approach with the ability to learn the complete encoding and decoding process. Furthermore, the excellent inter-subject performance demonstrates the generalizability and usability of the proposed approach.
dc.description.copyright© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.identifier.doi10.1109/jbhi.2022.3142034
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37227
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relationNSERC
dc.relation.hasversionhttps://doi.org/10.1109/jbhi.2022.3142034
dc.relation.ispartofIEEE Journal of Biomedical and Health Informatics
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineElectrical and Computer Engineering
dc.titleCompression of EMG Signals Using Deep Convolutional Autoencoders
dc.typejournal-article
oaire.citation.endPage2897
oaire.citation.issue7
oaire.citation.startPage2888
oaire.citation.titleIEEE Journal of Biomedical and Health Informatics
oaire.citation.volume26
oaire.license.conditionother
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa

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