Electrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate

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dc.authorid0000-0002-0664-649X
dc.authorid0000-0001-9213-576X
dc.contributor.authorBekiryazici, Tahir
dc.contributor.authorAydemir, Gurkan
dc.contributor.authorGurkan, Hakan
dc.date.accessioned2026-02-08T15:15:19Z
dc.date.available2026-02-08T15:15:19Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractObjectives Electrocardiogram (ECG) signals are beneficial for diagnosing cardiac diseases. The cardiac patients' life quality likely increases with continuous or long-period recording and monitoring of ECG signals, leading to better and early diagnosis of disease and heart attacks. However, continuous ECG recording necessitates high data rates and storage, which means high costs. Therefore, ECG compression is a handy concept that facilitates continuous monitoring of ECG signals. Deep neural networks open up new horizons for compression and also for ECG compression by providing high compression rates and quality. Although they bring constant compression ratios with better average quality, the compression quality of individual samples is not guaranteed, which may lead to misdiagnoses. This study aims to investigate the effect of compression quality on the diagnoses and to develop a deep neural network-based compression strategy that guarantees a quality-bound in return for varying compression ratios. Materials and methods The effect of the compression quality on the arrhythmia diagnoses is tested by comparing the performance of the deep learning-based ECG classifier on the original ECG recordings and the distorted recordings using a lossy compression algorithm with different compression error levels. Then, a compression error upper limit is calculated in terms of normalized percent root mean square difference (PRDN) error, which also coincides with the findings of the previous studies in the literature. Lastly, to enable deep learning in ECG compression, a single encoder-multi-decoder convolutional autoencoder architecture, and multiple quantization levels are proposed to guarantee a desired upper limit on the error rate. Results The efficiency of the proposed method is demonstrated on a popular benchmark data set for ECG compression methods using a transfer learning approach. The PRDN error is fixed to various values, and the average compression rates are reported. An average of 13.019 : 1 compression is achieved for a 10% PRDN error rate, assessed as a fair quality threshold for reconstruction error. It has also been shown that the compression model has a runtime that can be run in real-time on wearable devices such as commercial smartwatches. Conclusion This study proposes a deep learning-based ECG compression algorithm that guarantees a desired upper limit on the compression error. This model may facilitate an eHealth solution for continuous monitoring of ECG signals of individuals, especially cardiac patients. (c) 2024 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
dc.identifier.doi10.1016/j.irbm.2024.100859
dc.identifier.issn1959-0318
dc.identifier.issn1876-0988
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85203655038
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.irbm.2024.100859
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5709
dc.identifier.volume45
dc.identifier.wosWOS:001316368000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Science Inc
dc.relation.ispartofIrbm
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectECG signal compression
dc.subjectTransfer learning
dc.subjectECG classification
dc.subjectConvolutional autoencoder (CAE)
dc.titleElectrocardiogram Signal Compression Using Deep Convolutional Autoencoder with Constant Error and Flexible Compression Rate
dc.typeArticle

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