Clustering Continuous Wavelet Transform Characteristics of Heart Rate Variability through Unsupervised Learning

The analysis and interpretation of physiological signals acquired non-invasively are increasingly important in Smart Health, precision medicine, and medical research. However, this analysis is hampered due to the length, complexity, and inter-subject variation of these signals, and, consequently, di...

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Published in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2019; pp. 4584 - 4587
Main Authors: Wachowiak, Mark P., Moggridge, Jason J., Wachowiak-Smolikova, Renata
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.07.2019
Subjects:
Cluster Analysis
Continuous wavelet transforms
Convolution
Electrocardiography
Heart Rate
Heart rate variability
Humans
Time-frequency analysis
Unsupervised Machine Learning
Wavelet Analysis
ISSN:1557-170X, 2694-0604, 1558-4615, 2694-0604
Online Access:Get full text
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Summary:The analysis and interpretation of physiological signals acquired non-invasively are increasingly important in Smart Health, precision medicine, and medical research. However, this analysis is hampered due to the length, complexity, and inter-subject variation of these signals, and, consequently, dimensionality reduction and clustering offer substantial benefits. Machine learning, used widely in biomedicine, is increasingly being applied to physiological time series. Among the applications of unsupervised learning, clustering is one of the most important. In this paper, an unsupervised autoen-coder architecture, deep convolutional embedded clustering, is presented as a data-driven approach to study time-frequency characteristics of heart rate variability records. An autoen-coder network is trained on continuous wavelet transforms of heart rate variability signals calculated from publicly-available annotated ECG records with a wide variety of conditions. The latent variables learned by the clustering autoencoder are low-dimensional representations of wavelet transform characteristics that can be visualized and further analyzed. The results indicate that the learned clusters correspond to beat morphologies in the electrocardiogram in many cases, but also that the reduced dimensions of the time-frequency features can potentially provide additional insights into cardiac activity and the autonomic nervous system.
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ISSN:1557-170X
2694-0604
1558-4615
2694-0604
DOI:10.1109/EMBC.2019.8857515