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2019 | OriginalPaper | Chapter

Data Fusion for Improving Sleep Apnoea Detection from Single-Lead ECG Derived Respiration

Authors : Ana Jiménez Martín, Alejandro Cuevas Notario, J. Jesús García Domínguez, Sara García Villa, Miguel A. Herrero Ramiro

Published in: Bioinformatics and Biomedical Engineering

Publisher: Springer International Publishing

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Abstract

This work presents two algorithms for detecting apnoeas from the single-lead electrocardiogram derived respiratory signal (EDR). One of the algorithms is based on the frequency analysis of the EDR amplitude variation applying the Lomb-Scargle periodogram. On the other hand, the sleep apnoeas detection is carried out from the temporal analysis of the EDR amplitude variation. Both algorithms provide accuracies around 90%. However, in order to improve the robustness of the detection process, it is proposed to fuse the results obtained with both techniques through the Dempster-Shafer evidence theory. The fusion of the EDR-based algorithm results indicates that, the 84% of the detected apnoeas have a confidence level over 90%.

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Metadata
Title
Data Fusion for Improving Sleep Apnoea Detection from Single-Lead ECG Derived Respiration
Authors
Ana Jiménez Martín
Alejandro Cuevas Notario
J. Jesús García Domínguez
Sara García Villa
Miguel A. Herrero Ramiro
Copyright Year
2019
Book
Bioinformatics and Biomedical Engineering

Print ISBN: 978-3-030-17934-2

Electronic ISBN: 978-3-030-17935-9

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-17935-9_5

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