Detection and analysis of pulse waves during sleep via wrist-worn actigraphy

English version

Autoři: Johannes Zschocke ^aff001; Maria Kluge ^aff003; Luise Pelikan ^aff003; Antonia Graf ^aff003; Martin Glos ^aff003; Alexander Müller ^aff004; Rafael Mikolajczyk ^aff001; Ronny P. Bartsch ^aff005; Thomas Penzel ^aff003; Jan W. Kantelhardt ^aff002
Působiště autorů: Institute of Medical Epidemiology, Biostatistics and Informatics, Faculty of Medicine, Martin-Luther-University Halle-Wittenberg, Halle, Germany ^aff001; Institute of Physics, Martin-Luther-University Halle-Wittenberg, Halle, Germany ^aff002; Interdisziplinäres Schlafmedizinisches Zentrum, Charite - Universitätsmedizin Berlin, Berlin, Germany ^aff003; Klinik und Poliklinik für Innere Medizin I, Technische Universität München, Munich, Germany ^aff004; Department of Physics, Bar-Ilan University, Ramat Gan, Israel ^aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0226843

Souhrn

The high temporal and intensity resolution of modern accelerometers gives the opportunity of detecting even tiny body movements via motion-based sensors. In this paper, we demonstrate and evaluate an approach to identify pulse waves and heartbeats from acceleration data of the human wrist during sleep. Specifically, we have recorded simultaneously full-night polysomnography and 3d wrist actigraphy data of 363 subjects during one night in a clinical sleep laboratory. The acceleration data was segmented and cleaned, excluding body movements and separating episodes with different sleep positions. Then, we applied a bandpass filter and a Hilbert transform to uncover the pulse wave signal, which worked well for an average duration of 1.7 h per subject. We found that 81 percent of the detected pulse wave intervals could be correctly associated with the R peak intervals from independently recorded ECGs and obtained a median Pearson cross-correlation of 0.94. While the low-frequency components of both signals were practically identical, the high-frequency component of the pulse wave interval time series was increased, indicating a respiratory modulation of pulse transit times, probably as an additional contribution to respiratory sinus arrhythmia. Our approach could be used to obtain long-term nocturnal heartbeat interval time series and pulse wave signals from wrist-worn accelerometers without the need of recording ECG or photoplethysmography. This is particularly useful for an ambulatory monitoring of high-risk cardiac patients as well as for assessing cardiac dynamics in large cohort studies solely with accelerometer devices that are already used for activity tracking and sleep pattern analysis.

Klíčová slova:

Accelerometers – Age groups – Electrocardiography – Heart rate – Motion – Sleep – Sleep disorders

Zdroje

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Detection and analysis of pulse waves during sleep via wrist-worn actigraphy

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