gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

Heartbeat Reconstruction during Sleep by Wrist Worn Acceleration Devices

Meeting Abstract

  • Johannes Zschocke - Martin-Luther-University Halle-Wittenberg, Medical Faculty, Institute of Medical Epidemiology, Biostatistics, and Informatics, Halle, Germany; Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle, Germany
  • Maria Kluge - Interdisziplinäres Schlafmedizinisches Zentrum - Charité Berlin, Berlin, Germany
  • Luise Pelikan - Interdisziplinäres Schlafmedizinisches Zentrum - Charité Berlin, Berlin, Germany
  • Antonia Graf - Interdisziplinäres Schlafmedizinisches Zentrum - Charité Berlin, Berlin, Germany
  • Martin Glos - Interdisziplinäres Schlafmedizinisches Zentrum - Charité Berlin, Berlin, Germany
  • Thomas Penzel - Interdisziplinäres Schlafmedizinisches Zentrum - Charité Berlin, Berlin, Germany
  • Alexander Müller - Medizinische Klinik und Deutsches Herzzentrum München der Technischen Universität München, München, Germany
  • Alexander Kluttig - Martin-Luther-University Halle-Wittenberg, Medical Faculty, Institute of Medical Epidemiology, Biostatistics, and Informatics, Halle, Germany
  • Rafael Mikolajczyk - Martin-Luther-University Halle-Wittenberg, Medical Faculty, Institute of Medical Epidemiology, Biostatistics, and Informatics, Halle(Saale), Germany
  • Jan W. Kantelhardt - Institut für Physik, Martin-Luther-Universität Halle-Wittenberg, Halle, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 186

doi: 10.3205/19gmds018, urn:nbn:de:0183-19gmds0184

Veröffentlicht: 6. September 2019

© 2019 Zschocke et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe http://creativecommons.org/licenses/by/4.0/.


Gliederung

Text

Introduction: The increasing resolution of modern accelerometers opens the possibility to detect even tiny body movements in long-term recordings. In this study, we demonstrate and evaluate an approach for the identification of pulse waves (and thus also heartbeats) from acceleration data of the wrist during sleep.

Background: Accelerometers have already been used to detect heartbeats when placed on the chest wall, a method called seismo-cardiography. Specifically, the tissue’s response to heartbeats, i.e., chest motions (< 5 Hz) and chest vibrations (> 5 Hz) have been evaluated [1]. Furthermore, every heartbeat initiates a pulse wave that spreads across the arteries. This pressure wave stimulates vibrations of the surrounding tissue. Velocity and shape of the pulse wave were shown to depend on age, heart rate, body height, and gender [2].

Methods: We studied the acceleration data of 392 subjects who spend one diagnostic night in a sleep laboratory and wore a SOMNOwatch™ plus device [3] (also recording a single-channel ECG) in addition to standard polysomnography [4]. The 3D acceleration data was segmented and cleaned separately for each acceleration axis by excluding episodes of body movements and splitting episodes with different sleep positions. Then we applied a 5 to 14 Hz bandpass filter, a Hilbert transform [5], and a peak detection algorithm for each axis to determine peaks of the pulse waves. In the last step, we selected, for each sleep segment, the axis with the best pulse-wave peaks, defined according to the number of detected peaks and the strength of autocorrelations across the peaks. Due to the transition time between a heartbeat and the arrival of the pulse wave at the wrist, a direct comparison of the time points of R peaks in the ECG and pulse wave peaks is not appropriate. Hence, we compared instead pulse wave intervals (PWI) and R peak intervals (RPI).

Results: We were able to reconstruct heartbeats via detected pulse waves for a median duration of 1.3h per subject. When compared with the ECG as gold standard, 81 percent of the detected pulse-wave peaks could be correctly assigned to R peaks at an accuracy of 0.1 seconds. A comparison of RPI and PWI in tachograms showed that a respiratory sinus arrhythmia is also visible in PWI data. Also RPI and PWI were highly correlated with a mean Pearson correlation coefficient of 0.94. Furthermore, we could determine the pulse-wave transit times, i.e., the times between R peaks in the ECG and pulse-wave peaks at the wrist.

Discussion and Conclusion: Although further development, optimization, and validation is necessary, our approach represents a novel, additional option for obtaining (parts of) long-term nocturnal heartbeat-interval time series without the need to use ECG electrodes. This could open a path towards assessing heart rate and heart-rate variability in large cohort studies solely with accelerometers already used for actigraphy analysis. Possibly, the approach could be useful to make pelthysmogram-based approaches for measuring heartbeats at the wrist, as currently used in “smart” watches, more reliable.

The authors declare that they have no competing interests.

The authors declare that a positive ethics committee vote has been obtained.

This contribution has already been published: publication forthcoming.


References

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