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Accurate Detection of Heart Rate Using In-ear Photoplethysmography in a Clinical Setting

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Date 2022 Sep 5
PMID 36060539
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Abstract

Background: Recent research has shown that photoplethysmography (PPG) based wearable sensors offer a promising potential for chronic disease monitoring. The aim of the present study was to assess the performance of an in-ear wearable PPG sensor in acquiring valid and reliable heart rate measurements in a clinical setting, with epileptic patients.

Methods: Patients undergoing video-electroencephalography (EEG) monitoring with concomitant one-lead electrocardiographic (ECG) recordings were equipped with an in-ear sensor developed by cosinuss°.

Results: In total, 2,048 h of recording from 97 patients with simultaneous ECG and in-ear heart rate data were included in the analysis. The comparison of the quality-filtered in-ear heart rate data with the reference ECG resulted in a bias of 0.78 bpm with a standard deviation of ±2.54 bpm; Pearson's Correlation Coefficient PCC = 0.83; Intraclass Correlation Coefficient ICC = 0.81 and mean absolute percentage error MAPE = 2.57.

Conclusion: These data confirm that the in-ear wearable PPG sensor provides accurate heart rate measurements in comparison with ECG under realistic clinical conditions, especially with a signal quality indicator. Further research is required to investigate whether this technology is helpful in identifying seizure-related cardiovascular changes.

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References
1.
Etiwy M, Akhrass Z, Gillinov L, Alashi A, Wang R, Blackburn G . Accuracy of wearable heart rate monitors in cardiac rehabilitation. Cardiovasc Diagn Ther. 2019; 9(3):262-271. PMC: 6603497. DOI: 10.21037/cdt.2019.04.08. View

2.
Tamura T . Current progress of photoplethysmography and SPO for health monitoring. Biomed Eng Lett. 2019; 9(1):21-36. PMC: 6431353. DOI: 10.1007/s13534-019-00097-w. View

3.
Shrout P, Fleiss J . Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979; 86(2):420-8. DOI: 10.1037//0033-2909.86.2.420. View

4.
Hoppe C, Poepel A, Elger C . Epilepsy: accuracy of patient seizure counts. Arch Neurol. 2007; 64(11):1595-9. DOI: 10.1001/archneur.64.11.1595. View

5.
Allen J . Photoplethysmography and its application in clinical physiological measurement. Physiol Meas. 2007; 28(3):R1-39. DOI: 10.1088/0967-3334/28/3/R01. View