Extended algorithm for real-time pulse waveform segmentation and artifact detection in photoplethysmograms

Abstract

Background

Photoplethysmography can be used for measuring oxygen saturation or assessing autonomic function. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are important. However, the published algorithms are limited in their abilities and study design. Therefore, the authors developed a novel embedded algorithm for pulse waveform (PWF) segmentation and artifact detection in real-time.

Objectives

The previous PWF analysis was not able to detect a diastolic peak, which prevents analyses like arterial stiffness. Furthermore, the algorithm shows room for improvements if the first part of the pulse wave is disturbed. To overcome these limitations, the authors extended the PWF analysis.

Materials and methods

The extended PWF analysis was validated as before with the data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit. The output of the algorithm was compared with harmonized experts' annotations with a total duration of 31.5 h.

Results

The comparison of the artifacts detection performance between the extended and the original PWF analysis shows a reduced sensitivity from 99.6 to 99.5%, but increased specificity from 90.5 to 91.6%, precision from 98.5 to 98.6%, accuracy from 98.3 to 98.4%, Cohen's kappa coefficient from 0.927 to 0.932, and F‑measure from 0.990 to 0.991. Furthermore, the PWF analysis is now able to detect diastolic peaks.

Conclusion

The proposed novel extended PWF analysis seems to be a suitable method for real-time annotation of the PPG, and detection of pulse wave amplitude, pulse wave duration, rise time, pulse propagation time as well as their variations.

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