A novel algorithm for real-time pulse waveform segmentation and artifact detection achieved 99.6% sensitivity, 90.5% specificity, and 98.3% accuracy compared with expert annotations.
Does a novel embedded algorithm for real-time pulse waveform segmentation and artifact detection accurately identify artifacts compared to expert annotations in PPG signals?
The developed real-time pulse waveform analysis algorithm demonstrates high accuracy and agreement with expert annotations for PPG signal quality determination and artifact detection.
Effect estimate: Cohen's kappa 0.927
Photoplethysmography has been used in a wide range of medical devices for measuring oxygen saturation, cardiac output, assessing autonomic function, and detecting peripheral vascular disease. Artifacts can render the photoplethysmogram (PPG) useless. Thus, algorithms capable of identifying artifacts are critically important. However, the published PPG algorithms are limited in algorithm and study design. Therefore, the authors developed a novel embedded algorithm for real-time pulse waveform (PWF) segmentation and artifact detection based on a contour analysis in the time domain. This paper provides an overview about PWF and artifact classifications, presents the developed PWF analysis, and demonstrates the implementation on a 32-bit ARM core microcontroller. The PWF analysis was validated with data records from 63 subjects acquired in a sleep laboratory, ergometry laboratory, and intensive care unit in equal parts. The output of the algorithm was compared with harmonized experts' annotations of the PPG with a total duration of 31.5 h. The algorithm achieved a beat-to-beat comparison sensitivity of 99.6%, specificity of 90.5%, precision of 98.5%, and accuracy of 98.3%. The interrater agreement expressed as Cohen's kappa coefficient was 0.927 and as F-measure was 0.990. In conclusion, the PWF analysis seems to be a suitable method for PPG signal quality determination, real-time annotation, data compression, and calculation of additional pulse wave metrics such as amplitude, duration, and rise time.
Fischer et al. (Mon,) reported a other. Novel embedded algorithm for real-time pulse waveform segmentation and artifact detection vs. Harmonized experts' annotations was evaluated on Beat-to-beat comparison for artifact detection (sensitivity, specificity, precision, accuracy) (Cohen's kappa 0.927). A novel algorithm for real-time pulse waveform segmentation and artifact detection achieved 99.6% sensitivity, 90.5% specificity, and 98.3% accuracy compared with expert annotations.