Automatic detection of left ventricular ejection time from a finger photoplethysmographic pulse oximetry waveform: comparison with Doppler aortic measurement.
Left ventricular ejection time (LVET) is a useful measure of ventricular performance and preload. The present study explores a novel method of continuous LVET monitoring using a noninvasive finger photoplethysmographic pulse oximetry waveform (PPG-POW). A method for the automatic beat-to-beat detection of LVET from the finger PPG-POW is presented based on a combination of derivative analysis, waveform averaging and rule-based logic. The performance of the detection method was evaluated on 13 healthy subjects during graded head-up tilt. Overall, the correlation between the PPG-POW derived LVET and the aortic flow derived LVET was high and significant (r = 0.897, p < 0.05). The bias was -14 +/- 14 ms (mean +/- SD), and the percentage error was 9.7%. Although these results would not be sufficient to satisfy the requirement for clinical evaluation of LVET when absolute accuracy was demanded, the strong correlation between the PPG-POW LVET and the aortic LVET on an intra-subject basis (r = 0.945 +/- 0.043, mean +/- SD) would support the application of PPG-POW to detect the directional change in LVET of an individual. This could be very useful for the early identification of progressive hypovolaemia or blood loss. The present study has demonstrated a promising approach to extract potentially useful information from a noninvasive, easy-to-obtain signal that could be readily acquired either from existing patient monitoring equipment or from inexpensive instrumentation. More extensive investigation is necessary to evaluate the applicability of the present approach in clinical care monitoring.
Chan
Gregory S H
GS
Biomedical Systems Laboratory, School of Electrical Engineering and Telecommunications, University of New South Wales, Sydney, NSW, 2052, Australia.
Middleton
Paul M
PM
Celler
Branko G
BG
Wang
Lu
L
Lovell
Nigel H
NH
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001011:Aorta / Q000000981:diagnostic imaging / Q000502:physiology; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D015150:Echocardiography, Doppler; D005260:Female; D005385:Fingers / Q000098:blood supply / Q000502:physiology; D006801:Humans; D008297:Male; D010092:Oximetry / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017156:Photoplethysmography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013318:Stroke Volume / Q000502:physiology*; D016277:Ventricular Function, Left / Q000502:physiology*