Abstract Aims To test, whether a neural network-based classifier could aid in distinguishing photoplethysmographic (PPG) pulse waveforms of ventricular from those of supraventricular origin. Methods Thirty patients undergoing invasive electrophysiological studies for documented narrow complex tachycardias between May and December 2021 at a university medical centre were included in the trial. PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrograms (ECGs) and intracardiac electrograms from diagnostic catheters. Pulse waves were then annotated to either supraventricular (sinus rhythm, atrial pacing) or ventricular origin (ventricular pacing) based on invasive electrogram recordings, ECGs and stimulation protocols. 21147 data sets were split into test, training and validation data sets and used to develop, train and validate a convolutional neural network (CNN) for classifying PPG waveforms according to their origin into sinus rhythm, atrial or ventricular pacing. Results Datasets were complete for 27 patients. 74% were female and median age was 53 (range 18, 78) years, median BMI was 27±5, 19% had arterial hypertension, 11% had diabetes and 15% had coronary artery disease. During the electrophysiological study, typical AV-nodal re-entrant tachycardias could be induced in 63% of patients, 15% had inducible atrial tachycardias and the other patients had no inducible tachyarrhythmias. Our CNN-based classifier was able to correctly predict the origins of pulse waves in 95±1% for sinus rhythm, 96±1% for atrial paced rhythms and 95±1% for ventricular rhythms. Conclusion A neural network-based classifier trained on ground truth PPG data obtained by using invasive EP measurements for annotation could accurately predict atrial or ventricular origin from PPG waveforms alone.Graphical abstract
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