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SHR Neuro Cancer Cardio Lipid Metab Microb

Manninger, M; Lercher, I; Hermans, ANL; Isaksen, JL; Prassl, AJ; Zirlik, A; Vernooy, K; Chaldoupi, SM; Luermans, J; Ter, Bekke, RMA; Kanters, JK; Plank, G; Scherr, D; Pock, T; Linz, D.
Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.
Comput Methods Programs Biomed. 2025; 267:108798 Doi: 10.1016/j.cmpb.2025.108798
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Leading authors Med Uni Graz

Manninger-Wünscher Martin

Co-authors Med Uni Graz

Plank Gernot

Prassl Anton

Scherr Daniel

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Abstract:

BACKGROUND: It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias. We assessed, whether a neural network-based classifier can distinguish the origin of PPG pulse waveforms. METHODS: In thirty patients undergoing invasive electrophysiological (EP) studies for narrow complex tachycardia, PPG waveforms were recorded using a PPG wristband (Empatica E4) in parallel to 12-lead surface electrocardiograms (ECGs) and intracardiac bipolar electrograms. PPG waveforms were annotated to either atrial (AP, supraventricular) or ventricular pacing (VP) based on bipolar electrograms, ECGs and stimulation protocols. 25 221 samples were split into training, testing, and validation data sets and used to develop, optimize and validate a residual network based on convolutional layers for classifying PPG waveforms according to their origin into AP or VP. RESULTS: Datasets were complete for 27 patients. 74 % were female, median age was 53 (range 18, 78) years and median BMI was 27±5 kg/m². The electrophysiological study revealed typical atrioventricular nodal re-entrant tachycardias in 63 %, atrial tachycardias in 15 % and no inducible tachyarrhythmias in 12 % of patients. On an independent patient level, correct prediction was possible in ∼73 % for AP and ∼59 % for VP. With adaptive performance built on previous patient-specific annotations, the classifier correctly predicted the origins of PPG-derived pulse waves in ∼97 % for AP and ∼95 % for VP. CONCLUSIONS: A neural network trained on ground truth PPG data collected during EP studies could distinguish between supraventricular or ventricular origin from PPG waveforms alone.

Find related publications in this database (using NLM MeSH Indexing)

Humans - administration & dosage

Photoplethysmography - methods

Female - administration & dosage

Male - administration & dosage

Middle Aged - administration & dosage

Machine Learning - administration & dosage

Aged - administration & dosage

Adult - administration & dosage

Adolescent - administration & dosage

Neural Networks, Computer - administration & dosage

Young Adult - administration & dosage

Electrocardiography - administration & dosage

Signal Processing, Computer-Assisted - administration & dosage

Heart Ventricles - physiopathology

Tachycardia, Supraventricular - diagnosis, physiopathology

Find related publications in this database (Keywords)

Photoplethysmography

Arrhythmia

Neural network

Machine learning

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