Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw 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
Web of Science
PubMed
FullText
FullText_MUG
- Führende Autor*innen der Med Uni Graz
- Manninger-Wünscher Martin
- Co-Autor*innen der Med Uni Graz
- Plank Gernot
- Prassl Anton
- Scherr Daniel
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- 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