A deep hierarchical convolutional neural network using raw photoplethysmography signals achieved an overall accuracy of 93.48% for classifying six cardiovascular disease classes.
Does a deep hierarchical CNN using raw PPG signals accurately classify multiple cardiovascular diseases?
A deep learning model using raw PPG signals demonstrated high accuracy (93.48%) in classifying six distinct cardiovascular conditions, highlighting its potential for integration into wearable health technologies.
Effect estimate: F1-score 0.9386, Kappa 0.8968
Background cardiovascular disease is the leading global cause of mortality. Photoplethysmography (PPG), widely embedded in consumer wearables, offers a scalable diagnostic modality. However, prior approaches are often constrained by handcrafted features, binary classification, and poor generalizability, limiting their clinical impact. Methods A deep hierarchical convolutional neural network (CNN) was designed to extract both morphological and rhythmic characteristics directly from raw photoplethysmography (PPG) signals. The architecture employs progressively structured convolutional filter hierarchies to capture multi-scale signal features. To enhance signal stability and training efficiency, a dual-stage normalization strategy was implemented, consisting of Z-score standardization followed by Min–Max scaling. In addition, batch normalization and dropout regularization were incorporated to improve model generalization and reduce the risk of overfitting. The proposed framework was trained and evaluated on a multi-source dataset comprising 612 patients and 2,448 annotated PPG segments distributed across six diagnostic classes: atrial fibrillation (AF), heart failure (HF), acute coronary syndrome (ACS), cerebral vascular accident (CVA), deep vein thrombosis (DVT), and normal sinus rhythm (NSR). Results The model achieved an overall accuracy of 93.48%, a macro-average F1-score of 0.9386, and a Cohen’s Kappa of 0.8968, indicating “almost perfect” agreement. AF and HF were detected with flawless precision and recall (1.000), while ACS achieved high sensitivity (recall 0.964). Errors were primarily confined to physiologically related conditions (e.g., ACS vs. CVA). Inference efficiency was demonstrated with <5 ms per segment on consumer-grade hardware, confirming feasibility for real-time applications. Conclusion The proposed framework advances beyond lightweight but underpowered or overly complex models by combining representational depth with computational efficiency. Limitations include the need for external, multi-center validation and explainability integration. This study establishes a robust foundation for PPG-based, multi-class cardiovascular diagnostics, supporting clinical decision support and next-generation wearable health technologies.
Amjed Al Fahoum (2026) studied Cardiovascular disease (n=612). Deep hierarchical convolutional neural network (CNN) using raw PPG signals was evaluated on Overall accuracy for six-class cardiovascular disease classification (F1-score 0.9386, Kappa 0.8968). A deep hierarchical convolutional neural network using raw photoplethysmography signals achieved an overall accuracy of 93.48% for classifying six cardiovascular disease classes.