Key result
TQCPat-based model achieves ~91% accuracy classifying six arrhythmias using PPG signals.
Why the study?
Arrhythmia carries morbidity and mortality risks, and photoplethysmography (PPG) provides blood volume information that can be utilized to develop accurate, cost-effective detection models.
Does the TQCPat-based feature engineering model accurately classify arrhythmias using PPG signals in patients undergoing radiofrequency catheter ablation?
Does the TQCPat-based feature engineering model accurately classify arrhythmias using PPG signals in patients undergoing radiofrequency catheter ablation?
The TQCPat-based feature engineering model provides a computationally efficient and highly accurate (91.30%) method for multiclass arrhythmia detection using non-invasive PPG signals.
Answers are grounded in this paper and cite their sources.
Supports PPG-based multiclass arrhythmia classification; leaves open clinical utility pending prospective validation.
BACKGROUND AND PURPOSE: Arrhythmia, which presents with irregular and/or fast/slow heartbeats, is associated with morbidity and mortality risks. Photoplethysmography (PPG) provides information on volume changes of blood flow and can be used to diagnose arrhythmia. In this work, we have proposed a novel, accurate, self-organized feature engineering model for arrhythmia detection using simple, cost-effective PPG signals. METHOD: We have drawn inspiration from quantum circuits and employed a quantum-inspired feature extraction function /named the Tree Quantum Circuit Pattern (TQCPat). The proposed system consists of four main stages: (i) multilevel feature extraction using discrete wavelet transform (MDWT) and TQCPat, (ii) feature selection using Chi-squared (Chi2) and neighborhood component analysis (NCA), (iii) classification using k-nearest neighbors (kNN) and support vector machine (SVM) and (iv) information fusion. RESULTS: Our proposed TQCPat-based feature engineering model has yielded a classification accuracy of 91.30% using 46,827 PPG signals in classifying six classes with ten-fold cross-validation. CONCLUSION: Our results show that the proposed TQCPat-based model is accurate for arrhythmia classification using PPG signals and can be tested with a large database and more arrhythmia classes.
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Gelen et al. (2025) studied Arrhythmia (n=228). TQCPat-based feature engineering model was evaluated on Classification accuracy. The proposed TQCPat-based feature engineering model achieved a classification accuracy of 91.30% and an F1-score of 85.55% in classifying six arrhythmia classes using PPG signals.
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