What does this research mean for the field?

A methodology combining Convolutional Neural Networks on Time-Frequency spectra with Poincare plot-based signal processing accurately classifies smartwatch PPG signals into Atrial Fibrillation, Normal Sinus Rhythm, and Premature Atrial/Ventricular Contractions, improving overall AF detection accuracy by isolating premature contractions. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

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July 3, 2023

Signal Processing and Deep Learning Based Smartwatch Photoplethysmography Data Classification of Atrial Fibrillation, Premature Atrial and Ventricular Contraction

Q: What is the clinical evidence from this study?

Study design: Other. Population: Atrial Fibrillation, Premature Atrial and Ventricular Contraction. Intervention: Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS) and Poincare plot. Primary outcome: Classification accuracy, sensitivity, and specificity.

Resultado clave

A proposed methodology utilizing Convolutional Neural Networks on Time-Frequency spectra and Poincare plots improved the accuracy, sensitivity, and specificity of detecting AF, NSR, and PAC/PVC.

PICO estructurado

Does a CNN-based algorithm using Time-Frequency spectra and Poincare plots improve the accuracy of detecting AF, NSR, and PAC/PVC from smartwatch PPG data?

Población

PPG data from UMass Simband dataset (Smartwatch PPG Data with AF, NSR, PAC, PVC) and MIMIC III dataset (Finger Tips Pulse Oximetry PPG data with AF, NSR, PAC/PVC)

Intervención

Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS) along with signal processing and Poincare plot based PAC/PVC detection

Resultado

Classification accuracy, sensitivity, and specificity for AF, NSR, and PAC/PVCsurrogate

A novel deep learning and signal processing approach using smartwatch PPG data can accurately distinguish atrial fibrillation from normal sinus rhythm and premature contractions.

Resumen

Atrial Fibrillation (AF) with high mortality rate needs to be monitored and detected accurately. AF is indicated as varying pulse-to-pulse intervals in a PPG signal. To record Photoplethysmography (PPG) signal, wrist-watches are used. AF detection is made using features, discriminating AF from Normal Sinus Rhythm (NSR). The presence of Premature Atrial and Ventricular Contraction (PAC/PVC) in subjects due to its randomness may lead to false AF detection. The proposed methodology utilizes Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS) along with signal processing for classification of PPG signal into AF, NSR and PAC/PVC, The Poincare plot based PAC/PVC detection implemented in this paper not only separates PAC/PVC from NSR and AF but also improves the accuracy of AF and NSR detection. The results for training and testing are validated on UMass Simband dataset (Smartwatch PPG Data with AF, NSR, PAC, PVC) and MIMIC III dataset (Finger Tips Pulse Oximetry PPG data with AF, NSR, PAC/PVC). , The experimental results have shown that the proposed system gives higher accuracy, sensitivity and specificity values for both datasets.

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Cite This Study

Javed et al. (Mon,) conducted a other in Atrial Fibrillation, Premature Atrial and Ventricular Contraction. Convolutional Neural Network (CNN) on Time-Frequency spectra (TFS) and Poincare plot was evaluated on Classification accuracy, sensitivity, and specificity. A proposed methodology utilizing Convolutional Neural Networks on Time-Frequency spectra and Poincare plots improved the accuracy, sensitivity, and specificity of detecting AF, NSR, and PAC/PVC.

synapsesocial.com/papers/6a0848251e8b9db648ddfd67 https://doi.org/https://doi.org/10.1109/codit58514.2023.10284500

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