Key result
A novel method using chaos theory and Short time Fourier transform (STFT) was applied to physioNet databases for detecting R-peaks and classifying cardiac arrhythmias.
Why the study?
Because ECG signals are nonlinear and complex to analyze, more reliable and accurate techniques are needed to diagnose cardiac arrhythmias.
A novel computational approach using chaos theory and STFT may offer a reliable method for R-peak detection and arrhythmia classification in ECG signals.
Should not yet change practice; leaves open prospective validation of chaos-based arrhythmia detection.
Electrocardiogram (ECG) is an essential approach for observing the right condition of the heart. Generally, it represents P, Q, R, S, T and U waves. On the basis of these waves doctors can accurately diagnose cardiac arrhythmias. ECG signal is nonlinear in nature and due to this, its analysis becomes very much complex and needs extra attention. So a more reliable and accurate technique is needed for saving the life of the heart patients. Therefore, chaos theory has been applied for more accurate and reliable analysis of different ECG databases. R-peak is very crucial for classifying cardiac arrhythmia. Short time Fourier transform (STFT) and their frequency contents have been used for detecting the R-peaks. In this paper physioNet databases have been tested.
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Gupta et al. (2020) studied Cardiac arrhythmias. Chaos theory and Short time Fourier transform (STFT) was evaluated on R-peak detection and cardiac arrhythmia classification. A novel method using chaos theory and Short time Fourier transform (STFT) was applied to physioNet databases for detecting R-peaks and classifying cardiac arrhythmias.
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