Key result
A proposed FrWT-based technique using YWARM and PCA achieved 99.97% accuracy and 99.99% positive predictive value for R-peak detection on a real-time ECG database.
Why the study?
In recorded ECG signals, clinical information is masked by noise and distortion, requiring efficient pre-processing techniques to improve SNR for R-peak detection.
Does a FrWT-based technique improve R-peak detection efficiency in ECG signals compared to other pre-processing techniques?
Does a FrWT-based technique improve R-peak detection efficiency in ECG signals compared to other pre-processing techniques?
A novel FrWT-based pre-processing technique demonstrates high accuracy and sensitivity for R-peak detection in ECG signals, outperforming traditional filtering methods.
In recorded Electrocardiogram (ECG) signal, clinical information is masked by several noises and distortion resulting in low signal-to-noise-ratio (SNR). In this situation, an efficient pre-processing technique is required to improve SNR for efficient analysis of ECG signals. In this research article, performance of five pre-processing techniques viz. digital bandpass filter (DBPF), wavelet transform (WT), independent principal component analysis (IPCA), savitzky golay digital FIR filter (SGDFF) and fractional wavelet transform (FrWT) have been evaluated and compared for their effects on the efficiency of R-peak detection. FrWT has been utilized for pre-processing of ECG signal for the first time in this paper. A FrWT-based technique is also proposed using Yule Walker autoregressive modeling (YWARM) and Principal Component Analysis (PCA) for feature extraction and R-peak detection, respectively. YWARM is selected due to its more stable output for long time recorded ECG signal than existing techniques, whereas PCA is selected to get optimal dimensional feature vectors out of higher dimensional feature vectors. The proposed technique has been evaluated and compared with others on the basis of various performance parameters; SNR, mean squared error (MSE), sensitivity (SE), accuracy (Acc), and positive predictive value (PPV). The proposed technique yielded interesting results among all the methods; 34.37 dB of output SNR, 0.026% of MSE, 99.98% of SE, 99.97% of Acc and 99.99% of PPV on real time ECG database (RT DB) and 24.81 dB of output SNR, 0.099% of MSE, 99.96% of SE, 99.93% of Acc, and 99.97% of PPV on MIT-BIH Arrhythmia database (MIT-BIH Arr DB).
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Gupta et al. (2020) studied Electrocardiogram (ECG) signal analysis. Fractional wavelet transform (FrWT) based technique using YWARM and PCA vs. DBPF, WT, IPCA, and SGDFF was evaluated on R-peak detection performance (SNR, MSE, Sensitivity, Accuracy, PPV). A proposed FrWT-based technique using YWARM and PCA achieved 99.97% accuracy and 99.99% positive predictive value for R-peak detection on a real-time ECG database.
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