Performance Evaluation of Various Pre-Processing Techniques for R-Peak Detection in ECG Signal

ABSTRACT

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).

KEYWORDS:

• DBPF
• ECG
• FrWT
• Human heart
• IPCA
• SGDFF
• SNR
• Time-fractional-frequency plane
• WT

Additional information

Notes on contributors

Varun Gupta

Varun Gupta completed his BTech in electronics and communication engineering from BIT, Meerut in 2007 and MTech from Dr B R Ambedkar National Institute of Technology (NIT) Jalandhar in 2011. Currently, he is doing PhD from National Institute of Technology (NIT) Kurukshetra He is serving as an assistant professor in Electronics and Instrumentation Engineering Department, KIET Group of Institutions, Ghaziabad, India for the last nine years His research includes biomedical signal processing, control system, pattern recognition techniques, soft computing and image processing.

Monika Mittal

Monika Mittal is an associate professor in Electrical Engineering Department, NIT Kurukshetra. She has completed her graduation in electrical engineering from M M M Institute of Technology, Gorakhpur, India in 1992 and completed her post-graduation from NIT Kurukshetra, India in 1994 with specialization in control systems. She has completed her doctorate from Electrical Engineering Department, NIT Kurukshetra in 2013. She has a teaching experience of over 25 years. She has authored about 50 research papers in international and national journals and conferences. Currently, she is working in the areas of signal processing applications in control systems, computational algorithms, and wavelets in control. Email: [email protected]

Vikas Mittal

Vikas Mittal completed his BTech in electronics and communication engineering from REC Kurukshetra, India (now NIT Kurukshetra) in 1992. He completed his post-graduation and PhD also from NIT Kurukshetra. He has a teaching experience of about 25 years. He has authored more than 30 research papers in international and national journals and conferences. He was head of the Electronics and Communication Engineering Department, NIT Kurukshetra during 2017–2019. Currently, he is associate professor in the same department. His areas of interest are signal and image processing, signal processing applications in control systems, data & image fusion and computational algorithms. Email: [email protected]