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Abstract
Fourier analysis is well known to provide complete information of the frequencies present in a signal. But in the process, time information is lost. Therefore, its time-frequency representation is required for depicting both time and frequency information simultaneously. Therefore in this paper, fractional wavelet transform (FrWT) is proposed to be used for the first time for extracting the features of various datasets in a standard ECG database by combining the advantages of both fractional domain techniques and wavelets as case-II. Afterwards, Probabilistic Principal Component Analysis (PPCA) is used for detecting R-peaks for diagnosing heart abnormalities in various morphologies of the ECG signal. The proposed technique has been evaluated on the basis of sensitivity (SEN), detection error rate (DER), and positive predictivity (PPR) (of the detected ECG beats) for MIT-BIH Arrhythmia database (M/B Ar DB). Even though both FrFT and FrWT techniques exhibit a high degree of robustness, but SEN of 99.99%, DER of 0.026%, & PPR of 99.99% obtained by latter in case-II are better than SEN of 99.97%, DER of 0.053%, & PPR of 99.98% obtained by the former in case-I for M/B Ar DB. In this paper, average time error (ATE) is also obtained for the considered datasets establishing the effectiveness of the proposed technique further. These encouraging results suggest that the proposed methodology will go a long way in assisting the cardiologists to detect temporal patterns in a wide variety of electrophysiological cases, which is important for improved management of healthcare system.
Additional information
Notes on contributors
Varun Gupta
Varun Gupta completed his BTech in electronics and communication engineering from BIT, Meerut in 2007, MTech from Dr B R Ambedkar National Institute of Technology (NIT) Jalandhar in 2011 and PhD from National Institute of Technology (NIT) Kurukshetra in 2020. He is serving as an associate professor in Electronics and Instrumentation Engineering Department, KIET Group of Institutions, Ghaziabad, India for the last nine years. He has published a number of research papers in several reputed international and national journals and along with conferences. He is active reviewer of several journals including IETE Technical Review, IET Signal Processing, IRBM, Analog Integrated Circuits and Signal Processing, Wireless Personal Communications, International Journal of Medical Engineering and Informatics, International Journal of Control and Electrical Engineering, etc. His research includes biomedical signal processing, control system, pattern recognition techniques, soft computing, edge computing, LSTM networks, malware detection in medical IoT devices 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 dctorate from Electrical Engineering Department, NIT Kurukshetra in 2013. She has teaching experience of about 23 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 2017–19. Currently, he is associate professor in the same department. Presently, he has interests in the areas of signal and image processing, signal processing applications in control systems, data & image fusion and computational algorithms. Email: [email protected]