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Wavelet Packet Analysis of ECG Signals to Understand the Effect of Cannabis Abuse on Cardiac Electrophysiology of Indian Women Working in Paddy Fields
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Author(s): Suraj Kumar Nayak (National Institute of Technology Rourkela, India), Ashirbad Pradhan (National Institute of Technology Rourkela, India), Salman Siddique Khan (National Institute of Technology Rourkela, India), Shikshya Nayak (Veer Surendra Sai University of Technology, India), Soumanti Das (National Institute of Technology Rourkela, India), Gitika Yadu (National Institute of Technology Rourkela, India), Shankar Jaykishan Patel (Jagnyaseni Hospital, India), Champak Bhattacharyya (National Institute of Technology Rourkela, India)and Kunal Pal (National Institute of Technology Rourkela, India)
Copyright: 2022
Pages: 17
Source title:
Research Anthology on Artificial Neural Network Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-2408-7.ch060
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Abstract
This chapter is aimed at identifying the variation in the cardiac electrophysiology due to the abuse of the cannabis products (bhang) in a non-invasive manner. ECG signals were acquired from 25 Indian women working in the paddy fields. Amongst them, 10 women regularly abused bhang and the rest 15 women never abused bhang. The ECG signals were preprocessed and subjected to wavelet packet decomposition (WPD) up to the level 3 using db04 wavelet. Ninety-six statistical features were extracted from the wavelet packet coefficients and analyzed using linear and non-linear statistical methods. The results suggested a variation in the cardiac electrophysiology due to the abuse of bhang. Artificial neural networks (ANNs), namely, radial basis function (RBF) and multilayer perceptron (MLP) were able to classify the ECG signals with an accuracy of ≥95%. This supported the hypothesis that abuse of bhang may alter the cardiac electrophysiology. The results of the study may be used to increase awareness among people to avoid the abuse of cannabis products.
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