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Automated Detection of Cannabis-Induced Alteration in Cardiac Autonomic Regulation of the Indian Paddy-Field Workers Using Empirical Mode Decomposition, Discrete Wavelet Transform and Wavelet Packet Decomposition Techniques with HRV Signals
Early detection of the dysfunction of the cardiac autonomic regulation (CAR) may help in reducing cannabis-related cardiovascular morbidities. The current study examined the occurrence of changes in the CAR activity that is associated with the consumption of bhang, a cannabis-based product. For this purpose, the heart rate variability (HRV) signals of 200 Indian male volunteers, who were categorized into cannabis consumers and non-consumers, were decomposed by Empirical Mode Decomposition (EMD), Discrete Wavelet transform (DWT), and Wavelet Packet Decomposition (WPD) at different levels. The entropy-based parameters were computed from all the decomposed signals. The statistical significance of the parameters was examined using the Mann–Whitney test and t-test. The results revealed a significant variation in the HRV signals among the two groups. Herein, we proposed the development of machine learning (ML) models for the automatic classification of cannabis consumers and non-consumers. The selection of suitable input parameters for the ML models was performed by employing weight-based parameter ranking and dimension reduction methods. The performance indices of the ML models were compared. The results recommended the Naïve Bayes (NB) model developed from WPD processing (level 8, db02 mother wavelet) of the HRV signals as the most suitable ML model for automatic identification of cannabis users.
Investigating the effect of sound in horror clip on the cardiac electrophysiology of young adults using wavelet packet decomposition and machine learning classifiers
Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD
The effect of coffee (caffeinated) on electro-cardiac activity is not yet sufficiently researched. In the current study, the occurrence of coffee-induced short-term changes in electrocardiogram (ECG) signals was examined. Further, a machine learning model that can efficiently detect coffee-induced alterations in cardiac activity is proposed. The ECG signals were decomposed using three different joint time–frequency decomposition methods: empirical mode decomposition, discrete wavelet transforms, and wavelet packet decomposition with varying decomposition parameters. Various statistical and entropy-based features were computed from the decomposed coefficients. The statistical significance of these features was computed using Wilcoxon’s signed-rank (WSR) test for significance testing. The results of the WSR tests infer a significant change in many of these parameters after the consumption of coffee (caffeinated). Further, the analysis of the frequency bands of the decomposed coefficients reveals that most of the significant change was localized in the lower frequency band (<22.5 Hz). Herein, the performance of nine machine learning models is compared and a gradient-boosted tree classifier is proposed as the best model. The results suggest that the gradient-boosted tree (GBT) model that was developed using a db2 mother wavelet at level 2 decomposition shows the highest mean classification accuracy of 78%. The outcome of the current study will open up new possibilities in detecting the effects of drugs, various food products, and alcohol on cardiac functionality.