Automated Detection of Caffeinated Coffee-Induced Short-Term Effects on ECG Signals Using EMD, DWT, and WPD

Abstract

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.

Evaluation of the responsiveness pattern to caffeine through a smart data-driven ECG non-linear multi-band analysis

“…First, a superficial analysis of the accuracy curves shows that the amplitude of the classifiers' performance is within the expected range. The ability to detect physiological changes induced by caffeine demonstrates the discriminative power of ECG signals across both active and placebo modalities, highlighting the effectiveness of the DWT approach as documented in the literature [ 73 ]. Although the data extracted from these signals confirm that caffeine influences cardiac activity, the results do not precisely delineate the exact morphological changes in the ECG characteristics.…”

A Review on the Applications of Time‐Frequency Methods in ECG Analysis

“…Pradhan and Pal (2020) have reported that it is possible to use time-domain statistical and entropy-based features extracted from the ECG signal to automatically detect the presence of a psychoactive drug, "cafeine," in the body [186]. In a recent study [187], the authors employed three diferent time-frequency methods, EMD, DWT, and WPD, to automatically detect the cafeinated cofee-inducedshort-term efect in the ECG signal. Te application of ECG signals in seeing the impact of drugs and alcohol is new, and hence, a limited study is available in the literature.…”

The acute effects of coffee consumption on blood glucose and it’s relationship with serum cortisol and insulin in females

“…Caffeine counteracts the effects of adenosine, and moderate doses (40-200 mg) increase alertness, reduce fatigue, and shorten reaction time (Ballis 2019;Dam et al 2020). Adenosine is a vasodilator, and its inhibition causes sympathetic reflexes to be activated (Pradhan et al 2022). Caffeine works by blocking adenosine receptors A1 and A2, as well as influencing the autonomic nervous system (ANS), preventing adenosis, which results in central nervous system (CNS) activity via catecholamine release (Pradhan et al 2022).…”

“…Adenosine is a vasodilator, and its inhibition causes sympathetic reflexes to be activated (Pradhan et al 2022). Caffeine works by blocking adenosine receptors A1 and A2, as well as influencing the autonomic nervous system (ANS), preventing adenosis, which results in central nervous system (CNS) activity via catecholamine release (Pradhan et al 2022). Caffeine has an anti-obesity effect because it suppresses appetite, improves energy balance, basal metabolic rate, and thermogenesis by enhancing expression of uncoupling protein-1 in brown fat tissue and stimulating the sympathetic nervous system (Dam et al 2020).…”

“…Caffeine has neuroprotective properties, such as lowering the risk of depression and suicide, dementia, and stroke, but high doses (1200 mg or more) consumption is likely to cause sleep disorders, severe anxiety, palpitation, and elevated blood pressure. Caffeine increases catecholamine secretion, which stimulates autonomic nervous system (ANS) activity (Pradhan et al 2022).…”