Statistical and entropy-based features can efficiently detect the short-term effect of caffeinated coffee on the cardiac physiology

Pradhan, Bikash K.; Pal, Kunal

doi:10.1016/j.mehy.2020.110323

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…The reason may be that the morphological features are not efficient in tracking the minute changes in the ECG patterns. In our previous study [28], it has been reported that the statistical and entropy-based features that are obtained from 5 s ECG segment can efficiently detect the variation in ECG patterns due to coffee-induced short-term effects. These features reflect only the changes in the time domain.…”

Section: Discussionmentioning

confidence: 99%

“…The lower order statistical features (such as mean and variance) are of lower complexities and are independent of the fiducial points of the ECG signals. The higher-order statistical features, such as skewness and kurtosis, are related to the signal's shape and contain amplitude and phase information [28]. In an ECG signal, the skewness measures the symmetry of the signal around the R-peak, whereas the kurtosis represents whether the distribution of a signal is heavy or light-tailed compared to the normal distribution [56].…”

Section: Discussionmentioning

confidence: 99%

“…In the research of Pradhan and Pal [28], the performance of different tree-based ML models was tested in order to detect coffee-induced short-term effects. The results suggested that random forest was the best ML model with mean accuracy, sensitivity, and AUC of 75.1%, 75.0%, and 84.4%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…However, these methods have not yet been sufficiently explored for the prospect of detecting coffee-induced effects in electro-cardio physiology. In a recent study [28], it has been reported that statistical and entropy-based features can efficiently see coffee-induced changes in an ECG. Nevertheless, these features only reflect the variations of the signals in the time domain.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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“…The HR signal was determined from the BVP by the algorithm proposed by the Empatica wristband designers. The analysis method was the same for both BVP and HR and involved short-term signal fragments and the determination of statistical and entropy-based features [ 80 , 81 ]. Due to the characteristics of the HR signal, it was not filtered before further calculations.…”

Section: Discussionmentioning

confidence: 99%

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Romaniszyn-Kania

Pollak

Bugdoł

et al. 2021

Sensors

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Invasive or uncomfortable procedures especially during healthcare trigger emotions. Technological development of the equipment and systems for monitoring and recording psychophysiological functions enables continuous observation of changes to a situation responding to a situation. The presented study aimed to focus on the analysis of the individual’s affective state. The results reflect the excitation expressed by the subjects’ statements collected with psychological questionnaires. The research group consisted of 49 participants (22 women and 25 men). The measurement protocol included acquiring the electrodermal activity signal, cardiac signals, and accelerometric signals in three axes. Subjective measurements were acquired for affective state using the JAWS questionnaires, for cognitive skills the DST, and for verbal fluency the VFT. The physiological and psychological data were subjected to statistical analysis and then to a machine learning process using different features selection methods (JMI or PCA). The highest accuracy of the kNN classifier was achieved in combination with the JMI method (81.63%) concerning the division complying with the JAWS test results. The classification sensitivity and specificity were 85.71% and 71.43%.

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Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series

Liu

Yang

et al. 2023

Chaos, Solitons & Fractals

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Statistical and entropy-based features can efficiently detect the short-term effect of caffeinated coffee on the cardiac physiology

Cited by 8 publications

References 25 publications

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

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

Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods

Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series

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