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

Abstract

This study aimed to explore more efficient ways of administering caffeine to the body by investigating the impact of caffeine on the modulation of the nervous system's activity through the analysis of electrocardiographic signals (ECG). An ECG non-linear multi-band analysis using Discrete Wavelet Transform (DWT) was employed to extract various features from healthy individuals exposed to different caffeine consumption methods: expresso coffee (EC), decaffeinated coffee (ED), Caffeine Oral Films (OF_caffeine), and placebo OF (OF_placebo). Non-linear feature distributions representing every ECG minute time series have been selected by PCA with different variance percentages to serve as inputs for 23 machine learning models in a leave-one-out cross-validation process for analyzing the behavior differences between ED/EC and OF_placebo/OF_caffeine groups, respectively, over time. The study generated 50-point accuracy curves per model, representing the discrimination power between groups throughout the 50 min. The best model accuracies for ED/EC varied between 30 and 70 %, (using the decision tree classifier) and OF_placebo/OF_caffeine ranged from 62 to 84 % (using Fine Gaussian). Notably, caffeine delivery through OFs demonstrated effective capacity compared to its placebo counterpart, as evidenced by significant differences in accuracy curves between OF_placebo/OF_caffeine. Caffeine delivery via OFs also exhibited rapid dissolution efficiency and controlled release rate over time, distinguishing it from EC. The study supports the potential of caffeine delivery through Caffeine OFs as a superior technology compared to traditional methods by means of ECG analysis. It highlights the efficiency of OFs in controlling the release of caffeine and underscores their promise for future caffeine delivery systems.

Keywords: Caffeine; Discrete wavelet transform; Electrocardiographic signals; Machine learning; Non-linear features; Oral films.

Fig. 1

Methodology workflow.

Fig. 2

Comparison of the best-selected results for each binary classification.

Fig. 3

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained without feature selection (PCA = 100 %).

Fig. 4

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 95 %.

Fig. 5

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 80 %.

Fig. 6

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 70 %.

Fig. 7

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 50 %.

Fig. 8

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 20 %.

Fig. 9

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 10 %.

Fig. 10

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 5 %.

Fig. 11

50-point accuracy curves of the classifiers with the best results for both binary classifications, when the system was trained with PCA = 1 %.

Fig. 12

Accuracy curves of the classifiers with the best research results.