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Comparing the HRV Time-Series Signals Acquired from Cannabis Consuming and Non-Consuming Indian Paddy-Field Workers by Recurrence Quantification Analysis - 21/11/20

Doi : 10.1016/j.irbm.2020.11.001 
S.K. Nayak a, K.K. Tarafdar a, S. Banani a, I. Banerjee a, D. Kim b, K. Pal a,
a Department of Biotechnology and Medical Engineering, National Institute of Technology, Rourkela, 769008, India 
b Department of International Agricultural Technology & Institute of Green BioScience and Technology, Seoul National University, Gwangwon-do-25354, Republic of Korea 

Corresponding author.
Sous presse. Épreuves corrigées par l'auteur. Disponible en ligne depuis le Saturday 21 November 2020

Graphical abstract

Le texte complet de cet article est disponible en PDF.

Highlights

Recurrence Quantification Analysis of HRV time-series signals were carried out.
Visual changes were found in the recurrence plots of the test and control groups.
Mann-Whitney U test suggested that the changes in RQA features were insignificant.
An SVM model has been proposed for automated identification of the altered state.

Le texte complet de cet article est disponible en PDF.

Abstract

Objective

In the last few decades, the consumption of cannabis-based products for recreational purposes has dramatically increased. Unfortunately, cannabis consumption has been associated with the incidences of cardiovascular diseases. Hence, there is a necessity for understanding the plausible mechanics of cardiophysiological changes due to cannabis consumption. Accordingly, the current study was designed to understand the suitability of the recurrence quantification analysis (RQA) method in detecting the changes in the heart rate variability (HRV) time-series signals due to the consumption of cannabis (bhang). Further, a machine learning model has been proposed for the automated detection of the cannabis takers.

Materials and Methods

The RQA of the HRV time-series signals from 200 healthy Indian male paddy-field workers were carried out. The obtained parameters were statistically analyzed using the Mann-Whitney U test. Further, the decision trees, weight-based feature ranking, and dimensionality reduction methods were employed for identifying the relevant features for the development of a suitable machine learning model.

Results

Observable changes in the patterns of the recurrence plots among the bhang consuming and non-consuming groups were noticed. However, there were no significant differences in the RQA parameters. Among the developed machine learning models, the SVM model obtained from the “Information gain ratio” feature selection method exhibited the highest accuracy (%) of 69.09 ± 9.33.

Conclusion

Our study suggests that the RQA method is not as effective as the time and frequency domain methods for detecting the alterations in the HRV time-series signals due to cannabis consumption. The SVM model was found to be the best model for the automated detection of cannabis takers. The selection of the features by the information gain ratio method played an important role in the development of the optimized SVM model.

Le texte complet de cet article est disponible en PDF.

Keywords : Cannabis, Autonomic nervous system, Recurrence plot, Recurrence quantification analysis, Machine learning model


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