Skip to content

Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers

Dipen Deka, Bhabesh Deka

IEEE Access 2023 DOI: 10.1109/access.2023.3248263 (opens in new tab)

Read Full Text
Relevant Not relevant

Study at a glance

AI-extracted from the abstract
Characteristics Observational study with machine learning classification Peer reviewed
Sample size 12
Population Meditation practitioners (human subjects)
Intervention Meditation
Measures scalogram image texture features, visibility graph-based complexity measures, multiscale permutation entropies (MPEs)
Topics Meditation
Citations 11
Key findings Meditative states were associated with significant changes in scalogram pixel intensity distributions and undiminished permutation entropy at higher scales. A radial basis function SVM classifier distinguished meditative from pre-meditative states with 96.67% average accuracy, 95.83% sensitivity, and 97.5% specificity, with augmented data outperforming non-augmented data.

Abstract

In this paper, we propose a textural information-based analysis of scalogram image obtained from continuous wavelet transform of heart rate variability (HRV) signal to study its dynamics during meditation. In addition to features from scalogram image, visibility graph-based complexity measures and multiscale permutation entropies (MPEs) from HRV signal are used to elucidate the modulation in autonomic activity of heart during meditative and non-meditative state. Significant changes in the probability distribution of pixel intensities of scalogram image and undiminished permutation entropy at higher scales are observed during the meditative state. From the extracted features, we have selected the top-ranked features based on ReliefF algorithm and minimum threshold weight of importance set at 0.04. Considering the small sample size (12 subjects) of meditation dataset, we have employed a novel data augmentation technique based on averaging of feature sets to overcome the issue of overfitting. To examine the efficacy of the proposed technique, both the non-augmented and augmented data are applied to four different types of classifiers, namely k-nearest neighbor, support vector machine (SVM), logistic regression and random forest classifiers. Experimental results demonstrate that performance of classifiers in distinguishing meditative and pre-meditative state are much superior with the augmented data as compared to that with regular non-augmented data. Out of these classifiers, radial basis function (RBF)-based SVM results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%.

Comparable studies

Other observational and cohort studies on meditation, most cited first.

Study Year Design Participants
Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students 2006 Observational cohort
Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia 1992 Observational cohort n = 22
Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls 2005 Observational cohort n = 20
Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls 2011 Observational cohort
Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls 2004 Observational cohort

Citations in the library

Cites 5

Related reading

Explore topics

Meditation