Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers
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 m...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10050865/ |
| _version_ | 1797855398259064832 |
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| author | Dipen Deka Bhabesh Deka |
| author_facet | Dipen Deka Bhabesh Deka |
| author_sort | Dipen Deka |
| collection | DOAJ |
| description | 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 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 classifier results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%. |
| first_indexed | 2024-04-09T20:22:00Z |
| format | Article |
| id | doaj.art-588b7e194aa349a9b0980a44a44566f3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T20:22:00Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-588b7e194aa349a9b0980a44a44566f32023-03-30T23:00:49ZengIEEEIEEE Access2169-35362023-01-0111295762959010.1109/ACCESS.2023.324826310050865Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning ClassifiersDipen Deka0https://orcid.org/0000-0002-4284-0740Bhabesh Deka1https://orcid.org/0000-0002-9679-6159Department of Instrumentation Engineering, Central Institute of Technology (CIT) Kokrajhar, Kokrajhar, IndiaDepartment of ECE, School of Engineering, Tezpur University, Tezpur, IndiaIn 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 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 classifier results in the best performance with an average accuracy of 96.67%, sensitivity of 95.83% and specificity of 97.5%.https://ieeexplore.ieee.org/document/10050865/Heart rate variabilitymeditationscalogramvisibility graphentropyclassification |
| spellingShingle | Dipen Deka Bhabesh Deka Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers IEEE Access Heart rate variability meditation scalogram visibility graph entropy classification |
| title | Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers |
| title_full | Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers |
| title_fullStr | Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers |
| title_full_unstemmed | Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers |
| title_short | Detection of Meditation-Induced HRV Dynamics Using Averaging Technique-Based Oversampled Feature Set and Machine Learning Classifiers |
| title_sort | detection of meditation induced hrv dynamics using averaging technique based oversampled feature set and machine learning classifiers |
| topic | Heart rate variability meditation scalogram visibility graph entropy classification |
| url | https://ieeexplore.ieee.org/document/10050865/ |
| work_keys_str_mv | AT dipendeka detectionofmeditationinducedhrvdynamicsusingaveragingtechniquebasedoversampledfeaturesetandmachinelearningclassifiers AT bhabeshdeka detectionofmeditationinducedhrvdynamicsusingaveragingtechniquebasedoversampledfeaturesetandmachinelearningclassifiers |