BILMB: Design of a hybrid Bio inspired Incremental Learning-based model for analysing effects of Meditation on different Body-parts
A. Tayade, R. Khobragade, D. Datar
2023 6th International Conference on Information Systems and Computer Networks March 3, 2023 DOI: 10.1109/iscon57294.2023.10112067 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or methodological paper Peer reviewed |
|---|---|
| Topics | Meditation |
| Key points | Proposes a hybrid bio-inspired incremental learning model that combines multi-domain EEG feature extraction, Elephant Herding optimization, and a customized 1D CNN to analyze meditation effects on body parts. The authors report accuracy improvements of 5.9% for meditation detection and 8.3% for effect analysis over existing methods. |
Abstract
Meditation has proven to be a highly effective technique for improving the health conditions of different body parts. Pranayama assists in improving lung & brain function, while Transcendental Meditation assists in reducing stress and improving heart functions. Thus, effective analysis of meditation & its effects on human body can assist in intelligent adaptation of these techniques for different disorders. A wide variety of scanning, pre-processing & classification models are proposed by researchers to perform these tasks, and each of these models vary in terms of their application & function characteristics. But these models are either highly complex, or have lower efficiency when used under clinical conditions. Moreover, most of these models use a single type of analysis (either frequency, or entropy, or their counterparts), which restricts their applicability to limited use cases. To overcome these issues, this text proposes design of a novel hybrid bio inspired incremental learning-based model for analyzing effects of meditation on different body-parts. The proposed model uses a collected of pre-stored EEG signals of persons who have meditated & not meditated, along with their temporal health-reports. These signals are initially represented in multiple domains that assists in estimation of frequency, entropy, Gabor, wavelet, and convolutional feature sets. These feature sets are later selected by an Elephant Herding optimizer (EHO) which assists in identification of highly variant features. These features are used to train a customized ID CNN for identifying effects of meditation on different body parts, on temporal basis. Due to these operations, the proposed model is able to improve accuracy of meditation detection by 5.9%, and improve the effect analysis accuracy by 8.3% when compared with existing methods under similar use cases.
Comparable studies
Other theoretical and historical papers on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Mindfulness: A proposed operational definition. | 2004 | Theoretical or philosophical paper | |
| Mechanisms of mindfulness | 2005 | Theoretical or philosophical paper | |
| How Does Mindfulness Meditation Work? Proposing Mechanisms of Action From a Conceptual and Neural Perspective | 2011 | Theoretical or philosophical paper | |
| Mindfulness-based cognitive therapy for depression | 2006 | Theoretical or philosophical paper | |
| Self-awareness, self-regulation, and self-transcendence (S-ART): a framework for understanding the neurobiological mechanisms of mindfulness | 2012 | Theoretical or philosophical paper |
Citations in the library
Cites 15
- Quantitative change of EEG and respiration signals during mindfulness meditation Journal of NeuroEngineering and Rehabilitation May 14, 2014
- EEG Derived Neuronal Dynamics during Meditation: Progress and Challenges Advances in Preventive Medicine 2015
- Large effects of brief meditation intervention on EEG spectra in meditation novices IBRO Reports October 27, 2020
- Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture Sensors December 21, 2020
- Adaptive Baseline Correction of Meditation EEG American Journal of Electroneurodiagnostic Technology June 1, 2001
and 10 more in the library