A deep learning approach for analyzing brainwave signals during audio meditation in expectant mothers: variational mode decomposition with a CNN-BiLSTM model.
Daisy Das, Nabamita Deb, Saswati Sanyal Choudhury
Biomedical physics & engineering express March 31, 2026 DOI: 10.1088/2057-1976/ae55a8 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational study Peer reviewed |
|---|---|
| Population | Pregnant women |
| Intervention | audio meditation |
| Topics | Meditation |
| Keywords | Cnn EEG Lstm Vmd Audio |
| Key findings | A VMD-CNN-BiLSTM model classified resting, meditation, and post-meditation EEG states with 94.79% accuracy, verifying that brief audio meditation produces detectable neural changes in pregnant women. |
Abstract
Meditation has increasingly been recognized as a helpful non-pharmacological intervention to treat psychological stress, anxiety, and depression during pregnancy-a period that plays a significant role in maternal and fetal health. Although very popular, there is very little scientific evidence on how brief audio meditation affects brain activity in pregnant women. This work reports an EEG-based method to objectively investigate the neural effect of short-term audio meditation in pregnant women with the aid of sophisticated signal decomposition and deep learning methods. Electroencephalogram (EEG) signals were acquired from pregnant women in various mental states: resting, meditation, and post-meditation. For improved feature representation, Variational Mode Decomposition (VMD) was used to decompose each EEG channel into five intrinsic mode functions, representing multi-resolution signal components. The IMFs were subsequently processed through a hybrid deep learning model integrating Convolutional Neural Networks (CNNs) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) networks for temporal dynamics modeling. The test accuracy of the recommended VMD-CNN-BiLSTM architecture was 94.79%, and test loss was 0.2975, proving its high reliability in classifying EEG states during meditation. The model's performance verifies its ability to extract frequency-specific and temporal features characteristic of short-term cognitive modulation. This study offers a new, objective framework to evaluate the effect of meditation during prenatal care and opens avenues toward personalized, brain-based mindfulness training in maternal care.
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 12
- Neural mechanisms of attentional control in mindfulness meditation Frontiers in Neuroscience 2013
- Effect of Gayatri Mantra Meditation on Meditation Naive Subjects: an EEG and fMRI Pilot Study International Journal of Indian Psychology March 25, 2016
- Ensemble Learning based EEG Classification – Investigating the Effects of Combined Yoga and Rajyog Meditation Journal of Scientific & Industrial Research 2025
- Investigating the effects of focused attention (mantra) meditation on mismatch negativity: Insights into sensory and cognitive processing using an intensity oddball paradigm. Neuroscience April 6, 2025
- Tetrahedral feature map for analyzing meditative states in prenatal EEG Engineering Research Express May 6, 2025
and 7 more in the library