Fuzzy Rule-Based Multimodal Health Monitoring System Leveraging Machine Learning Techniques Using Eeg Datasets For Human Emotion And Psychological Disorders
- Title
- Fuzzy Rule-Based Multimodal Health Monitoring System Leveraging Machine Learning Techniques Using Eeg Datasets For Human Emotion And Psychological Disorders
- Creator
- Saba, Tahseen
- Contributor
- Addapalli, V N Krishna and Danti, Ajit
- Description
- In recent decades, machine learning and data analysis have become increasingly important in mental health for diagnosing and treating psychological disorders. One area of particular interest is the use of electroencephalography (EEG) brainwave data to classify emotional states and predict psychological disorders. This study proposed a data fusion to enhance the precision of emotion recognition. A feature selection strategy using data fusion techniques was implemented, along with a multi-layer Stacking Classifier combining various algorithms such as support vector classifier, Random Forest, multilayer perceptron, and Nu-support vector classifiers. Features were selected based on Linear Regression-based correlation coefficient scores, resulting in a dataset with 39% of the original 2548 features. This framework achieved a high precision of 98.75% in identifying emotions. The study also focused on negative emotional states for recognizing psychological disorders. A Genetic Algorithm (GA) was used for feature selection, and k-means clustering organized the data. The dataset included 707 trials and 2542 unlabeled features. Resampling techniques ensured a balanced representation of emotional states, and GASearchCV optimized Gradient Boosting classifier hyperparameters. The Elbow Method determined the optimal number of k-Means clusters, and resampling addressed class imbalance. GA parameters and gradient- boosting hyperparameters were empirically determined. ROC curves and classification reports evaluated performance, resulting in a high accuracy of 97.21% in predicting psychological disorders. The proposed system employed fuzzy logic to calculate a health score that combines the outputs of the emotional and psychological disorder monitoring models for a multimodal health monitoring system. This approach provides a more comprehensive assessment of an individual's overall mental health status. The findings suggest that the system achieved high efficiency in predicting emotions, showcasing comprehensive progress in EEG-based emotion analysis and disorder diagnosis. These advancements have potential implications for mental health monitoring and treatment, particularly with the integration of the PHQ-9 Scale and fuzzy logic.
- Source
- Author's Submission
- Date
- 2024-01-01
- Publisher
- Christ(Deemed to be University)
- Subject
- Computer Science and Engineering
- Rights
- Open Access
- Relation
- 61000431
- Format
- Language
- English
- Type
- PhD
- Identifier
- http://hdl.handle.net/10603/620497
Collection
Citation
Saba, Tahseen , “Fuzzy Rule-Based Multimodal Health Monitoring System Leveraging Machine Learning Techniques Using Eeg Datasets For Human Emotion And Psychological Disorders,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 22, 2025, https://archives.christuniversity.in/items/show/12469.