An Innovative Deep Learning Approach to Depression Detection Using Eeg
Keywords:
KNN, LSTM, EEG, Emotion, Stress, Deep Learning, E-healthcare.Abstract
This paper presents an innovative approach to depression detection by integrating K-Nearest Neighbors (KNN) machine learning with Long Short-Term Memory (LSTM) deep learning techniques applied to Electroencephalogram (EEG) data. Depression remains a significant global health issue, and traditional diagnostic methods often suffer from subjectivity and delays. To address these challenges, we propose a hybrid model that leverages the strengths of KNN for initial classification and LSTM for capturing temporal dependencies in EEG signals. The KNN algorithm provides a straightforward and interpretable classification approach, while LSTM networks are adept at handling sequential data and detecting patterns over time. By combining these methods, our approach aims to enhance the accuracy and reliability of depression detection, offering a more objective and efficient diagnostic tool. The results demonstrate that this hybrid model outperforms traditional methods in terms of classification accuracy and sensitivity, highlighting its potential for improving early detection and intervention strategies for depression.
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