Mental Stress Classification Using Multivariate Analysis of Variance with Bidirectional Long Short-Term Memory Model
Received: 29 May 2025 | Revised: 30 July 2025 | Accepted: 20 August 2025 | Online: 8 December 2025
Corresponding author: Raksha Rajanna
Abstract
In humans, stress is a natural reaction to pressure, and when stress increases, the risk of mental health issues also increases. Misclassification can be caused by redundancy in certain physiological and behavioral features. To overcome this limitation, this study performs a Multivariate Analysis of Variance (MANOVA) based feature selection method, along with a Bidirectional Long Short-Term Memory (Bi-LSTM) model for efficient stress classification. The proposed MANOVA technique evaluates multiple dependent variables simultaneously, capturing correlations between physiological features to identify the most informative for the classification of mental stress. The Bi-LSTM model processes stress-related physiological signals, including heart rate and skin conductance, both forward and backward, effectively capturing long-term dependencies that help improve classification. Initially, ElectroCardioGram (ECG) signal data were obtained from two benchmark datasets. Then, label encoding techniques were employed for converting categorical features into numerical ones, and normalization was used to scale the data into a uniform range. The proposed stress classification model was experimentally evaluated on the WESAD and SWELL-KW datasets, achieving accuracies of 99.50% and 99.80%, respectively, outperforming existing approaches.
Keywords:
bidirectional long short-term memory, electrocardiogram, label encoding, multivariate analysis of variance, stress classificationDownloads
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Copyright (c) 2025 Raksha Rajanna, Pushpalatha Mullur Puttubuddhi, Impana Kamalamma Puttaraju
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