Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection

• Published in: Computers in biology and medicine Vol. 150; p. 106122
• Main Authors: Kaur, Baljeet, Rathi, Swati, Agrawal, R.K.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.11.2022; Elsevier Limited
• Subjects: Acoustics; Algorithms; Animals; Biomarkers; Computer applications; Datasets; Depression; Depression - diagnosis; Discriminant analysis; Evolutionary algorithms; Experiments; Feature extraction; Feature selection; Frequency; Genetic algorithms; Humans; Internal Medicine; Mental depression; Optimization; Optimization algorithms; Other; Quantum-based Whale Optimization Algorithm; Self evaluation; Speech; Statistical analysis; Statistical tests; Suicides & suicide attempts; Support vector machines; Temporal variations; Whales; Depression; Speech; Feature extraction; Feature selection; Quantum-based Whale Optimization Algorithm
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106122

There is an urgent need to detect depression using a non-intrusive approach that is reliable and accurate. In this paper, a simple and efficient unimodal depression detection approach based on speech is proposed, which is non-invasive, cost-effective and computationally inexpensive. A set of spectral, temporal and spectro-temporal features is derived from the speech signal of healthy and depressed subjects. To select a minimal subset of the relevant and non-redundant speech features to detect depression, a two-phase approach based on the nature-inspired wrapper-based feature selection Quantum-based Whale Optimization Algorithm (QWOA) is proposed. Experiments are performed on the publicly available Distress Analysis Interview Corpus Wizard-of-Oz (DAICWOZ) dataset and compared with three established univariate filtering techniques for feature selection and four well-known evolutionary algorithms. The proposed model outperforms all the univariate filter feature selection techniques and the evolutionary algorithms. It has low computational complexity in comparison to traditional wrapper-based evolutionary methods. The performance of the proposed approach is superior in comparison to existing unimodal and multimodal automated depression detection models. The combination of spectral, temporal and spectro-temporal speech features gave the best result with the LDA classifier. The performance achieved with the proposed approach, in terms of F1-score for the depressed class and the non-depressed class and error is 0.846, 0.932 and 0.094 respectively. Statistical tests demonstrate that the acoustic features selected using the proposed approach are non-redundant and discriminatory. Statistical tests also establish that the performance of the proposed approach is significantly better than that of the traditional wrapper-based evolutionary methods. •Non-invasive low-complexity two-phase speech-based depression detection system.•Proposed two-phase approach involving QWOA gives high performance.•Spectral temporal and spectro-temporal features investigated extensively.•Selected speech features by the method are relevant and statistically significant.