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• W4296821937 abstract "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." @default.
• W4296821937 created "2022-09-24" @default.
• W4296821937 creator A5044492667 @default.
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• W4296821937 date "2022-11-01" @default.
• W4296821937 modified "2023-10-18" @default.
• W4296821937 title "Enhanced depression detection from speech using Quantum Whale Optimization Algorithm for feature selection" @default.
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• W4296821937 doi "https://doi.org/10.1016/j.compbiomed.2022.106122" @default.
• W4296821937 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36182759" @default.
• W4296821937 hasPublicationYear "2022" @default.
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