Bio-acoustic features of depression: A review

Almaghrabi, Shaykhah A.; Clark, Scott; Baumert, Mathias

doi:10.1016/j.bspc.2023.105020

Cited by 11 publications

(3 citation statements)

References 116 publications

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“…In the multi-classification task, the severity of depression was divided into four levels: no depression, mild depression, moderate depression, and severe depression. The PHQ-8 score (range 0-24) was discretized into 4 categories: [0-4], [5][6][7][8][9] , [10][11][12][13][14] and [15][16][17][18][19][20][21][22][23][24], and these four categories are labeled as non, mild, moderate and severe, respectively.…”

Section: Experimental Tasksmentioning

confidence: 99%

“…For instance, Mustafa et al used non-intrusive RF sensing for early diagnosis of spinal curvature syndrome disorders 22 . As a non-invasive diagnostic approach, voice signals are widely utilized for the detection of emotional disorders, owing to their rich abundance of pathophysiological information [23][24][25] . Moreover, employing the human voice for automatic diagnostic models in depression offers several advantages, including non-invasive data acquisition, relatively straightforward data collection, and low recording costs.…”

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confidence: 99%

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Depression recognition using voice-based pre-training model

Huang,

Wang,

Gao

et al. 2024

Sci Rep

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The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

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Section: Experimental Tasksmentioning

confidence: 99%

mentioning

confidence: 99%

Depression recognition using voice-based pre-training model

Huang,

Wang,

Gao

et al. 2024

Sci Rep

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“…Initial efforts targeting mental health have largely considered their use in adults, leveraging passively collected sensor data from smart phones and other sensors to identify phenotypes of mental health disorders and changes in their associated symptoms (e.g., [32][33][34][35][36][37]). A particular focus has been on vocal biomarkers of mental health [38][39][40][41] which have quickly emerged as one of the most promising and feasible measures to consider. More recent efforts are beginning to expand to consider additional data sources including biomarkers derived from wearable movement sensors [16,17,22], videos of body and facial movements [42,43], and a variety of physiological measurements such as heart rate, heart rate variability, respirations, and galvanic skin response [24][25][26]28,29].…”

Section: Introductionmentioning

confidence: 99%

Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges

O’Leary,

Lahey,

Lovato

et al. 2024

Sensors

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In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.

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