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Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.

ABSTRACT: Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance.

SUBMITTER: Hecker P

PROVIDER: S-EPMC9309252 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPolycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder that affects women of reproductive age, characterized by chronic anovulation, hyperandrogenism, and polycystic ovarian morphology. Emerging evidence indicates that neurological disorders play a significant role in the etiology of PCOS, highlighting the complex interplay between the central nervous system (CNS) and ovarian function. Yulinzhu, a traditional Chinese medicine (TCM) formulation, has been traditionally used to regulate menstrual cycles and improve fertility. This study aims to investigate the efficacy and mechanisms of Yulinzhu in treating PCOS induced by neurological disorders.MethodsAn extensive literature search was performed across electronic databases such as PubMed, EMBASE, Cochrane Library, and China National Knowledge Infrastructure (CNKI), covering publications up to 1 June 2024. The review included randomized controlled trials (RCTs) that compared Yulin Zhu with placebo, standard care, or other active treatments in patients with PCOS. Two reviewers independently carried out data extraction and quality assessment. Meta-analyses were conducted using both fixed and random-effects models, with heterogeneity evaluated using the I2 statistic.ResultsWe screened 891 records and included 6 studi es in the meta-analysis. The meta-analysis revealed that Yulinzhu about effective rate [RR = 1.19, 95% CI (1.10, 1.29), p < 0.0001], pregnancy rate [RR = 2.80, 95% CI (1.65, 4.76), p < 0.0001] and ovulation rate [RR = 1.33, 95% CI (1.10, 1.62), p = 0.04]. Meta-analysis shows the results of follicle estrogen [WMD = 0.69, 95% CI (-0.39, 1.78), p = 0.21], luteinizing hormone [WMD = -2.27, 95% CI (-3.86, -0.67), p = 0.005], testosterone [WMD = -0.44, 95% CI (-0.64, -0.25), p < 0.0001], estradiol [WMD = 16.20, 95% CI (2.74, 29.67), p < 0.0001].ConclusionThis study demonstrates that plant medicine compund Yulinzhu may effectively treats PCOS including hormonal regulation, anti-inflammatory actions, and neuroprotection. We expect further research with larger, well-designed clinical trials to substantiate our conclusions.

Dataset Information

Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends.

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