Speech signal acoustic analysis in the diagnosis of neurological and mental diseases: a systematic review and meta-analysis

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Abstract
Objective. To assess speech signal acoustic parameters for the diagnosis of neurological and mental diseases. Material and methods. The data were searched in accordance with the PRISMA requirements and guidelines across the PubMed, Google Scholar, ClinicalTrials.gov, CyberLenink, and eLibrary databases. Seven publications were selected for the final analysis of full-text articles. These papers evaluated the parameters of the speech signal in patients with Parkinson’s disease, Alzheimer’s disease, and primary depression. The meta-analysis examined Jitter and Shimmer in patients’ speech signals compared with those of healthy volunteers. Results. Six studies were included in the meta-analysis in assessing the diagnostic capabilities of changes in fundamental tone frequency (Jitter) for neurological and mental diseases. The meta-analysis included 180 patients and 193 healthy volunteers. According to the results, Jitter was significantly more pronounced in patients with Alzheimer’s disease, depression, and Parkinson’s disease than in healthy volunteers (mean difference 0.786 (0.481—1.091), I2=45.83%, p<0.001). The meta-analysis of speech-signal acoustic analysis results for diagnosing neurological and mental diseases, based on changes in signal amplitude (Shimmer), included 7 studies involving 222 patients and 295 healthy volunteers. There was no statistically significant difference in Shimmer in patients with Alzheimer’s disease, depression, and Parkinson’s disease, compared to patients without diseases (mean difference 0.392 (–0.179—0.962), I2=88.86%, p=0.178). Conclusion. This systematic review and meta-analysis of studies have shown the potential of acoustic analysis of the speech signal for diagnosing neurological and mental diseases. Jitter is a reliable diagnostic criterion for the presence and progression of neurological and mental diseases.

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