Evaluating the clinical utility of speech analysis and machine learning in schizophrenia: A pilot study

Jie Huang; Yanli Zhao; Zhanxiao Tian; Wei Qu; Xia Du; Jie Zhang; Yunlong Tan; Zhiren Wang; Shuping Tan

doi:10.1016/j.compbiomed.2023.107359

Evaluating the clinical utility of speech analysis and machine learning in schizophrenia: A pilot study

Comput Biol Med. 2023 Sep:164:107359. doi: 10.1016/j.compbiomed.2023.107359. Epub 2023 Aug 13.

Authors

Jie Huang¹, Yanli Zhao¹, Zhanxiao Tian¹, Wei Qu¹, Xia Du¹, Jie Zhang¹, Yunlong Tan¹, Zhiren Wang¹, Shuping Tan²

Affiliations

¹ Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China.
² Beijing HuiLongGuan Hospital, Peking University HuiLongGuan Clinical Medical School, Beijing, 100096, China. Electronic address: shupingtan@126.com.

PMID: 37591160
DOI: 10.1016/j.compbiomed.2023.107359

Abstract

Background: Schizophrenia is a serious mental disorder that significantly impacts social functioning and quality of life. However, current diagnostic methods lack objective biomarker support. While some studies have indicated differences in audio features between patients with schizophrenia and healthy controls, these findings are influenced by demographic information and variations in experimental paradigms. Therefore, it is crucial to explore stable and reliable audio biomarkers for an auxiliary diagnosis and disease severity prediction of schizophrenia.

Method: A total of 130 individuals (65 patients with schizophrenia and 65 healthy controls) read three fixed texts containing positive, neutral, and negative emotions, and recorded them. All audio signals were preprocessed and acoustic features were extracted by a librosa-0.9.2 toolkit. Independent sample t-tests were performed on two sets of acoustic features, and Pearson correlation on the acoustic features and Positive and Negative Syndrome Scale (PANSS) scores of the schizophrenia group. Classification algorithms in scikit-learn were used to diagnose schizophrenia and predict the level of negative symptoms.

Results: Significant differences were observed between the two groups in the mfcc_8, mfcc_11, and mfcc_33 of mel-frequency cepstral coefficient (MFCC). Furthermore, a significant correlation was found between mfcc_7 and the negative PANSS scores. Through acoustic features, we could not only differentiate patients with schizophrenia from healthy controls with an accuracy of 0.815 but also predict the grade of the negative symptoms in schizophrenia with an average accuracy of 0.691.

Conclusions: The results demonstrated the considerable potential of acoustic characteristics as reliable biomarkers for diagnosing schizophrenia and predicting clinical symptoms.

Keywords: Clinical symptom; MFCC; Machine learning; Schizophrenia; Speech analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Machine Learning
Pilot Projects
Quality of Life*
Schizophrenia* / diagnosis
Speech