Izvorni znanstveni članak

https://doi.org/10.24869/psyd.2023.489

A NEW ARTIFICIAL INTELLIGENCE-BASED CLINICAL DECISION SUPPORT SYSTEM FOR DIAGNOSIS OF MAJOR PSYCHIATRIC DISEASES BASED ON VOICE ANALYSIS

Neslihan Cansel ; Assoc. Prof. Dr., Department of Psychiatry, Inonu University, Faculty of Medicine, Malatya, Turkey
Ömer Faruk Alcin ; Assoc. Prof. Dr., Department of Software Engineering, Inonu University, Faculty of Engineering, Malatya, Turkey
Ömer Furkan Yılmaz ; MD, Department of Psychiatry, Yeşilyurt Hasan Çalik Hospital, Malatya, Turkey
Ali Ari ; Assist. Prof. Dr., Department of Computer Engineering, Inonu University, Faculty of Engineering, Malatya, Turkey
Mustafa Akan ; Assist. Prof. Dr., Department of Psychiatry, Turgut Özal University, Faculty of Medicine, Malatya, Turkey
İlknur Ucuz ; Assoc. Prof. Dr., Department of Child and Adolescent Psychiatry, Inonu University Faculty of Medicine Malatya, Turkey

Sažetak

Background: Speech features are essential components of psychiatric examinations, serving as important markers in the recognition
and monitoring of mental illnesses. This study aims to develop a new clinical decision support system based on artificial
intelligence, utilizing speech signals to distinguish between bipolar, depressive, anxiety and schizophrenia spectrum disorders.
Subjects and methods: A total of 79 patients, who were admitted to the psychiatry clinic between 2020-2021, including 15
with schizophrenia spectrum disorders, 24 with anxiety disorders, 25 with depressive disorders, and 15 with bipolar affective disorder,
alongside with 25 healthy individuals were included in the study. The speech signal dataset was created by recording participants’
readings of two texts determined by the Russell emotion model. The number of speech samples was increased by using random sampling
in speech signals. The sample audio signals were decomposed into time-frequency coefficients using Wavelet Packet Transform
(WPT). Feature extraction was performed using each coefficient obtained from both Mel-Frequency Cepstral Coefficients (MFCC) and
Gammatone Cepstral Coefficient (GTCC) methods. The disorder classification was carried out using k-Nearest Neighbor (kNN) and
Support Vector Machine (SVM) classifiers.
Results: The success rate of the developed model in distinguishing the disorders was 96.943%. While the kNN model exhibited
the highest performance in diagnosing bipolar disorder, it performed the least effectively in detecting depressive disorders. Whereas,
the SVM model demonstrated close and high performance in detecting anxiety and psychosis, but its performance was low in identifying
bipolar disorder.
The findings support the utilization of speech analysis for distinguishing major psychiatric disorders. In this regard, the future development
of artificial intelligence-based systems has the potential to enhance the psychiatric diagnosis process.

Ključne riječi

Artificial intelligence; mental illness; psychiatry; speech signal; Russel emotion model

Hrčak ID:

311400

URI

https://hrcak.srce.hr/311400

Datum izdavanja:

9.11.2023.

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