Vocal acoustic analysis and machine learning for the identification of schizophrenia
- PDF / 652,281 Bytes
- 14 Pages / 595.276 x 790.866 pts Page_size
- 57 Downloads / 137 Views
ORIGINAL ARTICLE
Vocal acoustic analysis and machine learning for the identification of schizophrenia Caroline Wanderley Espinola 1,2 & Juliana Carneiro Gomes 3 & Jessiane Mônica Silva Pereira 3 & Wellington Pinheiro dos Santos 1 Received: 16 May 2020 / Accepted: 22 September 2020 # Sociedade Brasileira de Engenharia Biomedica 2020
Abstract Purpose Psychiatry still needs objective biomarkers. In the context of schizophrenia, there are speech abnormalities such as tangentiality, derailment, alogia, neologisms, poverty of speech, and aprosodia. There is a growing interest in speech signals features as possible indicators of schizophrenia. This article aims to develop an intelligent tool for detection of schizophrenia using vocal patterns and machine learning techniques. The main advantages of this type of solution are the low cost, high performance, and for being non-invasive. Methods Thirty-one individuals over 18 years old were selected, 20 with previous diagnosis of schizophrenia, and 11 healthy controls. Their speech was audio-recorded in naturalistic settings, during a routine medical assessment for psychiatric patients. In the case of healthy patients, the recordings were made in different environments. Recordings were pre-processed, excluding non-participant voices. We extracted 33 features. We used the particle swarm optimization algorithm for feature selection. Results The classifiers’ performance was analyzed with four metrics: accuracy, sensibility, specificity, and kappa index. Best results were achieved when considering all 33 extracted features. Within machine models, support vector machines (SVM) models provided the greatest classification performance, with mean accuracy of 91.76% for PUK kernel. Our results outperform those from most studies published so far for the detection of schizophrenia based on acoustic patterns. Conclusion The use of machine learning classifiers using vocal parameters, in particular SVM, has shown to be very promising for the detection of schizophrenia. Nevertheless, further experiments with a larger sample will be necessary to validate our findings. Keywords Schizophrenia . Diagnosis . Voice . Acoustic parameters . Machine learning . Support vector machines
Introduction
* Wellington Pinheiro dos Santos [email protected] Caroline Wanderley Espinola [email protected] Jessiane Mônica Silva Pereira [email protected]; [email protected] 1
Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Recife, Brazil
2
Serviço de Emergências Psiquiátricas, Hospital Ulysses Pernambucano, Recife, Brazil
3
Núcleo de Engenharia da Computação, Escola Politécnica da Universidade de Pernambuco, Recife, Brazil
Current clinical practice in psychiatry depends on diagnostic criteria built entirely on expert consensus, instead of relying on objective biomarkers (Bzdok and Meyer-lindenberg 2018). Such criteria, described in the Diagnostic and Statistical Manual, 5th Edition (DSM-5), and in the International Classification of Diseases (ICD-10), are still consi
Data Loading...
