基于卷积神经网络的精神分裂症语音识别与分类

Q3 Medicine

International Journal of Medical Engineering and Informatics Pub Date : 2023-01-01 DOI:10.1504/ijmei.2023.134534

Akshita Abrol, Nisha Kapoor, Parveen Kumar Lehana

{"title":"基于卷积神经网络的精神分裂症语音识别与分类","authors":"Akshita Abrol, Nisha Kapoor, Parveen Kumar Lehana","doi":"10.1504/ijmei.2023.134534","DOIUrl":null,"url":null,"abstract":"Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.","PeriodicalId":39126,"journal":{"name":"International Journal of Medical Engineering and Informatics","volume":"156 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification and classification of schizophrenic speech using convolutional neural network for medical healthcare\",\"authors\":\"Akshita Abrol, Nisha Kapoor, Parveen Kumar Lehana\",\"doi\":\"10.1504/ijmei.2023.134534\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.\",\"PeriodicalId\":39126,\"journal\":{\"name\":\"International Journal of Medical Engineering and Informatics\",\"volume\":\"156 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Medical Engineering and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijmei.2023.134534\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Engineering and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijmei.2023.134534","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}

引用次数: 0

摘要

精神分裂症是一种严重影响患者生活质量的脑部疾病。其突出症状之一是诱导患者的声学变化。在缺乏明确的诊断方法的情况下，语音分析可以帮助对患者进行初步筛选。本文提出了一种利用深度学习自动区分精神分裂症和精神病患者与健康个体的方法。使用以语音谱图为输入的卷积神经网络，对精神分裂症水平的分类准确率为87.01%，对精神分裂症和健康语音的区分准确率为95.26%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Identification and classification of schizophrenic speech using convolutional neural network for medical healthcare

Schizophrenia is a brain disorder that significantly affects the quality of life of affected individuals. One of its prominent symptoms is the induction of changes in the acoustics of the patients. In the absence of definite methods for its diagnosis, speech analysis can help in the preliminary screening of the patients. In this paper, an automated method using deep learning for differentiating between individuals with schizophrenia and psychosis from healthy individuals is suggested. Using convolutional neural networks with speech spectrograms as input, a classification accuracy of 87.01% has been obtained for levels of schizophrenia and 95.26% for differentiating between schizophrenic and healthy speech.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Journal of Medical Engineering and Informatics Medicine-Medicine (miscellaneous)

CiteScore

2.20

自引率

0.00%

发文量

110

期刊介绍： IJMEI promotes an understanding of the structural/functional aspects of disease mechanisms and the application of technology towards the treatment/management of such diseases. It seeks to promote interdisciplinary collaboration between those interested in the theoretical and clinical aspects of medicine and to foster the application of computers and mathematics to problems arising from medical sciences. IJMEI includes authoritative review papers, the reporting of original research, and evaluation reports of new/existing techniques and devices. Each issue also contains a comprehensive information service. Topics covered include Hospital information/medical record systems, data protection/privacy Disease modelling/analysis, evidence-based clinical modelling/studies Computer-based patient/disease management systems Clinical trials/studies, outcome-based studies/analysis Electronic patient monitoring systems Nanotechnology in medicine, medical applications Tissue engineering, artificial organs, biomaterials design Healthcare standards, service standardisation Controlled medical terminology/vocabularies Nursing informatics, systems integration Healthcare/hospital management, economics Medical technology, intelligent instrumentation, telemedicine Medical/molecular imaging, disease management Bioinformatics, human genome studies/analysis Drug design.