Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques

Abstract

Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients.

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

“…They achieved an accuracy of 100% in classifying normal and pathological voices when the tests were performed on two databases [ 97 , 98 ]. Qian et al [ 134 ] and Huang et al [ 187 ] tried the popular end-to-end models in speech recognition and Transformer-based models for pathological speech signals processing. Their recognition results were very consistent with the evaluation scales of the patients.…”

“…DL accounts for only a small part of the current pathological voice recognition methods because a large amount of data is unavailable [ 92 , 93 , 101 , 104 , 110 , [119] , [124] , 129 , 145 , 146 ]. However, researchers can continuously explore the potential of DL methods such as Attention-based LSTM [ 45 , 47 ], end-to-end models [ 134 ], and Transformer models [ 187 ] and try advanced recognition algorithms to improve the performance of IST for medical applications. Moreover, the fusion of voice signals with signals of other modalities such as electroacoustic gate signals, EMR, X-ray images, and ultrasound [ 4 , 5 ] will be more valuable for disease diagnosis in smart hospitals.…”

Network Analysis of Magnetoencephalogram Signals in Schizophrenia Patients When Viewing Emotional Facial Stimuli

“…The calculation formula of the connection matrix A (i,j) of the functional network is shown in formula (2).…”

“…S CHIZOPHRENIA is a common and serious mental disorder with symptoms including hallucinations, delusions, disordered thoughts, and cognitive impairment [1], [2]. Although schizophrenia has been studied for many years, its pathophysiological pathogenesis is still unclear [3].…”

Learning from Label Relationships in Human Affect

“…Similarly in [33], by making per-frame predictions for depression estimation, the temporal dimension is not taken into consideration. More recently, in [21] the temporal dimension is taken into consideration as the work leverages audio and text modalities, however, no modality for vision is implemented. In contrast, our work proposes a transformer-based architecture to learn from the temporal dimension.…”