Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

Barros, Carla; Silva, Carlos A.; Pinheiro, Ana

doi:10.1016/j.artmed.2021.102039

Cited by 78 publications

(43 citation statements)

References 153 publications

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“…The current work intends to assess the potential of deep models to learn discriminatory EEG patterns in the early stages of auditory processing, which may inform about the significance of sensory changes to SZ diagnosis and prognosis. We followed good practices for the development and implementation of machine learning methods proposed in Barros et al (30). This paper is organized as follows.…”

Section: Related Workmentioning

confidence: 99%

“…Although deep learning architectures based on EEG signals have been proposed for SZ classification, the learning of patterns from the electrical brain response to auditory stimuli is a scarcely investigated topic. A recent review provided a critical analysis of deep learning and classical machine learning methods to detect SZ based on EEG signals ( 30 ), highlighting the potentialities of these methods in clinical research. Notwithstanding, from this review it is also clear that more studies are necessary and that surpass the limitations of the existing ones.…”

Section: Introductionmentioning

confidence: 99%

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From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

et al. 2022

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Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

et al. 2022

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“…RDoC can be effective supplementary information but the necessary data can be costly to obtain and is therefore less accessible in developing countries, or for patients who must pay to obtain a brain scan but cannot afford to do so. DSM-ICD remains the gold-standard for diagnosis (Barros et al, 2021). For this reason, we design our diagnostic machine learning system using the traditional symptomatic approach, using DSM-5 symptoms.…”

Section: Diagnosis Of Schizophreniamentioning

confidence: 99%

RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis

Organisciak¹,

Shum²,

Nwoye³

et al. 2022

Preprint

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Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has recently become a popular way to analyse and interpret medical data. Past attempts to use deep learning for schizophrenia diagnosis from brain-imaging data have shown promise but suffer from a large trainingapplication gap -it is difficult to apply lab research to the real world. We propose to reduce this training-application gap by focusing on readily accessible data. We collect a data set of psychiatric observations of patients based on DSM-5 criteria. Because similar data is already recorded in all mental health clinics that diagnose schizophrenia using DSM-5, our method could be easily integrated into current processes as a tool to assist clinicians, whilst abiding by formal diagnostic criteria. To facilitate real-world usage of our system, we show that it is interpretable and robust. Understanding how a machine learning tool reaches its diagnosis is essential to allow clinicians to trust that diagnosis.To interpret the framework, we fuse two complementary attention mechanisms, 'squeeze and excitation' and 'self-attention', to determine global attribute importance and attribute interactivity, respectively. The model uses these im- *

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“…Machine learning can be defined as computer models and algorithms that are able to automatically learn and adapt from data and experience without explicit instructions or human intervention [41][42][43][44][45][46][47][48]. As can be seen in Figure 5, machine learning methods can be classified into four categories: supervised learning, unsupervised learning deep learning, and reinforcement learning-the first three being more common.…”

Section: Machine Learning Algorithms Employed In Eeg Signal Classific...mentioning

confidence: 99%

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

et al. 2021

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In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

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Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

Cited by 78 publications

References 153 publications

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

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