Artificial intelligence techniques for automated diagnosis of neurological disorders
Background: Authors have been advocating the research ideology that a computer-aided
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
diagnosis (CAD) system trained using lots of patient data and physiological signals and …
Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls
The complexity and heterogeneity of schizophrenia symptoms challenge an objective
diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the …
diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the …
Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models
A Shoeibi, D Sadeghi, P Moridian… - Frontiers in …, 2021 - frontiersin.org
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals
in the brain, the function of some brain regions is out of balance, leading to the lack of …
in the brain, the function of some brain regions is out of balance, leading to the lack of …
A computerized method for automatic detection of schizophrenia using EEG signals
Diagnosis of schizophrenia (SZ) is traditionally performed through patient's interviews by a
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
skilled psychiatrist. This process is time-consuming, burdensome, subject to error and bias …
SPWVD-CNN for automated detection of schizophrenia patients using EEG signals
Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions,
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis …
VGG19 network assisted joint segmentation and classification of lung nodules in CT images
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are
essential to cure the patient. This paper introduces a deep learning framework to support the …
essential to cure the patient. This paper introduces a deep learning framework to support the …
Detection of Parkinson's disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques
Early detection of Parkinson's disease (PD) is very important in clinical diagnosis for
preventing disease development. In this study, we present efficient discrete wavelet …
preventing disease development. In this study, we present efficient discrete wavelet …
Fusion of multivariate EEG signals for schizophrenia detection using CNN and machine learning techniques
Schizophrenia is a severe mental disorder that has adverse effects on the behavior of an
individual such as disorganized speech and delusions. Electroencephalography (EEG) …
individual such as disorganized speech and delusions. Electroencephalography (EEG) …
Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals
Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral
characteristics such as interruption in thinking, memory, perception, speech and other living …
characteristics such as interruption in thinking, memory, perception, speech and other living …
[HTML][HTML] Schizophrenia classification using machine learning on resting state EEG signal
JR De Miras, AJ Ibáñez-Molina, MF Soriano… - … Signal Processing and …, 2023 - Elsevier
Schizophrenia is a severe mental disorder associated with a wide spectrum of cognitive and
neurophysiological dysfunctions. Early diagnosis is still difficult and based on the …
neurophysiological dysfunctions. Early diagnosis is still difficult and based on the …