Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.
Introduction: Correlation of Pirani score and foot bimalleolar (FBM) angle has been used in few studies but correlation of FBM angle with ultrasonography has never been evaluated so they are being correlated in assessing the severity of clubfoot in neonates treated by Ponseti method. Material and Methods: Thirty-two feet with congenital talipes equinovarus (CTEV) deformity in neonates were prospectively treated by the Ponseti method. FBM angle and ultrasound parameters were measured three times i.e. at the time of initial presentation, at four weeks of treatment and at completion of treatment. The feet were divided according to the Pirani score in groups: one (0-2.0), two (2.5-4) and three (4.5-6). Correlation between FBM angle and ultrasound parameters were evaluated using Pearson correlation/regression. Results: Correlation between FBM angle and ultrasound parameters were statistically significant (p-value < 0.05). Conclusion: Ultrasound has the potential to accurately depict the pathoanatomy in clubfoot. FBM angle and ultrasound are objective methods to assess the severity of clubfoot. FBM angle and ultrasonography correlated in severity of deformity and correction achieved along the course of treatment.