ObjectiveThe aim of this study was to investigate the value of Artificial Neural Networks (ANNs) in predicting the occurrence of Venous Thromboembolism (VTE) in patients with Obstructive Sleep Apnea (OSA), and to compare it with traditional Logistic regression models to assess its predictive efficacy, providing theoretical basis for the prediction of VTE risk in OSA patients. MethodsA retrospective analysis was conducted on patients diagnosed with OSA and hospitalized in the Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Kunming Medical University, from January 2018 to August 2023. Patients were divided into OSA combined with VTE group (n=128) and pure OSA control group (n=680). The dataset was randomly divided into a training set (n=646) and an independent validation set (n=162). The Synthetic Minority Oversampling Technique (SMOTE) was employed to address the issue of data imbalance. Artificial Neural Networks and Logistic regression models were then built on training sets with and without SMOTE. Finally, the performance of each model was evaluated using accuracy, sensitivity, specificity, Youden's index, and Area Under the Receiver Operating Characteristic Curve (AUC). Results When oversampling was conducted using SMOTE on the training set, both the Artificial Neural Network and Logistic regression models showed improved AUC. The Artificial Neural Network model with SMOTE performed the best with an AUC value of 0.935 (95%CI: 0.898–0.961), achieving an accuracy of 90.15%, specificity of 87.32%, sensitivity of 93.44%, and Youden’s index of 0.808 at the optimal cutoff point. The Logistic regression model with SMOTE yielded an AUC value of 0.817 (95%CI: 0.765–0.861), with an accuracy of 77.27%, specificity of 83.80%, sensitivity of 69.67%, and Youden's index of 0.535. The difference in AUC between the Artificial Neural Network model and Logistic regression model was statistically significant after employing SMOTE (P<0.05). Conclusions The Artificial Neural Network model demonstrates high effectiveness in predicting VTE formation in OSA patients, particularly with the further improvement in predictive performance when utilizing SMOTE oversampling technique, rendering it more accurate and stable compared to the traditional Logistic regression model.
With the increasing number of electrocardiogram (ECG) data, extensive application requirements of computer-aided ECG analysis have occurred. In the paper, we propose a variety of strategies to improve the performance of clinical ECG classification algorithm based on Lead Convolutional Neural Network (LCNN). Firstly, we obtained two classifiers by using different preprocessing methods and training methods in the study. Then, we applied the multiple output prediction method to both of them independently. Finally, the Bayesian approach was employed to fuse them. Tests conducted using more than 150 000 ECG records showed that the proposed method had an accuracy of 85.04% and the area under receiver operating characteristic curve (AUC) was 0.918 5, which significantly outperforms traditional methods based on feature extraction techniques.
The amygdala, a core limbic structure, plays a critical role in epilepsy due to its structural and functional heterogeneity. Its major subnuclei—the basolateral, centromedial, and superficial cortical nuclei—differ in cellular composition, connectivity, and function, and form extensive circuits with the prefrontal cortex, hippocampus, and hypothalamus. In epilepsy, the amygdala may become a hyperexcitable microcircuit through impaired GABAergic inhibition, ion channel dysfunction, altered synaptic plasticity, and neuroinflammation, thereby lowering seizure threshold. Abnormal amygdalar activity can propagate to the frontotemporal cortex via the uncinate fasciculus or influence autonomic centers through the stria terminalis. Theta/gamma coupling and high-frequency oscillations within the amygdala–hippocampal network are key electrophysiological markers of seizure spread. Clinically, different subnuclei are associated with distinct seizure symptoms: the basolateral nucleus is linked to affective auras such as fear, the centromedial nucleus to autonomic symptoms such as palpitations, and the superficial cortical nuclei to olfactory or gustatory auras. Stereotactic electroencephalography and multimodal neuroimaging improve epileptogenic zone localization and support precise surgical or neuromodulatory treatment targeting the amygdala. Future research should further clarify subnucleus-specific mechanisms, developmental dynamics, and cross-species differences to advance targeted therapies for amygdala-related epilepsy.