An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter,the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification,and Cox-Stuart test is used to determine whether an early stopping is needed or not,thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10,30 and 60 min,respectively,and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy,sensitivity and specificity of 97.70％,97.36％and 98.04％on the test set,demonstrating its superior performance.

PURPOSE: This study aimed to evaluate the accuracy of the ultrasonic jaw tracking system by comparing with the conventional electronic system in recording condylar movements. MATERIALS AND METHODS: Twenty-six subjects with normal occlusion participated in the study. The CADIAX® 4 and Jaw Motion Analyzer (JMA) systems were used to record condylar movement trajectories during mandibular border movements (protrusive/retrusive, lateral, and wide mouth opening), with each movement repeated three times. Both systems used facebows and sensors to locate the condylar axis points and capture movement trajectory data. Paired t-tests were used for normally distributed data, while the Wilcoxon rank-sum test was applied to non-normally distributed data. The level of significance was set at α = .05. RESULTS: The maximum condylar displacement in the sagittal plane during mandibular border movements and the sagittal condylar inclination (SCI) values on both the left and right sides showed no significant difference between the two systems (P > .05). The Bennett angle (BA) values on both the left and right sides measured by the JMA system were significantly higher than those measured by the CADIAX® 4 system (P < .05). The comfort levels of the JMA system were significantly higher than the CADIAX® 4 system (P < .05). CONCLUSION Through this study, it was found that the accuracy of the ultrasonic jaw tracking system was comparable with the conventional electronic system, except for the Bennett angle measurement. In terms of comfort and ease of use, the ultrasonic jaw tracking system is more favored.

PURPOSE: This study aimed to evaluate the accuracy of the ultrasonic jaw tracking system by comparing with the conventional electronic system in recording condylar movements. MATERIALS AND METHODS: Twenty-six subjects with normal occlusion participated in the study. The CADIAX® 4 and Jaw Motion Analyzer (JMA) systems were used to record condylar movement trajectories during mandibular border movements (protrusive/retrusive, lateral, and wide mouth opening), with each movement repeated three times. Both systems used facebows and sensors to locate the condylar axis points and capture movement trajectory data. Paired t-tests were used for normally distributed data, while the Wilcoxon rank-sum test was applied to non-normally distributed data. The level of significance was set at α = .05. RESULTS: The maximum condylar displacement in the sagittal plane during mandibular border movements and the sagittal condylar inclination (SCI) values on both the left and right sides showed no significant difference between the two systems (P > .05). The Bennett angle (BA) values on both the left and right sides measured by the JMA system were significantly higher than those measured by the CADIAX® 4 system (P < .05). The comfort levels of the JMA system were significantly higher than the CADIAX® 4 system (P < .05). CONCLUSION Through this study, it was found that the accuracy of the ultrasonic jaw tracking system was comparable with the conventional electronic system, except for the Bennett angle measurement. In terms of comfort and ease of use, the ultrasonic jaw tracking system is more favored.

PURPOSE: This study aimed to evaluate the accuracy of the ultrasonic jaw tracking system by comparing with the conventional electronic system in recording condylar movements. MATERIALS AND METHODS: Twenty-six subjects with normal occlusion participated in the study. The CADIAX® 4 and Jaw Motion Analyzer (JMA) systems were used to record condylar movement trajectories during mandibular border movements (protrusive/retrusive, lateral, and wide mouth opening), with each movement repeated three times. Both systems used facebows and sensors to locate the condylar axis points and capture movement trajectory data. Paired t-tests were used for normally distributed data, while the Wilcoxon rank-sum test was applied to non-normally distributed data. The level of significance was set at α = .05. RESULTS: The maximum condylar displacement in the sagittal plane during mandibular border movements and the sagittal condylar inclination (SCI) values on both the left and right sides showed no significant difference between the two systems (P > .05). The Bennett angle (BA) values on both the left and right sides measured by the JMA system were significantly higher than those measured by the CADIAX® 4 system (P < .05). The comfort levels of the JMA system were significantly higher than the CADIAX® 4 system (P < .05). CONCLUSION Through this study, it was found that the accuracy of the ultrasonic jaw tracking system was comparable with the conventional electronic system, except for the Bennett angle measurement. In terms of comfort and ease of use, the ultrasonic jaw tracking system is more favored.

PURPOSE: This study aimed to evaluate the accuracy of the ultrasonic jaw tracking system by comparing with the conventional electronic system in recording condylar movements. MATERIALS AND METHODS: Twenty-six subjects with normal occlusion participated in the study. The CADIAX® 4 and Jaw Motion Analyzer (JMA) systems were used to record condylar movement trajectories during mandibular border movements (protrusive/retrusive, lateral, and wide mouth opening), with each movement repeated three times. Both systems used facebows and sensors to locate the condylar axis points and capture movement trajectory data. Paired t-tests were used for normally distributed data, while the Wilcoxon rank-sum test was applied to non-normally distributed data. The level of significance was set at α = .05. RESULTS: The maximum condylar displacement in the sagittal plane during mandibular border movements and the sagittal condylar inclination (SCI) values on both the left and right sides showed no significant difference between the two systems (P > .05). The Bennett angle (BA) values on both the left and right sides measured by the JMA system were significantly higher than those measured by the CADIAX® 4 system (P < .05). The comfort levels of the JMA system were significantly higher than the CADIAX® 4 system (P < .05). CONCLUSION Through this study, it was found that the accuracy of the ultrasonic jaw tracking system was comparable with the conventional electronic system, except for the Bennett angle measurement. In terms of comfort and ease of use, the ultrasonic jaw tracking system is more favored.

The study aims to address the issues of class imbalance in sleep EEG data and gradient vanishing or explosion phenomena that may occur when deep networks extract more features.An improved adaptive synthetic sampling technique is firstly employed to perform data augmentation on the minority classes of sleep EEG data.Subsequently,convolutional neural networks and residual networks are utilized to learn data features,while a 3-layer bidirectional gated recurrent network is applied to explore deep temporal information and establish correlations between different sleep stages,enabling automatic feature learning and sleep cycle extraction.Finally,a multi-head self-attention mechanism is adopted to enhance the model's focus on critical parts of the sequence,thereby completing the classification of various sleep stages.Experimental results show that according to the AASM sleep staging criteria,the automatic sleep staging model integrating CNN-BiGRU and multi-head self attention achieves an overall accuracy of 90.77%and a Kappa coefficient of 0.88 on the Sleep-EDF-20 dataset after data class balancing,with the precision of N1 stage reaching 87.1%.On the Sleep-EDFx dataset,the model attains an MF1 score of 0.84 while maintaining a precision of 77.2%for N1 stage classification.These metrics demonstrate significant improvements in performance as compared with CNN-BiGRU model tested on the original dataset.When benchmarked against other related studies,the proposed architecture exhibits superior sleep stage classification accuracy.These findings collectively validate the effectiveness and generalization capability of the proposed method.

PURPOSE: This study aimed to evaluate the accuracy of the ultrasonic jaw tracking system by comparing with the conventional electronic system in recording condylar movements. MATERIALS AND METHODS: Twenty-six subjects with normal occlusion participated in the study. The CADIAX® 4 and Jaw Motion Analyzer (JMA) systems were used to record condylar movement trajectories during mandibular border movements (protrusive/retrusive, lateral, and wide mouth opening), with each movement repeated three times. Both systems used facebows and sensors to locate the condylar axis points and capture movement trajectory data. Paired t-tests were used for normally distributed data, while the Wilcoxon rank-sum test was applied to non-normally distributed data. The level of significance was set at α = .05. RESULTS: The maximum condylar displacement in the sagittal plane during mandibular border movements and the sagittal condylar inclination (SCI) values on both the left and right sides showed no significant difference between the two systems (P > .05). The Bennett angle (BA) values on both the left and right sides measured by the JMA system were significantly higher than those measured by the CADIAX® 4 system (P < .05). The comfort levels of the JMA system were significantly higher than the CADIAX® 4 system (P < .05). CONCLUSION Through this study, it was found that the accuracy of the ultrasonic jaw tracking system was comparable with the conventional electronic system, except for the Bennett angle measurement. In terms of comfort and ease of use, the ultrasonic jaw tracking system is more favored.

An epilepsy prediction model based on two-dimensional convolutional neural network and Cox-Stuart test for non-independent patients is proposed to address the problem of how to effectively predict whether epilepsy patients are going to have an attack or not. After EEG data normalization and EEG signal noise removal using a notch filter and a high-pass filter,the filtered signals are inputted into the two-dimensional convolutional neural network model for feature extraction and classification,and Cox-Stuart test is used to determine whether an early stopping is needed or not,thereby reducing the computational and time complexities of the model. The model is tested under the conditions with pre-seizure periods of 10,30 and 60 min,respectively,and the results show that the model performs best when the pre-seizure period is 10 min. The model has an average accuracy,sensitivity and specificity of 97.70％,97.36％and 98.04％on the test set,demonstrating its superior performance.

The study aims to address the issues of class imbalance in sleep EEG data and gradient vanishing or explosion phenomena that may occur when deep networks extract more features.An improved adaptive synthetic sampling technique is firstly employed to perform data augmentation on the minority classes of sleep EEG data.Subsequently,convolutional neural networks and residual networks are utilized to learn data features,while a 3-layer bidirectional gated recurrent network is applied to explore deep temporal information and establish correlations between different sleep stages,enabling automatic feature learning and sleep cycle extraction.Finally,a multi-head self-attention mechanism is adopted to enhance the model's focus on critical parts of the sequence,thereby completing the classification of various sleep stages.Experimental results show that according to the AASM sleep staging criteria,the automatic sleep staging model integrating CNN-BiGRU and multi-head self attention achieves an overall accuracy of 90.77%and a Kappa coefficient of 0.88 on the Sleep-EDF-20 dataset after data class balancing,with the precision of N1 stage reaching 87.1%.On the Sleep-EDFx dataset,the model attains an MF1 score of 0.84 while maintaining a precision of 77.2%for N1 stage classification.These metrics demonstrate significant improvements in performance as compared with CNN-BiGRU model tested on the original dataset.When benchmarked against other related studies,the proposed architecture exhibits superior sleep stage classification accuracy.These findings collectively validate the effectiveness and generalization capability of the proposed method.

Objective To establish a hybrid deep learning lung sound classification model based on convolutional neural network(CNN)-long short-term memory(LSTM)for electronic auscultation.Methods Wavelet transform was used to extract features from the dataset,transforming lung sound signals into energy entropy,peak value and other features.On this basis,a classification model based on hybrid algorithm incorporating CNN and LSTM neural network was constructed.The features extracted by wavelet transform were input into CNN module to obtain the spatial features of the data,and then the temporal features were detected through LSTM module.The fusion of the two types of features enabled the classification of lung sounds through the model,thereby assisting in the diagnosis of pulmonary diseases.Results The accuracy rate and F1 score of CNN-LSTM hybrid model were significantly higher than those of other single models,reaching 0.948 and 0.950.Conclusion The proposed CNN-LSTM hybrid model demonstrates higher accuracy and more precise classification,showcasing broad potential application value in intelligent auscultation.