Paroxysmal kinesigenic dyskinesia (PKD) is a rare neurological disorder marked by transient involuntary movements triggered by sudden actions. Current diagnostic approaches, including genetic screening, face challenges in identifying secondary cases due to symptom overlap with other disorders. This study introduces a novel PKD recognition method utilizing a resting-state electroencephalogram (EEG) functional connectivity matrix and a deep learning architecture (AT-1CBL). Resting-state EEG data from 44 PKD patients and 44 healthy controls (HCs) were collected using a 128-channel EEG system. Functional connectivity matrices were computed and transformed into graph data to examine brain network property differences between PKD patients and controls through graph theory. Source localization was conducted to explore neural circuit differences in patients. The AT-1CBL model, integrating 1D-CNN and Bi-LSTM with attentional mechanisms, achieved a classification accuracy of 93.77% on phase lag index (PLI) features in the Theta band. Graph theoretic analysis revealed significant phase synchronization impairments in the Theta band of the functional brain network in PKD patients, particularly in the distribution of weak connections compared to HCs. Source localization analyses indicated greater differences in functional connectivity in sensorimotor regions and the frontal-limbic system in PKD patients, suggesting abnormalities in motor integration related to clinical symptoms. This study highlights the potential of deep learning models based on EEG functional connectivity for accurate and cost-effective PKD diagnosis, supporting the development of portable EEG devices for clinical monitoring and diagnosis. However, the limited dataset size may affect generalizability, and further exploration of multimodal data integration and advanced deep learning architectures is necessary to enhance the robustness of PKD diagnostic models.

The severe challenges of the fast aging population and the prevalence of cardiovascular diseases highlight the needs for effective solutions supporting more accurate and affordable medical diagnosis and treatment. Recent advances in cloud computing have inspired numerous designs of cloud-based health care services. In this paper, we developed a cloud-computing platform monitored by physicians, which can receive 12-lead ECG records and send back diagnostic reports to users. Aiming to lessen the physicians' workload, we implemented an analysis algorithm that can identify abnormal heart rate, irregular heartbeat, abnormal amplitude, atrial fibrillation and abnormal ECG in it. A large number of testing samples were used to evaluate performance. Our algorithm achieved a TPR95 (specificity under the condition of negative predictive value being equal to 95 percent) of 68.5 percent and 0.9317 AUC (area under the ROC curve) for classification of normal and abnormal ECG records and a sensitivity of 98.51 percent and specificity of 98.26 percent for atrial fibrillation classification, comparable to the state-of-the-art results for each subject. The proposed ECG cloud computing service has been applied in Hunan Jinshengda Aerial Hospital Network and it now can receive and analyze ECG records in real time.