To solve the problem of lacking of the subtypes of hypertension and the pathogenesis of complications in current clinical analysis, an analysis model involving integrating principal components analysis (PCA), K-means clustering algorithm, and Apriori algorithm was proposed in this article. Firstly, according to the redundant interference problem caused by the diversity of the patients' clinical index, the PCA theory was used to reduce the dimension and the redundant relationship. Secondly, on the basis of obtaining the main component of the clinical index data, the K-means algorithm was used to conduct the patients’ group analysis. Finally, the Apriori algorithm was used to analyze the frequent pattern of complications based on the complication data of different patients group. We used an example to verify efficacy of the above methods. The new analysis model of complications of hypertensive patients would provide an effective solution for the application of the current medical big data.
The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.