A5008964809- ECG Monitoring and Analysis
- EEG and Brain-Computer Interfaces
- Quality and Safety in Healthcare
- Non-Invasive Vital Sign Monitoring
- Phonocardiography and Auscultation Techniques
- Artificial Intelligence in Healthcare
- Machine Learning in Healthcare
Zhengzhou University
2019-2021
Zhengzhou University of Industrial Technology
2019
Abstract Background: In the field of diagnostic CVD, predecessors used a large amount data with no missing two-category data, and obtained good results. However, in process electronic input historical number attribute values are missing, there multiple levels disease risk. Goal: On set imbalance values, this paper focuses on five cardiovascular disease. Methods: A new prediction model Adaboost+RF is constructed by using information gain ratio to analyze feature contribution degree set. The...
Arrhythmia is one of the most common abnormal symptoms that can threaten human life. In order to distinguish arrhythmia more accurately, classification strategy multifeature combination and Stacking-DWKNN algorithm proposed in this paper. The method consists four modules. preprocessing module, signal denoised segmented. Then, multiple different features are extracted based on single heartbeat morphology, P length, QRS T PR interval, ST segment, QT RR R amplitude, amplitude. Subsequently,...
Severe arrhythmia can threaten human life, therefore, the timely detection of is important. In this paper, a clustering method based on PCA-KNN proposed. Firstly, P-QRS-T waves are extracted. Then principal component analysis (PCA) algorithm used to reduce dimension high-dimensional heartbeat. Finally, k-nearest neighbor (KNN) recognition arrhythmia. Experiments MIT-BIH database show that compared with most advanced methods, accuracy model as high 98.99%.