计算机科学
心房颤动
阵发性心房颤动
人工智能
支持向量机
特征提取
极限学习机
模式识别(心理学)
特征(语言学)
信号(编程语言)
机器学习
心脏病学
人工神经网络
医学
哲学
程序设计语言
语言学
作者
Eman A. Maghawry,Rasha M. Ismail,Tarek F. Gharib
出处
期刊:Journal of Intelligent and Fuzzy Systems
[IOS Press]
日期:2021-03-02
卷期号:40 (3): 5087-5099
被引量:3
摘要
Paroxysmal Atrial Fibrillation (PAF) is a special class of Atrial Fibrillation. Predicting PAF events from electrocardiogram (ECG) signal streams plays a vital role in generating real-time alerts for cardiac disorders. These alerts are extremely important to cardiologists in taking precautions to prevent their patients from having a stroke. In this study, an effective predictive approach to PAF events using the Extreme Learning Machine classification technique is proposed. Besides, we propose a feature extraction method that integrates new ECG signal features to its time-domain ones. The new features are based on the construction of sparse vectors for peaks in ECG signals that provide high overlap between similar ECGs. The proposed prediction approach with the new ECG features representation were evaluated on a real PAF dataset using the five-fold cross-validation method. Experiments show promising results for predicting PAF in terms of accuracy and execution time compared to other existing studies. The proposed approach achieved classification accuracy of 97% for non-streaming ECG signals mode and 94.4% for streaming mode.
科研通智能强力驱动
Strongly Powered by AbleSci AI