A Novel Cardiac Arrhythmias Detection Approach for Real-Time Ambulatory ECG Diagnosis

作者：Zhou, Haiying; Zhu, Xiancheng; Wang, Sishan; Zhou, Kui; Ma, Zheng; Li, Jian^*; Hou, Kun-Mean; De Vaulx, Christophe

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(10): 1758004.

DOI：10.1142/S0218001417580046

• Cardiac arrhythmias detection
• morphological characteristics
• statistical features
• linear discriminant with confidence interval estimation

In view of requirements of low-resource consumption and high-efficiency in real-time Ambulatory Electrocardiograph Diagnosis (AED) applications, a novel Cardiac Arrhythmias Detection (CAD) algorithm is proposed. This algorithm consists of three core modules: an automatic-learning machine that models diagnostic criteria and grades the emergency events of cardiac arrhythmias by studying morphological characteristics of ECG signals and experiential knowledge of cardiologists; a rhythm classifier that recognizes and classifies heart rhythms basing on statistical features comparison and linear discriminant with confidence interval estimation; and an arrhythmias interpreter that assesses emergency events of cardia arrhythmias basing on a two rule-relative interpretation mechanisms. The experiential results on off-line MIT-BIH cardiac arrhythmia database as well as online clinical testing explore that this algorithm has 92.8% sensitivity and 97.5% specificity in average, so that it is suitable for real-time cardiac arrhythmias monitoring.

• 出版日期2017-10
• 单位湖北汽车工业学院