Park Juyoung, Kang Kyungtae
Department of Computer Science and Engineering, Hanyang University , Ansan, Republic of Korea.
Telemed J E Health. 2014 Dec;20(12):1069-77. doi: 10.1089/tmj.2014.0033.
The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor.
We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan-Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree.
We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes.
This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.
心电图（ECG）数据的自动解读能够对心脏活动进行持续分析，从而实现对动态心电图监测仪等无线设备的有效利用。
我们提出了一种智能心跳监测系统，以实时检测心律失常的可能性。我们使用Pan-Tompkins算法从心电图信号中检测心跳并提取QRS波群和P波等特征，然后使用决策树将心跳分为16种类型。
我们根据麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库的数据测试了系统的灵敏度、特异性和准确性。我们的系统在心跳检测中的平均准确率达到97%，心跳分类平均准确率超过96%，与最佳竞争方案相当。
这项工作为动态心电图监测中用于决策支持的智能分类系统的系统设计提供了指导。
