基于支持向量机和基于规则处理的自动心电图解释远程监测系统。
A telesurveillance system with automatic electrocardiogram interpretation based on support vector machine and rule-based processing.
机构信息
National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan.
出版信息
JMIR Med Inform. 2015 May 7;3(2):e21. doi: 10.2196/medinform.4397.
BACKGROUND
Telehealth care is a global trend affecting clinical practice around the world. To mitigate the workload of health professionals and provide ubiquitous health care, a comprehensive surveillance system with value-added services based on information technologies must be established.
OBJECTIVE
We conducted this study to describe our proposed telesurveillance system designed for monitoring and classifying electrocardiogram (ECG) signals and to evaluate the performance of ECG classification.
METHODS
We established a telesurveillance system with an automatic ECG interpretation mechanism. The system included: (1) automatic ECG signal transmission via telecommunication, (2) ECG signal processing, including noise elimination, peak estimation, and feature extraction, (3) automatic ECG interpretation based on the support vector machine (SVM) classifier and rule-based processing, and (4) display of ECG signals and their analyzed results. We analyzed 213,420 ECG signals that were diagnosed by cardiologists as the gold standard to verify the classification performance.
RESULTS
In the clinical ECG database from the Telehealth Center of the National Taiwan University Hospital (NTUH), the experimental results showed that the ECG classifier yielded a specificity value of 96.66% for normal rhythm detection, a sensitivity value of 98.50% for disease recognition, and an accuracy value of 81.17% for noise detection. For the detection performance of specific diseases, the recognition model mainly generated sensitivity values of 92.70% for atrial fibrillation, 89.10% for pacemaker rhythm, 88.60% for atrial premature contraction, 72.98% for T-wave inversion, 62.21% for atrial flutter, and 62.57% for first-degree atrioventricular block.
CONCLUSIONS
Through connected telehealth care devices, the telesurveillance system, and the automatic ECG interpretation system, this mechanism was intentionally designed for continuous decision-making support and is reliable enough to reduce the need for face-to-face diagnosis. With this value-added service, the system could widely assist physicians and other health professionals with decision making in clinical practice. The system will be very helpful for the patient who suffers from cardiac disease, but for whom it is inconvenient to go to the hospital very often.
背景
远程医疗保健是一种影响全球临床实践的全球趋势。为了减轻卫生专业人员的工作量并提供无处不在的医疗保健，必须建立一个具有增值服务的基于信息技术的综合监测系统。
目的
我们进行了这项研究，以描述我们提出的远程监测系统，该系统旨在监测和分类心电图（ECG）信号，并评估 ECG 分类的性能。
方法
我们建立了一个具有自动心电图解释机制的远程监测系统。该系统包括：（1）通过电信自动传输心电图信号，（2）心电图信号处理，包括噪声消除、峰值估计和特征提取，（3）基于支持向量机（SVM）分类器和基于规则的处理的自动心电图解释，以及（4）心电图信号及其分析结果的显示。我们分析了 213420 个由心脏病专家诊断为金标准的心电图信号，以验证分类性能。
结果
在国立台湾大学医院（NTUH）远程医疗中心的临床心电图数据库中，实验结果表明，心电图分类器对正常节律检测的特异性值为 96.66%，对疾病识别的灵敏度值为 98.50%，对噪声检测的准确率为 81.17%。对于特定疾病的检测性能，识别模型主要产生了心房颤动的灵敏度值为 92.70%，起搏器节律的灵敏度值为 89.10%，房性早搏的灵敏度值为 88.60%，T 波倒置的灵敏度值为 72.98%，心房扑动的灵敏度值为 62.21%，一度房室传导阻滞的灵敏度值为 62.57%。
结论
通过连接的远程医疗保健设备、远程监测系统和自动心电图解释系统，该机制旨在用于连续的决策支持，并且足够可靠，可以减少面对面诊断的需求。通过这种增值服务，该系统可以广泛协助医生和其他卫生专业人员进行临床实践决策。该系统对于患有心脏病但经常去医院不方便的患者将非常有帮助。
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