A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
复制标题
DOI：
10.3389/fphy.2019.00103
复制
发表时间：
2019-07-18
影响因子：
3.1
通讯作者：
Ortin, Silvia
中科院分区：
文献类型：
--
作者：
Alfaras, Miquel;Soriano, Miguel C.;Ortin, Silvia
我们提出了一个全自动和快速的心电图心律失常分类器的基础上一个简单的大脑启发的机器学习方法称为回声状态网络。我们的分类器具有低要求的特征处理，仅需要单个ECG导联。其训练和验证遵循患者间程序。我们的方法与在线分类兼容，与健康监测无线设备和可穿戴设备的最新进展保持一致。使用集成的组合使我们能够利用并行性来训练具有显着速度的分类器。心跳分类器在两个心电图数据库（MIT-BIH AR和AHA）上进行评估。在MIT-BIH AR数据库中，我们的分类方法对于室性异位搏动提供了92.7%的灵敏度和86.1%的阳性预测值，使用单导联II时，以及95.7%的灵敏度和75.1%的阳性预测值，当使用导联V1 '时。这些结果与全自动ECG分类器的最新技术水平相当，甚至优于遵循更复杂特征选择方法的其他ECG分类器。
We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.