论文标题
通过使用机器学习技术来识别缺血性心脏病，根据测量心率变异性的参数
Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability
论文作者
论文摘要
心脏病的诊断是对患者临床数据的适当检查通常解决的一项艰巨任务。最近，事实证明，使用心率变异性（HRV）分析以及某些机器学习算法是诊断过程中的宝贵支持。但是，到目前为止，仅根据人工神经网络（ANN）诊断出缺血性心脏病（IHD），仅适用于迹象，症状和顺序的ECG和冠状动脉造影，这是一种侵入性工具，而可以通过从HRV中提取的参数来轻松地从ECG中获得的信号来以非侵入性的方式识别。在这项研究中，有18个非侵入性特征（年龄，性别，左心室射血分数和15个从HRV获得），有243名受试者（156名正常受试者和87名IHD患者）用于训练和验证一系列几个ANN，不同的输入和隐藏节点不同。最佳结果是使用7个输入参数和7个隐藏节点获得的培训和验证数据集的精度分别为98.9％和82％。
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from HRV, a signal easily obtained from the ECG. In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects (156 normal subjects and 87 IHD patients) were used to train and validate a series of several ANN, different for number of input and hidden nodes. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset, respectively.
