基于高频心电图的缺血型心脏疾病分类算法

Classification Algorithm for Ischemic Cardiac Diseases Based on High-Frequency Electrocardiogram

现阶段高频心电图(high-frequency electrocardiogram,HFECG)分类算法多为心梗(myocardial infarction,MI)与非心梗的二类分类或心梗类别分类算法,无法在心梗早期的心肌缺血阶段发现病例。基于此,本文提出了一种基于高频心电图的缺血型心脏疾病分类算法。该算法选取并改进了6个高频成分参数作为特征,使用XGBoost模型对样本进行分类。相较于传统算法,该算法增加了对缺血型异常(ischemic,ISC)病例的分类,可以及早发现心梗潜在病例。此外,本文对高频成分参数中幅值下降区域的求解过程与形态学指标进行了改进,提高了算法性能。采用本文算法在PTB-XL数据集上进行了实验,并利用临床数据进行了验证。实验结果表明,本文采用的高频心电图特征对于心肌缺血异常具有较强的表征能力,针对PTB-XL数据集,对四分类类别:正常(NORM)、其他异常(ABNORM)、ISC和MI的识别准确率依次为83.9%,81.7%,88.2%和93.9%。该算法可以有效挖掘处于心梗早期心肌缺血阶段的病例。

Most of the high-frequency electrocardiogram(HFECG) classification algorithms at this stage are either two-class classification of myocardial infarction(MI) and non-infarct or infarct class classification algorithms, which cannot detect cases in the early myocardial ischemic stage of infarction. For this reason, an ischemic cardiac disease classification algorithm based on HFECG is proposed in this paper. The algorithm selects and improves six high-frequency component parameters as features and uses the XGBoost model to classify the samples. Compared with the traditional algorithm, this algorithm adds the classification of ischemic(ISC) cases, which can detect potential cases of MI early. In addition, this paper improves the solving process and morphological index of the reduced amplitude zone(RAZ) in the high-frequency component parameters, which improves the classification performance of the algorithm. The algorithm in this paper is used in the experiments on the PTB-XL data set, and the clinical data is used to verify it. The experimental results show that the HFECG features used in this paper have strong characterization ability for myocardial ischemic abnormalities, with accuracy rates of 83.9%, 81.7%, 88.2% and 93.9% for PTB-XL data set for the four classification categories: normal(NORM), abnormity(ABNORM), ISC and MI, in that order. The algorithm can effectively tap cases in the early myocardial ischemic stage of infarction.