ST segment morphology is closely related to cardiovascular disease. It is used not only for characterizing different diseases, but also for predicting the severity of the disease. However, the short duration, low energy, variable morphology and interference from various noises make ST segment morphology classification a difficult task. In this paper, we address the problems of single feature extraction and low classification accuracy of ST segment morphology classification, and use the gradient of ST surface to improve the accuracy of ST segment morphology multi-classification. In this paper, we identify five ST segment morphologies: normal, upward-sloping elevation, arch-back elevation, horizontal depression, and arch-back depression. Firstly, we select an ST segment candidate segment according to the QRS wave group location and medical statistical law. Secondly, we extract ST segment area, mean value, difference with reference baseline, slope, and mean squared error features. In addition, the ST segment is converted into a surface, the gradient features of the ST surface are extracted, and the morphological features are formed into a feature vector. Finally, the support vector machine is used to classify the ST segment, and then the ST segment morphology is multi-classified. The MIT-Beth Israel Hospital Database (MITDB) and the European ST-T database (EDB) were used as data sources to validate the algorithm in this paper, and the results showed that the algorithm in this paper achieved an average recognition rate of 97.79% and 95.60%, respectively, in the process of ST segment recognition. Based on the results of this paper, it is expected that this method can be introduced in the clinical setting in the future to provide morphological guidance for the diagnosis of cardiovascular diseases in the clinic and improve the diagnostic efficiency.
ST段的形态变化和心血管疾病息息相关，不仅能表征不同的疾病，并且能够预示患病的严重程度。但ST段持续时间短、能量低、形态多变并且受到多种噪声的干扰，导致ST段形态分类成为一个难题。本文针对目前ST段形态分类存在的特征提取单一、分类准确率低等问题，利用ST曲面的梯度来提高ST段形态多分类的精度。本文对正常、上斜型抬高、弓背型抬高、水平型压低、弓背型压低五种ST段形态进行识别，首先根据QRS波群位置及医学统计规律选定一个ST段候选段，其次提取ST段面积、均值、与参考基线差值、斜率、均方差特征。此外，将ST段转换成曲面，提取ST曲面的梯度特征，与形态学特征组成特征向量，最后使用支持向量机分类，进而实现ST段形态多分类。采用麻省理工学院-贝斯以色列医院数据库（MITDB）和欧盟ST-T数据库（EDB）为数据来源对本文算法进行验证，结果显示，本文算法在ST段识别过程中分别达到了97.79%和95.60%的平均准确率。基于本文研究结果，期望今后可在临床环境中引入本文方法，为临床中心血管疾病的诊断提供形态指导，提高诊断效率。.
Keywords: Electrocardiogram signal; Feature fusion; Gradient; ST segment morphology; Support vector machine.