Early accurate detection of inferior myocardial infarction is an important way to reduce the mortality from inferior myocardial infarction. Regrading the existing problems in the detection of inferior myocardial infarction, complex model structures and redundant features, this paper proposed a novel inferior myocardial infarction detection algorithm. Firstly, based on the clinic pathological information, the peak and area features of QRS and ST-T wavebands as well as the slope feature of ST waveband were extracted from electrocardiogram (ECG) signals leads Ⅱ, Ⅲ and aVF. In addition, according to individual features and the dispersion between them, we applied genetic algorithm to make judgement and then input the feature with larger degree into support vector machine (SVM) to realize the accurate detection of inferior myocardial infarction. The proposed method in this paper was verified by Physikalisch-Technische Bundesanstalt (PTB) diagnostic electrocardio signal database and the accuracy rate was up to 98.33%. Conforming to the clinical diagnosis and the characteristics of specific changes in inferior myocardial infarction ECG signal, the proposed method can effectively make precise detection of inferior myocardial infarction by morphological features, and therefore is suitable to be applied in portable devices development for clinical promotion.
下壁心肌梗死的早期检测是降低下壁心肌梗死死亡率的重要手段。针对现有的下壁心肌梗死检测存在模型结构复杂、特征冗余的问题，本文提出一种下壁心肌梗死检测算法。该方法与临床病理信息相结合，提取心电信号Ⅱ、Ⅲ和 aVF 三个导联中的 QRS 波段和 ST-T 波段的峰值、面积以及 ST 的斜率等特征，并由遗传算法依据单个特征以及特征之间的离散度进行判断，选择区分度较大的特征送入支持向量机（SVM），实现下壁心肌梗死检测。本文采用德国国家计量学研究所（PTB）诊断心电数据库对该方法进行验证，准确率为 98.33%。本文方法有效地利用心电信号形态学特征实现了下壁心肌梗死的检测，符合临床医生诊断以及下壁心肌梗死心电信号特异性改变的特点，适合临床广泛推广以及便携式设备应用的实现。.
Keywords: electrocardiogram; genetic algorithm; inferior myocardial infarction; morphological characteristics.