Xiong Peng, Xue Yanping, Liu Ming, Du Haiman, Wang Hongrui, Liu Xiuling
Key Laboratory of Digital Medical Engineering of Hebei Province, College of Electronic and Information Engineering, Hebei University, Baoding, Hebei 071002, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2020 Feb 25;37(1):142-149. doi: 10.7507/1001-5515.201904028.
Inferior myocardial infarction is an acute ischemic heart disease with high mortality, which is easy to induce life-threatening complications such as arrhythmia, heart failure and cardiogenic shock. Therefore, it is of great clinical value to carry out accurate and efficient early diagnosis of inferior myocardial infarction. Electrocardiogram is the most sensitive means for early diagnosis of inferior myocardial infarction. This paper proposes a method for detecting inferior myocardial infarction based on densely connected convolutional neural network. The method uses the original electrocardiogram (ECG) signals of serially connected Ⅱ, Ⅲ and aVF leads as the input of the model and extracts the robust features of the ECG signals by using the scale invariance of the convolutional layers. The characteristic transmission of ECG signals is enhanced by the dense connectivity between different layers, so that the network can automatically learn the effective features with strong robustness and high recognition, so as to achieve accurate detection of inferior myocardial infarction. The Physikalisch Technische Bundesanstalt diagnosis public ECG database was used for verification. The accuracy, sensitivity and specificity of the model reached 99.95%, 100% and 99.90%, respectively. The accuracy, sensitivity and specificity of the model are also over 99% even though the noise exists. Based on the results of this study, it is expected that the method can be introduced in the clinical environment to help doctors quickly diagnose inferior myocardial infarction in the future.
下壁心肌梗死是一种死亡率高的急性缺血性心脏病，易引发心律失常、心力衰竭和心源性休克等危及生命的并发症。因此，对下壁心肌梗死进行准确、高效的早期诊断具有重要的临床价值。心电图是早期诊断下壁心肌梗死最敏感的手段。本文提出了一种基于密集连接卷积神经网络的下壁心肌梗死检测方法。该方法将连续连接的Ⅱ、Ⅲ和aVF导联的原始心电图（ECG）信号作为模型的输入，并利用卷积层的尺度不变性提取ECG信号的鲁棒特征。不同层之间的密集连接增强了ECG信号的特征传递，使网络能够自动学习具有强鲁棒性和高识别度的有效特征，从而实现对下壁心肌梗死的准确检测。使用德国物理技术研究院诊断公开ECG数据库进行验证。模型的准确率、灵敏度和特异性分别达到99.95%、100%和99.90%。即使存在噪声，模型的准确率、灵敏度和特异性也均超过99%。基于本研究结果，有望在临床环境中引入该方法，以帮助医生在未来快速诊断下壁心肌梗死。
