Goktekin Mehmet Cagri, Gul Evrim, Çakmak Tolga, Demir Fatih, Kobat Mehmet Ali, Akbulut Yaman, Işık Ömer, Kadiroğlu Zehra, Demir Kürşat, Şengür Abdulkadir
Emergency Medicine Department, School of Medicine, Firat University, Elazig 23119, Turkey.
Clinics of Cardiology, Balıkesir Atatürk City Hospital, Balıkesir 10050, Turkey.
Diagnostics (Basel). 2025 Feb 2;15(3):347. doi: 10.3390/diagnostics15030347.
Heart attacks are the leading cause of death in the world. There are two important classes of heart attack: ST-segment Elevation Myocardial Infarction (STEMI) and Non-ST-segment Elevation Myocardial Infarction (NSTEMI) patient groups. While the STEMI group has a higher mortality rate in the short term, the NSTEMI group is considered more dangerous and insidious in the long term. Blocked coronary arteries can be predicted from ECG signals in STEMI patients but not in NSTEMI patients. Therefore, coronary angiography (CAG) is inevitable for these patients. However, in the elderly and some patients with chronic diseases, if there is a single blockage, the CAG procedure poses a risk, so medication may be preferred. In this study, a novel deep learning-based approach is used to automatically detect the occluded main coronary artery or arteries in NSTEMI patients. For this purpose, a new seven-class dataset was created with expert cardiologists. : A new Multi Input-Multi Scale (MI-MS) ConvMixer model was developed for automatic detection. The MI-MS ConvMixer model allows simultaneous training of 12-channel ECG data and highlights different regions of the data at different scales. In addition, the ConMixer structure provides high classification performance without increasing the complexity of the model. Moreover, to maximise the classifier performance, the WSSE algorithm was developed to adjust the classification prediction value according to the feature importance weights. This algorithm improves the SVM classifier performance. The features extracted from this model were classified with the WSSE algorithm, and an accuracy of 88.72% was achieved. : This study demonstrates the potential of the MI-MS ConvMixer model in advancing ECG signal classification for diagnosing coronary artery diseases, offering a promising tool for real-time, automated analysis in clinical settings. The findings highlight the model's ability to achieve high sensitivity, specificity, and precision, which could significantly improve.
心脏病发作是全球主要的死因。心脏病发作主要分为两类：ST段抬高型心肌梗死（STEMI）和非ST段抬高型心肌梗死（NSTEMI）患者群体。虽然STEMI组在短期内死亡率较高，但NSTEMI组从长期来看被认为更危险且隐匿。STEMI患者的心电图信号可以预测冠状动脉阻塞情况，但NSTEMI患者则不行。因此，这些患者不可避免地需要进行冠状动脉造影（CAG）。然而，对于老年人和一些患有慢性病的患者，如果存在单一阻塞，CAG操作存在风险，所以可能更倾向于药物治疗。在本研究中，采用了一种基于深度学习的新方法来自动检测NSTEMI患者中阻塞的主要冠状动脉。为此，与心脏病专家共同创建了一个新的七类数据集。开发了一种新的多输入多尺度（MI-MS）ConvMixer模型用于自动检测。MI-MS ConvMixer模型允许同时训练12通道心电图数据，并在不同尺度上突出显示数据的不同区域。此外，ConvMixer结构在不增加模型复杂度的情况下提供了较高的分类性能。而且，为了最大化分类器性能，开发了WSSE算法根据特征重要性权重调整分类预测值。该算法提高了支持向量机分类器的性能。用WSSE算法对从该模型提取的特征进行分类，准确率达到了88.72%。本研究证明了MI-MS ConvMixer模型在推进用于诊断冠状动脉疾病的心电图信号分类方面的潜力，为临床环境中的实时自动分析提供了一个有前景的工具。研究结果突出了该模型实现高灵敏度、特异性和精度的能力，这可能会显著提高。
