Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented a method based on a deep convolutional neural network (CNN) for the detection of MI automatically. The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals. In the case of imbalanced data, we optimize our deep model with a new loss function named the focal loss to deal with this case by constituting the loss indirectly the focus in those difficult classes. The Physikalisch-Technische Bundesanstalt (PTB) dataset was employed in the validation to classify the signals to normal and MI. The performance of our technique alongside state-of-the-art in the area shows an increase in terms of average accuracy and F1 score. Results show that focal loss improves the detection accuracy by 9% for detecting MI signals. In summary, the proposed method achieved an overall accuracy, precision, F1 score, and recall of 98.84%, 98.31%, 97.92%, and 97.63, respectively using focal loss and overall accuracy of 89.72%, a precision of 88.52%, a recall of 81.11% and F1 score of 83.02% without using focal loss. Our method using focal loss is an effective tool to perform a fast and reliable MI diagnosis to assist the cardiologists in detecting MI early.

心肌梗塞（MI）是急性流向心脏的血液，这会使心脏遭受血液不足和局部缺血的困扰，从而使心肌受损，细胞可能死亡并失去功能。尽管世界范围内MI的发病率较低，但它仍然是常见的致病性死亡。因此，及早发现MI信号可以降低死亡率。本文提出了一种基于深度卷积神经网络（CNN）的MI自动检测方法。提出的CNN是一种端到端模型，不需要任何阶段的机器学习，并且只需要一个阶段即可从输入信号中检测MI。在数据不平衡的情况下，我们使用称为损失的新损失函数优化深度模型，以通过在这些困难类别中间接构成损失来应对这种情况。验证中使用了物理技术联合会（PTB）数据集将信号分类为正常信号和心梗信号。我们的技术在该地区的技术水平与最新水平相比，表现出平均准确性和F1得分的提高。结果表明，聚焦损失将检测MI信号的检测精度提高了9％。综上所述，所提出的方法使用散焦损失和89.72％的整体准确度，88.52％的查全率，分别实现了98.84％，98.31％，97.92％和97.63的整体准确度，准确性，F1得分和查全率不使用散焦的情况下，得分为81.11％，F1得分为83.02％。我们使用病灶损失的方法是执行快速可靠的心梗诊断的有效工具，可帮助心脏病医生及早发现心梗。