This paper presents a novel method for myocardial infarction (MI) detection using lead II of electrocardiogram (ECG). Under our proposed method, we first clean the noisy ECG signals using db4 wavelet, followed by an R-peak detection algorithm to segment the ECG signals into beats. We then translate the ECG timeseries dataset to an equivalent dataset of gray-scale images using Gramian Angular Summation Field (GASF) and Gramian Angular Difference Field (GADF) operations. Subsequently, the gray-scale images are fed into a custom two-dimensional convolutional neural network (2D-CNN) which efficiently differentiates the ECG beats of the healthy subjects from the ECG beats of the subjects with MI. We train and test the performance of our proposed method on a public dataset, namely, Physikalisch Technische Bundesanstalt (PTB) ECG dataset from Physionet. Our proposed approach achieves an average classification accuracy of 99.68\%, 99.80\%, 99.82\%, and 99.84\% under GASF dataset with noise and baseline wander, GADF dataset with noise and baseline wander, GASF dataset with noise and baseline wander removed, and GADF dataset with noise and baseline wander removed, respectively. Our proposed method is able to cope with additive noise and baseline wander, and does not require handcrafted features by a domain expert. Most importantly, this work opens the floor for innovation in wearable devices (e.g., smart watches, wrist bands etc.) to do accurate, real-time and early MI detection using a single-lead (lead II) ECG.

翻译：本文提出了一种基于心电图（ECG）导联II进行心肌梗死（MI）检测的新方法。首先采用db4小波对含噪心电信号进行去噪处理，随后通过R波检测算法将心电信号分割为单个心动周期。接着利用格拉姆角和场（GASF）与格拉姆角差场（GADF）变换，将心电时间序列数据集转换为等效的灰度图像数据集。随后将灰度图像输入定制的二维卷积神经网络（2D-CNN），该网络能够高效区分健康受试者与心肌梗死患者的心电心动周期。我们在公开数据集——即Physionet平台的德国联邦物理技术研究院（PTB）心电数据库上对所提方法进行训练与性能测试。实验结果表明，在含噪声与基线漂移的GASF数据集、含噪声与基线漂移的GADF数据集、去除噪声与基线漂移的GASF数据集以及去除噪声与基线漂移的GADF数据集上，所提方法平均分类准确率分别达到99.68%、99.80%、99.82%和99.84%。该方法能够有效处理加性噪声和基线漂移，且无需领域专家手工设计特征。更重要的是，本研究为可穿戴设备（如智能手表、腕带等）利用单导联（导联II）心电信号实现精准、实时的早期心肌梗死检测开拓了创新空间。