基于深度卷积神经网络的心电图像心脏病分类与诊断

Thanu Kurian, T. S
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引用次数: 0

摘要

如果在早期阶段正确识别心血管疾病,可以减少对患者的严重后果,包括死亡。心电检查是检测心脏病最好的诊断工具之一。使用与ECG相关的信号数据训练的模型很难在实际的医疗场景中实现。提出了一种利用12导联心电图图像诊断心肌梗死、心跳异常、心肌梗死史和心跳正常等心脏状况的CNN模型。使用智能手机扫描图像即可拍摄心电图像。这对没有专家进行诊断的小型医疗中心非常有帮助。该模型训练效率高,准确率达99%,使用性能优越的移动设备扫描心电图像诊断心脏状况。该工作还比较了模型与ResNet和EfficientNet-B0等预训练模型在相同ECG图像数据集上的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Convolution Neural Network-Based Classification and Diagnosis of Heart Disease using ElectroCardioGram (ECG) Images
A cardiovascular disease, if identified correctly at an early stage, could reduce the critical consequences in patients , including fatality. One of the best diagnostic tool for detecting heart disease is through an ECG test. Models trained using signal data related to ECG is difficult to be implemented in an actual healthcare scenario. A CNN model is proposed which makes use of 12-lead ECG images to diagnose cardiac conditions such as myocardial infarction, abnormal heart beat, history of myocardial infarction and normal heartbeat. The ECG image can be taken by scanning the image using a smart phone. This would be very helpful in small healthcare centers where there are no experts for diagnosis. The proposed model was efficiently trained with an accuracy of 99% and cardiac condition was diagnosed using ECG images scanned using a mobile with a superior performance. The work also compares the performance of model with pretrained models as ResNet and EfficientNet-B0 for the same ECG image dataset.
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