Ahmad Mostafa, Ahmed Ali, Hashim Hasan, Farsi Mohammed, Mahmoud Nader
Computer Science Department, Faculty of Computers and Information, Menoufia University, Shibin el Kom, Menofia Governorate 6131567, Egypt.
Department of Computer Science and AI, College of Computing and Information Technology, University of Bisha, Bisha 67714, Saudi Arabia.
Diagnostics (Basel). 2025 Jun 13;15(12):1501. doi: 10.3390/diagnostics15121501.
Accurate and efficient diagnosis of heart disease through electrocardiogram (ECG) analysis remains a critical challenge in clinical practice due to noise interference, morphological variability, and the complexity of overlapping cardiac signals. : This study presents a comprehensive deep learning (DL) framework that integrates advanced ECG signal segmentation with transfer learning-based classification, aimed at improving diagnostic performance. The proposed ECG segmentation algorithm introduces a distinct and original approach compared to prior research by integrating adaptive preprocessing, histogram-based lead separation, and robust point-tracking techniques into a unified framework. While most earlier studies have addressed ECG image processing using basic filtering, fixed-region cropping, or template matching, our method uniquely focuses on automated and precise reconstruction of individual ECG leads from noisy and overlapping multi-lead images-a challenge often overlooked in previous work. This innovative segmentation strategy significantly enhances signal clarity and enables the extraction of richer and more localized features, boosting the performance of DL classifiers. The dataset utilized in this work of 12 lead-based standard ECG images consists of four primary classes. : Experiments conducted using various DL models-such as VGG16, VGG19, ResNet50, InceptionNetV2, and GoogleNet-reveal that segmentation notably enhances model performance in terms of recall, precision, and F1 score. The hybrid VGG19 + SVM model achieved 98.01% and 100% accuracy in multi-class classification, along with average accuracies of 99% and 97.95% in binary classification tasks using the original and reconstructed datasets, respectively. : The results highlight the superiority of deep, feature-rich models in handling reconstructed ECG signals and confirm the value of segmentation as a critical preprocessing step. These findings underscore the importance of effective ECG segmentation in DL applications for automated heart disease diagnosis, offering a more reliable and accurate solution.
由于噪声干扰、形态变异以及心脏信号重叠的复杂性，通过心电图（ECG）分析准确有效地诊断心脏病仍然是临床实践中的一项关键挑战。本研究提出了一个全面的深度学习（DL）框架，该框架将先进的ECG信号分割与基于迁移学习的分类相结合，旨在提高诊断性能。与先前的研究相比，所提出的ECG分割算法引入了一种独特的原创方法，即将自适应预处理、基于直方图的导联分离和稳健的点跟踪技术集成到一个统一的框架中。虽然大多数早期研究使用基本滤波、固定区域裁剪或模板匹配来处理ECG图像处理，但我们的方法独特地专注于从嘈杂和重叠的多导联图像中自动精确重建单个ECG导联——这是先前工作中经常被忽视的一个挑战。这种创新的分割策略显著提高了信号清晰度，并能够提取更丰富、更局部化的特征，从而提高了DL分类器的性能。本研究中使用的基于12导联标准ECG图像的数据集由四个主要类别组成。使用各种DL模型（如VGG16、VGG19、ResNet50、InceptionNetV2和GoogleNet）进行的实验表明，分割在召回率、精确率和F1分数方面显著提高了模型性能。混合VGG19 + SVM模型在多类分类中达到了98.01%和100%的准确率，在使用原始数据集和重建数据集的二分类任务中平均准确率分别为99%和97.95%。结果突出了深度、特征丰富的模型在处理重建ECG信号方面的优越性，并证实了分割作为关键预处理步骤的价值。这些发现强调了有效ECG分割在DL应用于自动心脏病诊断中的重要性，提供了一种更可靠、准确的解决方案。
