Department of Health Management and Information Technology, College of Applied Medical Sciences, King Faisal University, Al-Ahsa, 36362, Saudi Arabia.
Department of Respiratory Care, College of Applied Medical Sciences, King Faisal University, Al-Ahsa, 36362, Saudi Arabia.
Sci Rep. 2024 Nov 6;14(1):26919. doi: 10.1038/s41598-024-78028-8.
Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts' models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas.
心血管疾病是导致死亡的一个原因，因此准确诊断至关重要。心电图在检测心脏病（如心脏病发作、束支传导阻滞和心律失常）方面发挥着作用。手动分析心电图容易出错且耗时，这凸显了自动化方法的重要性。本研究使用人工智能模型（如 AlexNet 和双分支模型）对来自 PTB 诊断 ECG 数据库的 ECG 信号进行分类。AlexNet 在验证集上的准确率达到了 98.64%，在测试集上的准确率达到了 99%，而双分支融合网络模型在测试集上的准确率达到了 99%。数据预处理包括对 ECG 信号进行标准化、平衡和重塑。这些模型表现出了精确性、敏感性和特异性。与 Hybrid AlexNet SVM 和 DCNN LSTM 等先进模型相比，我们提出的模型显示出了性能优势。高达 99%的准确率突显了它们在 ECG 分类方面的潜力。这些结果验证了将学习模型纳入自动化 ECG 分析设置的优势，为包括农村地区在内的各种医疗保健环境提供了适应性解决方案。
