Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, 600119 Tamil Nadu, India.
Department of Computer Science and Engineering, KIoT-College of Informatics, Kombolcha, Wollo University, Ethiopia.
Biomed Res Int. 2022 Jul 4;2022:5203401. doi: 10.1155/2022/5203401. eCollection 2022.
Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart's pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG's massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.
心律失常是人们生活中偶尔出现的心跳节律异常。这些心律失常可能会导致潜在的致命后果，使你的生命处于危险之中。因此，心律失常的识别和分类是心脏诊断的一个重要方面。心电图（ECG）是一种收集心脏跳动活动的记录，被认为是捕捉这些异常事件的指南。然而，由于心电图包含大量的数据，从图像评估中提取关键数据变得非常困难。因此，创建一个有效的系统来分析 ECG 的大量数据至关重要。从 ECG 信号处理 ECG 图像。为了优化识别和分类过程，本研究提出了一种基于混合深度学习的技术。本文在两个方面做出了贡献。首先，通过一些图像处理技术，将一维 ECG 数据转换为二维图像，从而实现噪声的自动降低和特征的提取。然后，根据实验证据，提出了一种称为 CNNLSTM 的混合模型，它结合了 CNN 和 LSTM 模型。我们使用广泛使用的 MIT_BIH 心律失常数据集进行了全面的研究，以评估所提出的 CNN-LSTM 技术的效果。结果表明，所提出的方法具有 99.10%的准确率。此外，所提出的模型的平均灵敏度为 98.35%，特异性为 98.38%。这些结果优于其他方法，并且将大大减少医生的参与量。
