School of Computer Science, National College of Business Administration and Economics, Lahore 54000, Pakistan.
Department of Software, Gachon University, Seongnam 13120, Republic of Korea.
Comput Intell Neurosci. 2022 Oct 7;2022:5054641. doi: 10.1155/2022/5054641. eCollection 2022.
With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.
随着物联网（IoT）的出现，医疗保健领域对各种疾病的研究得到了改善，云计算有助于集中数据并在全球范围内访问患者记录。在这种情况下，心电图（ECG）被用于诊断心脏病或异常。机器学习技术以前曾被使用过，但基于特征，不如迁移学习准确；所提出的开发和验证嵌入式设备通过使用迁移学习（DVEEA-TL）模型来证明心电图心律失常。该模型是硬件、软件和两个数据集的结合，这些数据集经过扩充和融合，与之前的工作和研究相比，其准确率结果更高。在提出的模型中，通过结合 Kaggle 数据集和另一个数据集创建了一个新的数据集，该数据集是通过实时获取健康和不健康数据集来创建的，然后应用 AlexNet 迁移学习方法来获得更准确的心电图信号读数。在这项研究中，DVEEA-TL 模型在训练和验证阶段的心脏异常诊断准确率分别为 99.9%和 99.8%，这是该领域以前的研究中最好和更可靠的方法。
