Musa Nehemiah, Gital Abdulsalam Ya'u, Aljojo Nahla, Chiroma Haruna, Adewole Kayode S, Mojeed Hammed A, Faruk Nasir, Abdulkarim Abubakar, Emmanuel Ifada, Folawiyo Yusuf Y, Ogunmodede James A, Oloyede Abdukareem A, Olawoyin Lukman A, Sikiru Ismaeel A, Katb Ibrahim
Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria.
University of Jeddah, Jeddah, Saudi Arabia.
J Ambient Intell Humaniz Comput. 2023;14(7):9677-9750. doi: 10.1007/s12652-022-03868-z. Epub 2022 Jul 7.
The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.
The online version contains supplementary material available at 10.1007/s12652-022-03868-z.
深度学习在处理诸如图像处理、计算机视觉、目标检测、语音识别、医学成像等人工智能应用任务方面优于传统机器学习技术，这使得深度学习成为主导人工智能应用的热门词汇。在过去十年中，深度学习在诸如心电图（ECG）等生理信号中的应用吸引了大量研究。然而，以往的综述未能就深度学习在心电图应用的领域，提供包括基于生物特征心电图系统在内的系统全面综述。为弥补这一差距，我们对深度学习在心电图中的应用进行了系统的文献综述，包括基于生物特征心电图的系统。该研究系统分析了150项关于深度学习在心电图中应用的初步研究。研究表明，深度学习在心电图中的应用已涉及不同领域。我们提出了一种深度学习在心电图应用领域的新分类法。本文还讨论了基于生物特征心电图的系统以及基于领域、区域、任务、深度学习模型、数据集来源和预处理方法的研究的元数据分析。强调了挑战和潜在的研究机会以推动新的研究。我们相信这项研究将对那些试图在利用深度学习算法进行心电图信号处理的现有知识体系中增加知识的新研究人员和专家研究人员都有用。
在线版本包含可在10.1007/s12652-022-03868-z获取的补充材料。
