The key primary aim is to provide state of the art knowledge about how deep learning evolved and revolutionized machine learning in the past few years. Second, to critically review the application of deep learning for different biomedical signals analysis and provide a holistic overview of current works of literature. Finally, to discuss the research opportunities with deep learning algorithms in the field of study that can serve as a starting point for new researchers to identify the future research direction in a concise manner. | Journal of Computer Science and Information Technology December 2018, Vol. 6, No. 2, pp. 45-59 ISSN 2334-2366 (Print) 2334-2374 (Online) Copyright © The Author(s). All Rights Reserved. Published by American Research Institute for Policy Development DOI: URL: Convolutional Neural Network Application in Biomedical Signals Haya Alaskar1 Abstract Recent improvements in big data and machine learning have enhanced the importance of biomedical signal and image-processing research. One part of machine learning evolution is deep learning networks. Deep learning networks are designed for the task of exploiting compositional structure in data. The golden age of the deep learning network in particular convolutional neural networks (CNNs) began in 2012. CNNs have rapidly become a methodology of optimal choice for analysing biomedical signals. CNNs have been successful in detecting and diagnosing an abnormality in biomedical signals. This paper has three distinct aims. The key primary aim is to provide state of the art knowledge about how deep learning evolved and revolutionized machine learning in the past few years. Second, to critically review the application of deep learning for different biomedical signals analysis and provide a holistic overview of current works of literature. Finally, to discuss the research opportunities with deep learning algorithms in the field of study that can serve as a starting point for new researchers to identify the future research direction in a concise manner. Keywords: Convolutional neural network; Biomedical signals; Deep learning; Diagnosis. 1. Introduction Once it was possible to record and load signals into a machine, researchers focused their efforts on designing systems for automated analysis. Initially, the electrocardiograph (electrocardiogram) in 1902 represent important information about the structure and function of the heart (AlGhatrif and Lindsay, 2012). Thirty-five years .
