Biomedical signals analysis plays a crucial role in diagnosing and monitoring various medical conditions. In recent years, convolutional neural networks (CNNs) have gained significant attention for their potential in analyzing biomedical signals due to their ability to capture spatial dependencies and extract relevant features automatically. This article presents a comprehensive review of the application of CNNs in biomedical signals analysis. The review covers the utilization of CNNs in electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), and other biomedical signal types. It discusses the various architectures, training techniques, and performance evaluation methods employed in these applications. Furthermore, the article explores the challenges and future directions in the field of CNN-based biomedical signals analysis.

Convolutional Neural Networks, Biomedical Signals Analysis, Electrocardiogram

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