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Abstract

The early screening and continuous monitoring of cardiovascular diseases need effective acquisition and smart processing of electrocardiogram (ECG) signals. In this article, we introduce a compact platform designed for the acquisition of low-noise ECG signals and classification of the signals using a one-dimensional convolutional neural network (1D-CNN). Our compact platform consists of a low-noise analog front-end (AFE), including an instrumentation amplifier and a chain of analog filters, along with a data acquisition component designed to ensure effective suppression of baseline wander and high frequencies. Our compact platform consumes a low amount of power and can therefore be used for continuous monitoring of cardiovascular diseases. Results obtained using our compact platform demonstrate a high level of accuracy and a low amount of power consumption of less than 50 mW. Our compact platform can be used for continuous monitoring and screening of cardiovascular diseases

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