ECG Signal Classification Algorithm and Circuit Design Based on Deep Learning

With the advancement of medical technology, ECG signals, as an important physiological signal, play a key role in disease diagnosis and monitoring. This paper proposes an ECG signal classification algorithm based on deep learning, combined with the corresponding circuit design, to achieve efficient and accurate ECG signal analysis. First, the characteristics of ECG signals are introduced, and ECG signals in the MIT-BIH database are extracted and divided into 8 categories. On this basis, a one-dimensional convolutional neural network is designed to achieve classification and diagnosis, so as to fully explore the features and improve the accuracy of classification. Through experiments on public data sets, the ability of the proposed algorithm in ECG signal recognition is verified when the network is fully converged, and the classification accuracy can reach 98.53 %. Then, an accuracy statistics circuit module is designed to count the classification effect of the FPGA end, and its classification accuracy can reach 95.68%. Finally, the reasons for the difference in ECG signal classification accuracy between the hardware end and the software end are analyzed. Combined with the optimization of the algorithm and circuit design, this study not only provides new ideas for the recognition and classification of ECG signals, but is also expected to play an important role in the prevention and health management of cardiovascular diseases in the future.