ECG classification is a key technology in intelligent ECG monitoring. In the past, traditional machine learning methods such as SVM and KNN have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for the ECG classification and shows high classification accuracy. However, the end-to-end neural network has large computational complexity including a large number of parameters and operations. Although dedicated hardware such as FPGA and ASIC can be developed to accelerate the neural network, they result in large power consumption, large design cost, or limited flexibility. In this work, we have proposed an ultra-lightweight end-to-end ECG classification neural network which has extremely low computational complexity (~8.2k parameters & ~227k MUL/ADD operations) and can be squeezed into a low-cost MCU (i.e. microcontroller) while achieving 99.1% overall classification accuracy. This outperforms the state-of-the-art ECG classification neural network. Implemented on a low-cost MCU (i.e. MSP432), the proposed design consumes only 0.4 mJ and 3.1 mJ per heartbeat classification for normal and abnormal heartbeats respectively for real-time ECG classification.
ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network https://www.embs.org/jbhi/wp-content/uploads/sites/18/2021/12/1..jpg 486 237 Journal of Biomedical and Health Informatics (JBHI) //www.embs.org/jbhi/wp-content/uploads/sites/18/2022/06/ieee-jbhi-logo2x.png