ULECGNet: An Ultra-Lightweight End-to-End ECG Classification Neural Network
Jianbiao Xiao, Jiahao Liu, Huanqi Yang, Qingsong Liu, Ning Wang, Zhen Zhu, Yulong Chen, Long Yu, Liang Chang, Liang Zhou, Jun Zhou
Abstract
ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In the past, traditional machine learning methods such as support vector machine (SVM) and K-nearest neighbor (KNN) have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for 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 field-programmable gate array (FPGA) and application-specific integrated circuit (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 that has extremely low computational complexity (∼8.2k parameters & ∼227k multiplication/addition operations) and can be squeezed into a low-cost microcontroller (MCU) such as MSP432 while achieving 99.1% overall classification accuracy. This outperforms the state-of-the-art ECG classification neural network. Implemented on 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.