Precise and efficient heartbeat classification using a novel lightweight-modified method

Yunqing Liu, Yanrui Jin, Jinlei Liu, Chengjin Qin, Ke Lin, Haotian Shi, Jianfeng Tao, Liqun Zhao, Chengliang Liu

Abstract: Highlights•We propose a novel lightweight-modified two-dimensional neural network for heartbeat classification.•It takes the modified VGGNet network as the backbone and contains inverted residual with linear bottleneck module.•Compared with the existing methods, it can reduce the computational complexity and improve classification efficiency.•It could effectively ensure the accuracy of heartbeat classification.•It would be helpful for the application of original two-dimensional ECG in computer-aided diagnosis.