Enhancing Electrocardiogram Irregular Heartbeat Forecasting in a CNN-Informer Framework

Heart arrhythmia is a chronic condition characterized by irregular heartbeats without early detection, which can lead to high mortality rates. To address this critical concern and enhance the prediction of arrhythmia onset and its prevention, this paper proposes a CNN-Informer model, a deep learning approach, for long-term time series forecasting of electrocardiogram (ECG) signals. The model achieves a long-term prediction with a remarkable mean square error (MSE) of 0.8358, a short-term prediction MSE of 0.3712 on the database from Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology (MIT-BIH Arrhythmia Database) and an MSE of 0.6843 on the 12-lead ECG arrhythmia database under the auspices of Chapman University and Shaoxing People’s Hospital. These results show that the CNN-Informer model is promising for time series forecasting and computer-assisted diagnosis in clinical settings and holds the potential for improvements in patient care and management.