A Novel CNN-Informer Model for Electrocardiogram Time Series Forecasting

Heart arrhythmia is a chronic condition character-ized by irregular heartbeats; without early detection, it can lead to high mortality rates. To address this critical concern and enhance the prediction of arrhythmia onset and its prevention, this paper proposes the CNN-Informer model, a deep learning approach, for long-term time series forecasting of electrocardio-gram (ECG) signals. Through rigorous evaluation, the model achieved long-term prediction on the MIT-BIH database at a remarkable accuracy of 93.9%, short-term prediction accuracies of 94.9% on the database from Beth Israel Deaconess Medical Center and the Massachusetts Institute of Technology (MIT-BIH Arrhythmia Database), and 96.4% on the 12-lead ECG arrhythmia database under the auspices of Chapman University and Shaoxing People's Hospital. The CNN-Informer model is promising for time series forecasting and computer-assisted diagnosis in clinical settings, holding potential for improvements in patient care and management.