In this study, a novel Atrial Fibrillation (AFib) detection algorithm is presented based on Electrocardiography (ECG) signals. In particular, the spectrogram of ECG signal is used as an input to a Convolutional Neural Network (CNN) to classify normal and AFib ECG signals. This model is shown to perform well with an accuracy of 92.91% and a value of 0.9789 for the area under the ROC curve (AUC). This study demonstrated the potential of using image classification methods and CNN model to detect abnormal biosignals with noise
