A novel deep neural network for detection of Atrial Fibrillation using ECG signals

In healthcare practice, one of the most predominantly occurring dysrhythmia is atrial fibrillation (AF). The anomalous heart rhythm and the deficiency of an evident P-wave signal are the consequences of AF, several cerebral apoplexies, thrombus, blood coagulation, cognitive impairments, and strokes. It is arduous to ascertain the symptoms of AF and clinically silent that might cause death. There are certain liabilities for diagnosis of AF in manual Electrocardiogram (ECG) since it demands high expertise; it is a time demanding and tedious process which is also accompanied by variations between intra-and inter-observer. Hence, to combat with this issue, a novel AF detection models has been proposed a conglomerate parallel structure of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) framework which can deepen the understanding of features and classification. To test and legitimize the system, we utilize the data of the MIT-BIH Atrial Fibrillation Database. Parallel models for control subjects have been designed specifically to validate performance in terms of classification for more voracious categorization of 3 classes namely: Non-Atrial Fibrillation (N-AF), Atrial Fibrillation (AF) and Normal Sinus Rhythm (NSR). The model obtained an Accuracy, Sensitivity and Specificity of 99.6%, 98.64% and 99.01% respectively.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

Atrial fibrillation is a common dysrhythmia in healthcare practice with serious consequences. Traditional diagnosis methods have limitations, leading to the need for novel detection models to improve accuracy and efficiency in diagnosis.