Detection of Atrial Fibrillation using a Feedforward Sequential Model

Abstract

Atrial Fibrillation (AFib) and its associated symptoms are significant problems that doctors and several studies have attempted to solve throughout the years. It is diagnosed by analyzing a patient’s electrocardiogram (ECG) data. However, continuous efforts have been made to develop an algorithm that detects AFib with optimal efficiency and cost-effectiveness. In this study, a sequential model was used based on feedforward neural network as this is arguably the simplest algorithm developed and requires minimal computing power. The results showed that training the algorithm for 1000 epochs yielded the best results. Further studies showed that using a combination of 10-fold cross-validation and blindfold validation proved an ideal way to determine the model's capabilities in distinguishing patients with AFib from those without. In conclusion, the developed model successfully distinguished between AFib and non-AFib patients with a 96.67% sensitivity, 94.61% specificity, and 95.64% accuracy.

Keywords

• atrial fibrillation
• ecg
• feedforward neural network
• sequential method