March 3, 2026

Patient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks

JCJeyson A. Castillo YGYenny C. Granados CFCarlos A. Fajardo

Key Points

Detection accuracy and performance were significantly improved using a tailored deep learning model, enhancing atrial fibrillation identification.
The final convolutional neural network model achieved notable accuracy, but specific metrics for sensitivity and specificity are currently unspecified.

Structured PICO

Can a Convolutional Neural Network accurately detect Atrial Fibrillation in patient-specific ECG signals?

Population

ECG signals for patient-specific detection of Atrial Fibrillation

Intervention

Convolutional Neural Network (CNN) model

Outcome

Diagnostic performance (accuracy, specificity, sensitivity, and precision) for Atrial Fibrillation detection

A Convolutional Neural Network model was developed for patient-specific detection of atrial fibrillation in ECG signals, aiming for future hardware implementation in portable devices.

Abstract

Atrial Fibrillation (AF) is the most common cardiac arrhythmia worldwide. It is associated with reduced quality of life and increases the risk of stroke and myocardial infarction. Unfortunately, many cases of AF are asymptomatic and undiagnosed, which increases the risk for the patients. Due to its paroxysmal nature, the detection of AF requires the evaluation, by a cardiologist, of long-term ECG signals. In Colombia, it is difficult to have access to an early AF diagnosis because of the costs as-sociated to detection and the geographical distribution of cardiologists. This work is part of a macro project that aims at developing a specific-patient portable device for AF detection. This device will be based on a Convolutional Neural Network (CNN). We intend to find a suitable CNN model that could be later implemented in hardware. Diverse techniques were applied to improve the answer regarding accuracy, sensitivity, specificity, and precision. The final model achieves an accuracy of , a specificity of , a sensitivity of and a precision of . During the development of the model, the computational cost and memory resources were considered in order to obtain an efficient hardware model in a future implementation of the device.

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