Atrial Fibrillation Detection Using Machine Learning

TL;DR

The paper tackles non-invasive atrial fibrillation detection in ambulatory settings by fusing synchronized ECG and PPG signals and engineering 22 discriminative features. It compares three classifiers—Bagged Decision Trees, Cubic SVM, and Subspace KNN—using 10-fold cross-validation and a held-out test, reporting near 99% accuracy with ensemble methods and robust sensitivity/specificity. The Bagged Trees approach emerges as the top performer, validating the effectiveness of multimodal, feature-based detection in wearable contexts. The work demonstrates the feasibility of high-accuracy AF screening using affordable, non-invasive signals and highlights the need for larger, more diverse datasets and real-time deployment strategies for real-world use.

Abstract

Atrial fibrillation (AF) is a common cardiac arrhythmia and a major risk factor for ischemic stroke. Early detection of AF using non-invasive signals can enable timely intervention. In this work, we present a comprehensive machine learning framework for AF detection from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals. We partitioned continuous recordings from 35 subjects into 525 segments (15 segments of 10,000 samples each at 125Hz per subject). After data cleaning to remove segments with missing samples, 481 segments remained (263 AF, 218 normal). We extracted 22 features per segment, including time-domain statistics (mean, standard deviation, skewness, etc.), bandpower, and heart-rate variability metrics from both PPG and ECG signals. Three classifiers -- ensemble of bagged decision trees, cubic-kernel support vector machine (SVM), and subspace k-nearest neighbors (KNN) -- were trained and evaluated using 10-fold cross-validation and hold-out testing. The subspace KNN achieved the highest test accuracy (98.7\%), slightly outperforming bagged trees (97.9\%) and cubic SVM (97.1\%). Sensitivity (AF detection) and specificity (normal rhythm detection) were all above 95\% for the top-performing models. The results indicate that ensemble-based machine learning models using combined PPG and ECG features can effectively detect atrial fibrillation. A comparative analysis of model performance along with strengths and limitations of the proposed framework is presented.

Figures (7)

• Figure 1: Denoising
• Figure 2: Baseline Drift Removal
• Figure 3: Normalization
• Figure 4: Heatmap of extracted features from PPG and ECG segments showing inter-feature correlation patterns
• Figure 5: Confusion matrix for the Bagged Trees classifier.