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.

翻译：心房颤动（AF）是一种常见的心律失常，也是缺血性中风的主要风险因素。利用无创信号早期检测房颤可实现及时干预。本研究提出了一种基于光电容积脉搏波（PPG）与心电图（ECG）同步信号的综合性机器学习房颤检测框架。我们将35名受试者的连续记录划分为525个片段（每名受试者15个片段，每个片段包含125Hz采样率下的10,000个样本）。经过数据清洗去除存在样本缺失的片段后，剩余481个片段（263个房颤片段，218个正常片段）。我们从PPG和ECG信号中为每个片段提取了22个特征，包括时域统计量（均值、标准差、偏度等）、频带功率以及心率变异性指标。采用装袋决策树集成、立方核支持向量机（SVM）和子空间k近邻（KNN）三种分类器，通过10折交叉验证与保留测试进行训练与评估。子空间KNN取得了最高的测试准确率（98.7%），略优于装袋决策树（97.9%）和立方SVM（97.1%）。最优模型的灵敏度（房颤检测）与特异度（正常节律检测）均超过95%。结果表明，基于PPG与ECG融合特征的集成机器学习模型能有效检测心房颤动。本文进一步对模型性能进行了对比分析，并讨论了所提框架的优势与局限性。