使用在真实生活场景中采集的智能手表光电容积脉搏波信号进行多类别心律失常分类
Multiclass Arrhythmia Classification using Smartwatch Photoplethysmography Signals Collected in Real-life Settings
AI总结：
本文提出一种基于智能手表多模态数据（PPG、加速度计、心率）的1D-Bi-GRU模型，在真实场景数据上将PAC/PVC检测灵敏度提升至83%，同时保持97.31%的AF检测准确率，且模型更轻更快。
摘要：
大多数多类别心律失常分类的深度学习模型都在指尖光电容积脉搏波（PPG）数据上进行测试，与智能手表采集的PPG相比，指尖PPG具有更高的信噪比，并且目前报道的房性/室性早搏（PAC/PVC）检测的最佳灵敏度仅为75%。为了在保持高心房颤动（AF）检测性能的同时提高PAC/PVC检测灵敏度，我们使用多模态数据，将一维PPG、加速度计和心率数据作为计算高效的一维双向门控循环单元（1D-Bi-GRU）模型的输入，以检测三类心律失常。我们使用了来自美国国立卫生研究院（NIH）资助的Pulsewatch临床试验中易受运动伪影影响的智能手表PPG数据。我们的多模态模型在72名受试者上进行了测试，实现了前所未有的83%的PAC/PVC检测灵敏度，同时保持了97.31%的AF检测高准确率。即使我们的模型计算效率更高（轻14倍、快2.7倍），这些结果在PAC/PVC检测上仍比最佳先进模型高出20.81%，在AF检测上高出2.55%。
英文摘要：
Most deep learning models of multiclass arrhythmia classification are tested on fingertip photoplethysmographic (PPG) data, which has higher signal-to-noise ratios compared to smartwatch-derived PPG, and the best reported sensitivity value for premature atrial/ventricular contraction (PAC/PVC) detection is only 75%. To improve upon PAC/PVC detection sensitivity while maintaining high AF detection, we use multi-modal data which incorporates 1D PPG, accelerometers, and heart rate data as the inputs to a computationally efficient 1D bi-directional Gated Recurrent Unit (1D-Bi-GRU) model to detect three arrhythmia classes. We used motion-artifact prone smartwatch PPG data from the NIH-funded Pulsewatch clinical trial. Our multimodal model tested on 72 subjects achieved an unprecedented 83% sensitivity for PAC/PVC detection while maintaining a high accuracy of 97.31% for AF detection. These results outperformed the best state-of-the-art model by 20.81% for PAC/PVC and 2.55% for AF detection even while our model was computationally more efficient (14 times lighter and 2.7 faster).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
