Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

中文翻译：

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稳健的光体积描记质量评估的监督方法

早期发现心房颤动 (AFib) 对于预防中风复发至关重要。监测心律的新工具对于风险分层和中风预防很重要。由于许多长期 AFib 检测的新方法现在都基于可穿戴设备的光电容积描记 (PPG) 记录，因此确保高 PPG 信噪比是稳健检测 AFib 发作的基本要求。传统上，信号质量评估通常基于评估脉冲之间的相似性以得出信号质量指标。在存在心律失常的情况下，使用这种方法准确评估 PPG 质量存在局限性，如 AFib 的情况，主要是由于脉搏形态的显着变化。在本文中，我们首先使用 AFib 患者的 PPG 记录数据集测试了从 PPG 质量评估研究中选择的算法的性能。然后，我们提出了机器学习方法，用于对加州大学旧金山分校 (UCSF) 神经重症监护病房收治的 13 名中风患者的 30 秒 PPG 记录片段和来自 UCSF 五个普通医院之一的 3764 名患者的另一个数据集进行 PPG 质量评估重症监护病房。我们使用了从两个系统获取的数据，即来自床边监测系统的指尖 PPG (fPPG)，以及使用可穿戴商用腕带测量的径向 PPG (rPPG)。我们比较了各种有监督的机器学习技术，包括 k 最近邻、决策树和两类支持向量机 (SVM)。SVM 提供了最好的性能。