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首页> 外文会议>World Congress on Medical Physics and Biomedical Engineering >Atrial Fibrillation Detection from Wrist Photoplethysmography Data Using Artificial Neural Networks
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Atrial Fibrillation Detection from Wrist Photoplethysmography Data Using Artificial Neural Networks

机译：使用人工神经网络从手腕光学质谱数据中的心房颤动检测

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Atrial fibrillation (AF) can be detected by analysis of the rhythm of heartbeats. The development of photoplethysmography (PPG) technology has enabled comfortable and unobtrusive physiological monitoring of heart rate with a wrist-worn device. Therefore, it is important to examine the possibility of using PPG signal to detect AF episodes in real-world situations. The aim of this paper is to evaluate an AF detection method based on artificial neural networks (ANN) from PPG-derived beat-to-beat interval data used for primary screening or monitoring purposes. The proposed classifier is able to distinguish between AF and sinus rhythms (SR). In total 30 patients (15 with AF, 15 with SR, mean age 71.5 years) with multiple comorbidities were monitored during routine postoperative treatment. The monitoring included standard ECG and a wrist-worn PPG monitor with green and infrared light sources. The input features of the ANN are based on the information obtained from inter-beat interval (DBI) sequences of 30 consecutive PPG pulses. One of the main concerns about the PPG signals is their susceptibility to be corrupted by noise and artifacts mostly caused by subject movement. Therefore, in the proposed method the IBI reliability is automatically evaluated beforehand. The amount of uncertainty due to unreliable beats was 15.42%. The achieved sensitivity and specificity of AF detection for 30 beats sequences were 99.20 ± 1.3% and 99.54 ± 0.64%, respectively. Based on these results, the ANN algorithm demonstrated excellent performance at recognizing AF from SR using wrist PPG data.
机译：通过分析心跳节奏，可以检测心房颤动（AF）。光学仪描绘（PPG）技术的发展使心脏速率的心率舒适和不显眼的生理监测。因此，重要的是检查使用PPG信号以检测现实世界情况中的AF集的可能性。本文的目的是评估基于人工神经网络（ANN）的AF检测方法，从用于初级筛选或监测目的的PPG导出的节拍间隔数据。所提出的分类器能够区分AF和窦性心弦乐（SR）。在常规术后治疗期间监测有多种患者30名患者（15例，15例，平均71.5岁），在常规治疗期间监测多种同种植体。该监控包括标准的ECG和带有绿色和红外光源的腕部PPG监视器。 ANN的输入特征基于从短节拍间隔（DBI）序列的30个连续的PPG脉冲获得的信息。关于PPG信号的主要担忧之一是它们易受噪声和伪影损坏的敏感性，并且伪像主要由受试者移动引起。因此，在所提出的方法中，预先自动评估IBI可靠性。由于不可靠节奏而导致的不确定性的数量为15.42％。为30次击败序列的AF检测的敏感性和特异性分别为99.20±1.3％和99.54±0.64％。基于这些结果，ANN算法使用手腕PPG数据识别来自SR的优异性能。

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