Background

Atrial fibrillation (AF) is a common arrhythmia that is strongly related to the risk of stroke. Some methods in the literature approach AF diagnosis based on cardiovascular signals of several minutes in length. However, many traditional methods utilized to monitor health status in terms of AF rely on electrocardiograms, which are time consuming and require specialized equipment to collect. By contrast, more practical systems focus on noninvasively collected short-term cardiovascular signals, such as arterial pulse waveforms (APWs).

Methods

In this paper, an AF diagnosis algorithm based on the processing of parameters extracted from short-length heart period (HP) measures is proposed. The HP is obtained by locating foot points (FPOs) in 10-second epochs of APW signals. The algorithm consists of two main stages. First, five parameters representative of the APW morphology are extracted to train an artificial neural network (ANN) model for FPO detection. The moving interpolation difference method and an improved second derivative maximum method are employed for APW parameter extraction. Second, 13 temporal-domain, frequency-domain and nonlinear HP parameters are extracted from the previously identified FPOs. These are subsequently orthogonalized using principal component analysis to train a second ANN for effective AF diagnosis.

Results

Both ANNs were trained and validated on a labeled data set with 20-fold cross-validation, achieving a mean sensitivity and specificity of 97.53% and 90.13%, respectively, for AF diagnosis and an F1 score of 99.18% for FPO identification.

Conclusions

This paper presents a validated solution for the diagnosis of AF from APW records using parameters derived from HP measures. In addition, compared to that of a commercial BP+ device, improved FPO detection performance is achieved, making the proposed algorithm a strong candidate for the automatic detection of FPOs in oscillometric devices.

中文翻译：

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基于使用人工神经网络的动脉脉搏波足点检测的房颤诊断。

背景

心房颤动（AF）是一种常见的心律失常，与中风的风险密切相关。文献中的一些方法基于几分钟的心血管信号来进行房颤诊断。但是，许多用于监测AF状况的传统方法依赖于心电图，这很耗时，需要专门的设备来收集。相比之下，更实用的系统专注于非侵入式收集的短期心血管信号，例如动脉脉搏波形（APW）。

方法

本文提出了一种基于短时心动周期（HP）测量值提取参数的AF诊断算法。通过在APW信号的10秒内定位脚点（FPO）来获得HP。该算法包括两个主要阶段。首先，提取代表APW形态的五个参数，以训练用于FPO检测的人工神经网络（ANN）模型。移动插值差法和改进的二阶导数最大值法被用于APW参数提取。其次，从先前确定的FPO中提取13个时域，频域和非线性HP参数。随后使用主成分分析将其正交，以训练第二个ANN以进行有效的AF诊断。

结果

两种人工神经网络都在经过标记的数据集上进行了20倍交叉验证的训练和验证，对AF诊断的平均敏感性和特异性分别为97.53％和90.13％，对于FPO鉴定的F1评分为99.18％。

结论

本文提出了一种经过验证的解决方案，可以使用从HP量度得出的参数从APW记录诊断AF。此外，与商用BP +设备相比，FPO的检测性能得到了改善，使该算法成为自动检测示波设备中FPO的强大候选者。