Abstract

Introduction

The emphasis on disease prevention, early detection, and preventive treatments will revolutionize the way sleep clinicians evaluate their patients. Obstructive Sleep Apnea (OSA) is one of the most prevalent sleep disorders with approximately 100 millions patients been diagnosed worldwide. The effectiveness of sleep disorder therapies can be enhanced by providing personalized and real-time prediction of OSA episode onsets. Previous attempts at OSA prediction are limited to capturing the nonlinear, nonstationary dynamics of the underlying physiological processes.

Methods

This paper reports an investigation into heart rate dynamics aiming to predict in real time the onsets of OSA episode before the clinical symptoms appear. The method includes (a) a representation of a transition state space network to characterize dynamic transition of apneic states (b) a Dirichlet-Process Mixture-Gaussian-Process prognostic method for estimating the distribution of the time estimate the remaining time until the onset of an impending OSA episode by considering the stochastic evolution of the normal states to an anomalous (apnea)

Results

The approach was tested using three datasets including (1) 20 records from 14 OSA subjects in benchmark ECG apnea databases (Physionet.org), (2) records of eight subjects from previous work. The average prediction accuracy (R²) is reported as 0.75%, with 87% of observations within the 95% confidence interval. Estimated risk indicators at 1 to 3 min till apnea onset are reported as 85.8 %, 80.2 %, and 75.5 %, respectively.

Conclusion

The present prognosis approach can be integrated with wearable devices to facilitate individualized treatments and timely prevention therapies.

Support

N/A

中文翻译：

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0439阻塞性睡眠呼吸暂停发作个性化预后的非线性动力学预测

摘要

介绍

对疾病预防，早期发现和预防性治疗的重视将彻底改变睡眠临床医生评估其患者的方式。阻塞性睡眠呼吸暂停（OSA）是最普遍的睡眠障碍之一，全球约有1亿患者被诊断为睡眠呼吸暂停。睡眠障碍疗法的有效性可以通过提供OSA发作的个性化和实时预测来增强。OSA预测的先前尝试仅限于捕获基础生理过程的非线性，非平稳动力学。

方法

本文报道了一项对心率动力学的研究，旨在实时预测临床症状出现之前的OSA发作。该方法包括：（a）过渡状态空间网络的表征，以表征呼吸暂停状态的动态过渡；（b）Dirichlet-过程混合-高斯-过程预测方法，用于估计时间的分布；估计直到发作开始的剩余时间。考虑到正常状态向异常（呼吸暂停）的随机演变，即将发生的OSA发作

结果

使用三种数据集对该方法进行了测试，包括（1）基准ECG呼吸暂停数据库（Physionet.org）中14位OSA受试者的20条记录，（2）先前工作的8位受试者的记录。据报道，平均预测准确度（R ²）为0.75％，其中95％置信区间内有87％的观测值。据报道，在发生呼吸暂停之前的1至3分钟，估计的风险指标分别为85.8％，80.2％和75.5％。

结论

当前的预后方法可以与可穿戴设备集成在一起，以促进个性化治疗和及时的预防治疗。

支持

不适用