Real-time prediction of disordered breathing events in people with obstructive sleep apnea.
Real-time prediction of disordered breathing events in people with obstructive sleep apnea.
复制标题
实时预测阻塞性睡眠呼吸暂停患者的呼吸紊乱事件。
DOI：
10.1007/s11325-014-0993-x
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Carley,DavidW
中科院分区：
文献类型：
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作者：
Waxman,JonathanA;Graupe,Daniel;Carley,DavidW
目的阻塞性睡眠呼吸暂停（OSA）的常规治疗是有效的，但患者依从性差，可能无法完全缓解主要的OSA相关心血管危险因素或改善生活质量的某些方面。预测OSA患者呼吸紊乱事件的发生可能会导致治疗OSA的策略得到改善，并为我们了解潜在的疾病机制提供信息。在这项工作中，我们描述了一个可部署的系统，能够进行实时预测的睡眠呼吸障碍事件的患者诊断为OSA，提供了一种新的方法，深入了解OSA的病理生理学，发现人口亚组，并改善theraps.MethodsLarge内存存储和检索人工神经网络与864个不同的配置应用于多导睡眠图记录64例。小波变换，熵的措施，和其他统计应用于六个生理信号，提供网络输入。使用近似统计检验来确定每个患者的最佳性能网络。最重要的预测障碍性呼吸事件在OSA患者通过分析内部网络parameters.ResultsThe平均优化的个人预测的灵敏度和特异性分别为0.81和0.77，分别确定。对于所有OSA患者，预测结果优于随机猜测。内部网络参数的分析显示，高度的异质性之间的呼吸紊乱事件的预测，并可能揭示patient subgroup.ConclusionsWe报告的第一个实用的系统来预测个人呼吸紊乱事件在一个异质组的患者诊断为OSA。呼吸障碍的预测模式提示了潜在的病理生理学机制，并强调了对OSA诊断、治疗和管理的个体化方法的需求。
PurposeConventional therapies for obstructive sleep apnea (OSA) are effective but suffer from poor patient adherence and may not fully alleviate major OSA-associated cardiovascular risk factors or improve certain aspects of quality of life. Predicting the onset of disordered breathing events in OSA patients may lead to improved strategies for treating OSA and inform our understanding of underlying disease mechanisms. In this work, we describe a deployable system capable of performing real-time predictions of sleep disordered breathing events in patients diagnosed with OSA, providing a novel approach for gaining insight into OSA pathophysiology, discovering population subgroups, and improving therapies.MethodsLArge Memory STorage and Retrieval artificial neural networks with 864 different configurations were applied to polysomnogram records from 64 patients. Wavelet transforms, measures of entropy, and other statistics were applied to six physiological signals to provide network inputs. Approximate statistical tests were used to determine the best performing network for each patient. The most important predictors of disordered breathing events in OSA patients were determined by analyzing internal network parameters.ResultsThe average optimized individual prediction sensitivity and specificity were 0.81 and 0.77, respectively. Predictions were better than random guessing for all OSA patients. Analysis of internal network parameters revealed a high degree of heterogeneity among disordered breathing event predictors and may reveal patient subgroups.ConclusionsWe report the first practical system to predict individual disordered breathing events in a heterogeneous group of patients diagnosed with OSA. The pattern of disordered breathing predictors suggests variable underlying pathophysiological mechanisms and highlights the need for an individualized approach to OSA diagnosis, therapy, and management.