论文标题
使用持续同源和马尔可夫链对睡眠状态进行分类：一项试点研究
Classifying sleep states using persistent homology and Markov chain: a Pilot Study
论文作者
论文摘要
阻塞性睡眠呼吸暂停（OSA）是一种睡眠失调的呼吸形式，其特征是在睡眠期间经常发生上气道塌陷。儿科OSA发生在1-5％的儿童中，并且可能与其他严重的健康状况有关，例如高血压，行为问题或生长改变。 OSA通常是通过研究患者的睡眠周期，通过各种睡眠状态（例如觉醒，快速移动和非比起眼动的）进行诊断来诊断的。睡眠状态数据是使用过夜的多聚会测试来获得的，该测试患者在医院或睡眠诊所接受了该测试，该技术人员在每30秒的时间间隔内手动标记，也称为“ Epoch”，目前的睡眠状态。这个过程很费力，容易出现人为错误。我们寻求一种对睡眠状态进行分类的自动方法，以及一种分析睡眠周期的方法。本文是使用两种方法进行睡眠状态分类的试点研究：首先，我们将使用拓扑数据分析领域的方法来对睡眠状态进行分类，其次，我们将睡眠状态模型为马尔可夫链，并视觉分析睡眠模式。将来，我们将继续基于这项工作来改善我们的方法。
Obstructive sleep Apnea (OSA) is a form of sleep disordered breathing characterized by frequent episodes of upper airway collapse during sleep. Pediatric OSA occurs in 1-5% of children and can related to other serious health conditions such as high blood pressure, behavioral issues, or altered growth. OSA is often diagnosed by studying the patient's sleep cycle, the pattern with which they progress through various sleep states such as wakefulness, rapid eye-movement, and non-rapid eye-movement. The sleep state data is obtained using an overnight polysomnography test that the patient undergoes at a hospital or sleep clinic, where a technician manually labels each 30 second time interval, also called an "epoch", with the current sleep state. This process is laborious and prone to human error. We seek an automatic method of classifying the sleep state, as well as a method to analyze the sleep cycles. This article is a pilot study in sleep state classification using two approaches: first, we'll use methods from the field of topological data analysis to classify the sleep state and second, we'll model sleep states as a Markov chain and visually analyze the sleep patterns. In the future, we will continue to build on this work to improve our methods.
