Lan Kun-Chan, Chang Da-Wei, Kuo Chih-En, Wei Ming-Zhi, Li Yu-Hung, Shaw Fu-Zen, Liang Sheng-Fu
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan; Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.
Department of Psychology, National Cheng Kung University, Tainan 701, Taiwan.
J Neurosci Methods. 2015 May 15;246:142-52. doi: 10.1016/j.jneumeth.2015.03.013. Epub 2015 Mar 16.
Recently, there has been increasing interest in the development of wireless home sleep staging systems that allow the patient to be monitored remotely while remaining in the comfort of their home. However, transmitting large amount of Polysomnography (PSG) data over the Internet is an important issue needed to be considered. In this work, we aim to reduce the amount of PSG data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to classify sleep stages.
We examine the effects of off-the-shelf lossy compression on an all-night PSG dataset from 20 healthy subjects, in the context of automated sleep staging. The popular compression method Set Partitioning in Hierarchical Trees (SPIHT) was used, and a range of compression levels was selected in order to compress the signals with various degrees of loss. In addition, a rule-based automatic sleep staging method was used to automatically classify the sleep stages.
Considering the criteria of clinical usefulness, the experimental results show that the system can achieve more than 60% energy saving with a high accuracy (>84%) in classifying sleep stages by using a lossy compression algorithm like SPIHT.
COMPARISON WITH EXISTING METHOD(S): As far as we know, our study is the first that focuses how much loss can be tolerated in compressing complex multi-channel PSG data for sleep analysis.
We demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.
最近，人们对开发无线家庭睡眠分期系统的兴趣日益浓厚，该系统可让患者在舒适的家中接受远程监测。然而，通过互联网传输大量多导睡眠图（PSG）数据是一个需要考虑的重要问题。在这项工作中，我们旨在减少必须传输或存储的PSG数据量，同时对与睡眠阶段分类相关的信号信息产生尽可能小的影响。
在自动睡眠分期的背景下，我们研究了现成的有损压缩对来自20名健康受试者的整夜PSG数据集的影响。使用了流行的压缩方法分层树中的集合划分（SPIHT），并选择了一系列压缩级别以对信号进行不同程度的有损压缩。此外，使用基于规则的自动睡眠分期方法对睡眠阶段进行自动分类。
考虑到临床实用性标准，实验结果表明，通过使用SPIHT等有损压缩算法，该系统在睡眠阶段分类中可以实现超过60%的节能，且准确率较高（>84%）。
据我们所知，我们的研究是首个关注在压缩用于睡眠分析的复杂多通道PSG数据时可容忍多少损失的研究。
我们证明了使用有损SPIHT压缩进行无线家庭睡眠分期的可行性。
