Using off-the-shelf lossy compression for wireless home sleep staging

Kun Chan Lan, Da Wei Chang, Chih En Kuo, Ming Zhi Wei, Yu Hung Li, Fu Zen Shaw, Sheng Fu Liang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


Background: 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. New method: 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. Results: 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. Conclusions: We demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.

Original languageEnglish
Pages (from-to)142-152
Number of pages11
JournalJournal of Neuroscience Methods
Publication statusPublished - 2015 May 5

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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