کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4334930 1614623 2015 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using off-the-shelf lossy compression for wireless home sleep staging
ترجمه فارسی عنوان
استفاده از فشرده سازی از دست رفته در فضای بیرونی برای تنظیم خواب بیسیم خانه
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
چکیده انگلیسی


• We examine the effects of off-the-shelf lossy compression on an all-night PSG dataset, in the context of automated sleep staging.
• The popular compression method Set Partitioning in Hierarchical Trees (SPIHT) was used.
• A rule-based automatic sleep staging method was used to classify the sleep stages.
• The result shows that the system can achieve more than 60% energy saving and a high accuracy (>84%) in classifying sleep stages.
• The feasibility of using off-the-shelf lossy compression for wireless home sleep staging was demonstrated.

BackgroundRecently, 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 methodWe 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.ResultsConsidering 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.ConclusionsWe demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Neuroscience Methods - Volume 246, 15 May 2015, Pages 142–152
نویسندگان
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