کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6269380 1295136 2012 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Basic NeuroscienceAutomated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب (عمومی)
پیش نمایش صفحه اول مقاله
Basic NeuroscienceAutomated determination of wakefulness and sleep in rats based on non-invasively acquired measures of movement and respiratory activity
چکیده انگلیسی

The current standard for monitoring sleep in rats requires labor intensive surgical procedures and the implantation of chronic electrodes which have the potential to impact behavior and sleep. With the goal of developing a non-invasive method to determine sleep and wakefulness, we constructed a non-contact monitoring system to measure movement and respiratory activity using signals acquired with pulse Doppler radar and from digitized video analysis. A set of 23 frequency and time-domain features were derived from these signals and were calculated in 10 s epochs. Based on these features, a classification method for automated scoring of wakefulness, non-rapid eye movement sleep (NREM) and REM in rats was developed using a support vector machine (SVM). We then assessed the utility of the automated scoring system in discriminating wakefulness and sleep by comparing the results to standard scoring of wakefulness and sleep based on concurrently recorded EEG and EMG. Agreement between SVM automated scoring based on selected features and visual scores based on EEG and EMG were approximately 91% for wakefulness, 84% for NREM and 70% for REM. The results indicate that automated scoring based on non-invasively acquired movement and respiratory activity will be useful for studies requiring discrimination of wakefulness and sleep. However, additional information or signals will be needed to improve discrimination of NREM and REM episodes within sleep.

► We constructed a non-contact monitoring system to measure movement and respiration. ► We developed a method for automated scoring of wakefulness and sleep using a support vector machine. ► Agreement between automated and visual scoring was 91% for wakefulness. ► Agreement was 84% for non-rapid eye movement sleep and 70% for rapid eye movement sleep. ► Automated scoring based on movement and respiration can discriminate wakefulness and sleep.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Neuroscience Methods - Volume 204, Issue 2, 15 March 2012, Pages 276-287
نویسندگان
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