Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
The growing rate of public health problem for increasing number of people afflicted with poor sleep quality suggests the importance of developing portable sleep electroencephalogram (EEG) monitoring systems. The system could record the overnight EEG signal, classify sleep stages automatically, and grade the sleep quality. We in our laboratory collected the signals in an easy way using a single channel with three electrodes which were placed in frontal position in case of the electrode drop-off during sleep. For a test, either silver disc electrodes or disposable medical electrocardiographic electrodes were used. Sleep EEG recorded by the two types of electrodes was compared to each other so as to find out which type was more suitable. Two algorithms were used for sleep EEG processing, i. e. amplitude-integrated EEG (aEEG) algorithm and sample entropy algorithm. Results showed that both algorithms could perform sleep stage classification and quality evaluation automatically. The present designed system could be used to monitor overnight sleep and provide quantitative evaluation.
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