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
Introduction: Analysis of continuous diaphragm electromyography (dEMG) has not been well studied. We describe a system of analyzing continuous dEMG using implanted electrodes.
Methods: dEMG signal was acquired via two pairs of electrodes near the diaphragm motor points. Raw bursts of dEMG signal were compared to externally captured electrocardiogram (ECG) using adaptive filtering in order to remove cardiac contamination. Differential energy levels were used to identify each dEMG burst, and average amplitude and area values from both hemidiaphragms were aggregated and averaged for the duration of the recording.
Results: A 64-year-old patient with amyotrophic lateral sclerosis underwent three serial dEMG studies every 6 months. An average of three tracings were collected per visit, and all had excellent intertest reliability (κ > 0.8). Average dEMG area correlated with forced vital capacity and mean inspiratory pressure (r(2) > 0.9).
Conclusions: The approach described represents a comprehensive method for capturing and analyzing continuous diaphragm EMG signal.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1002/mus.21985 | DOI Listing |
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