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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Bradycardia is defined as a sinus rhythm of less than 60 beats per minute and atrial tachyarrhythmia including atrial fibrillation (AF) is frequently associated with bradycardia. Pacemaker is the only effective treatment for symptomatic bradycardia and automatic mode switching (AMS) function is built in pacemaker to switch mode in the presence of atrial tachyarrhythmia. AMS algorithms consider appropriate mode switching in case of undersensing or oversensing and this consideration makes their onset time and resynchronization time late. Current pacemakers have onset time from 2.5 seconds to 26 seconds and resynchronization time from 3.4 seconds to 143 seconds according to manufacturers. In this work, we proposed beat detection algorithm based on amplitude difference between peak and trough for accurate extraction of atrial rate achieving faster mode switching. Evaluation of beat detection algorithm was conducted with six canine AF electrogram (EGM) data. Result showed 96.64% sensitivity, 95.5% positive predictive value in average. With this, transition from AF to normal sinus rhythm could be detected faster than existing AMS algorithms. In conclusion, proposed algorithm can efficiently detect beats in EGM during AF and from this, we can implement faster AMS algorithm.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1109/EMBC.2016.7591301 | DOI Listing |
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