<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neha, H. K.</style></author><author><style face="normal" font="default" size="100%">Sardana, H. K.</style></author><author><style face="normal" font="default" size="100%">Dahiya, N.</style></author><author><style face="normal" font="default" size="100%">Dogra, N.</style></author><author><style face="normal" font="default" size="100%">Kanawade, R.</style></author><author><style face="normal" font="default" size="100%">Sharma, Y. P.</style></author><author><style face="normal" font="default" size="100%">Kumar, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated myocardial infarction and angina detection using second derivative of photoplethysmography</style></title><secondary-title><style face="normal" font="default" size="100%">Physical and Engineering Sciences in Medicine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial neural network</style></keyword><keyword><style  face="normal" font="default" size="100%">Myocardial infarction detection</style></keyword><keyword><style  face="normal" font="default" size="100%">PPG</style></keyword><keyword><style  face="normal" font="default" size="100%">SDPPG</style></keyword><keyword><style  face="normal" font="default" size="100%">Unstable angina detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">1259-1269</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Photoplethysmography (PPG) based healthcare devices have gained enormous interest in the detection of cardiac abnormalities. Limited research has been implemented for myocardial infarction (MI) detection. Moreover, PPG-based detection of angina is still a research gap. PPG signals are not always informative. Therefore, this research work presents the use of PPG signals and their second derivative to evaluate myocardial infarction and angina using a novel set of morphological features. The obtained morphological features are fed onto the feed-forward artificial neural network for the identification of the type of MI and unstable angina (UA). The initial experiments have been carried out on non-ambulatory (public) subjects for feature extraction and later evaluated on ambulatory (self-generated) databases. The intended method attains accuracy, sensitivity, and specificity of 98%, 97%, 98% on the public database and 94%, 94%, 94% on the self-generated database. The result shows that the proposed set of features can detect MI and UA with significant accuracy.&lt;/p&gt;
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	Foreign&lt;/p&gt;
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	4.4&lt;/p&gt;
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