@article {48807,
	title = {Automated myocardial infarction and angina detection using second derivative of photoplethysmography},
	journal = {Physical and Engineering Sciences in Medicine},
	volume = {46},
	year = {2023},
	month = {SEP},
	pages = {1259-1269},
	type = {Article},
	abstract = {<p>
	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.</p>
},
	keywords = {Artificial neural network, Myocardial infarction detection, PPG, SDPPG, Unstable angina detection},
	issn = {2662-4729},
	doi = {10.1007/s13246-023-01293-w},
	author = {Neha, H. K. and Sardana, H. K. and Dahiya, N. and Dogra, N. and Kanawade, R. and Sharma, Y. P. and Kumar, S.}
}
