@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.}
}
@article {48328,
	title = {Photoplethysmograph based arrhythmia detection using morphological features},
	journal = {Biomedical Signal Processing and Control},
	volume = {81},
	year = {2023},
	month = {MAR},
	pages = {104422},
	type = {Article},
	abstract = {<p>
	Photoplethysmography (PPG) is a non-invasive optical technique that is used for the detection of cardiovascular diseases. The paroxysmal nature of arrhythmic events and the lack of timely recorded data emphasize the need to develop an automated method for the identification of arrhythmias. The literature shows the detection of a single type of arrhythmia using PPG. However, limited research has been carried out for the detection of multiple types of arrhythmia. In this research work, a new set of morphological features have been proposed for the automated detection of multiple arrhythmias using rule-based and statistical learning-based approaches. The proposed work has been implemented on the retrospective dataset and validated on the prospective dataset. The results show that the rule-based arrhythmia detection method is equipollent to the statistical learning approach with an accuracy of 98.43\%/94.16\% on the retrospective dataset and 94.16\%/93\% on the prospective dataset.</p>
},
	keywords = {Arrhythmia, Machine learning algorithms, Photoplethysmography, Signal processing},
	issn = {1746-8094},
	doi = {10.1016/j.bspc.2022.104422},
	author = {Neha and Sardana, H. K. and Kanawade, R. and Dogra, N.}
}
