ArticlePhysical and engineering sciences in medicine2023
Automated myocardial infarction and angina detection using second derivative of photoplethysmography.
Article in Physical and engineering sciences in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 3 citations in OpenAlex.
- MDT-HMTL: A Multidimensional Temporal Hierarchical Multi-Task Learning Framework for Myocardial Infarction Detection and State Classification Using PPG Signals.Sensors (Basel, Switzerland) · 2026Article
- Photoplethysmography acceleration indices as digital biomarkers of cardiometabolic health.Frontiers in digital health · 2026Article
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7 authors at 4 institutions in 1 country.
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Abstract
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.
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