Utilization of second derivative photoplethysmographic features for myocardial infarction classification

Autor:	Mohd Hasni Jaafar, Kok Beng Gan, Mohd Alauddin Mohd Ali, Rusna Meswari, Nurhafizah Mahri
Rok vydání:	2017
Předmět:	Adult Male medicine.medical_specialty Biomedical Engineering Myocardial Infarction Infarction 030204 cardiovascular system & hematology Logistic regression Sensitivity and Specificity law.invention 03 medical and health sciences Electrocardiography 0302 clinical medicine Randomized controlled trial law Photoplethysmogram Internal medicine medicine Humans 030212 general & internal medicine Myocardial infarction Photoplethysmography Second derivative Aged Aged 80 and over business.industry General Medicine Middle Aged medicine.disease Surgery Jerk Logistic Models Cardiology Crest Female business
Zdroj:	Journal of medical engineeringtechnology. 41(4)
ISSN:	1464-522X
Popis:	Myocardial infarction (MI) is a common disease that causes morbidity and mortality. The current tools for diagnosing this disease are improving, but still have some limitations. This study utilised the second derivative of photoplethysmography (SDPPG) features to distinguish MI patients from healthy control subjects. The features include amplitude-derived SDPPG features (pulse height, ratio, jerk) and interval-derived SDPPG features (intervals and relative crest time (RCT)). We evaluated 32 MI patients at Pusat Perubatan Universiti Kebangsaan Malaysia and 32 control subjects (all ages 37-87 years). Statistical analysis revealed that the mean amplitude-derived SDPPG features were higher in MI patients than in control subjects. In contrast, the mean interval-derived SDPPG features were lower in MI patients than in the controls. The classifier model of binary logistic regression (Model 7), showed that the combination of SDPPG features that include the pulse height (d-wave), the intervals of "ab", "ad", "bc", "bd", and "be", and the RCT of "ad/aa" could be used to classify MI patients with 90.6% accuracy, 93.9% sensitivity and 87.5% specificity at a cut-off value of 0.5 compared with the single features model.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1cad3282415a117346969b2e101809a0 https://pubmed.ncbi.nlm.nih.gov/28351231 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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