Ferdowsi, Mahbuba and Kwan, Ban-Hoe and Tan, Maw Pin and Saedon, Nor' Izzati and Subramaniam, Sukanya and Abu Hashim, Noor Fatin Izzati and Nasir, Siti Sakinah Mohd and Abidin, Imran Zainal and Chee, Kok Han and Goh, Choon-Hian (2024) Classification of vasovagal syncope from physiological signals on tilt table testing. BioMedical Engineering OnLine, 23 (1). p. 37. ISSN 1475-925X, DOI https://doi.org/10.1186/s12938-024-01229-9.
Full text not available from this repository.Abstract
Background The diagnostic test for vasovagal syncope (VVS), the most common cause of syncope is head-up tilt test (HUTT) assessment. During the test, subjects experienced clinical symptoms such as nausea, sweating, pallor, the feeling of palpitations, being on the verge of passing out, and fainting. The study's goal is to develop an algorithm to classify VVS patients based on physiological signals blood pressure (BP) and electrocardiography (ECG) obtained from the HUTT.Methods After 10 min of supine rest, the subject was tilted at a 70-degree angle on a tilt table for approximately a total of 35 min. 400 mu g of glyceryl trinitrate (GTN) was administered sublingually after the first 20 min and monitoring continued for another 15 min. Mean imputation and K-nearest neighbors (KNN) imputation approaches to handle missing values. Next, feature selection techniques were implemented, including genetic algorithm, recursive feature elimination, and feature importance, to determine the crucial features. The Mann-Whitney U test was then performed to determine the statistical difference between two groups. Patients with VVS are categorized via machine learning models including Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Multinomial Naive Bayes (MNB), KNN, Logistic Regression (LR), and Random Forest (RF). The developed model is interpreted using an explainable artificial intelligence (XAI) model known as partial dependence plot.Results A total of 137 subjects aged between 9 and 93 years were recruited for this study, 54 experienced clinical symptoms were considered positive tests, while the remaining 83 tested negative. Optimal results were obtained by combining the KNN imputation technique and three tilting features with SVM with 90.5% accuracy, 87.0% sensitivity, 92.7% specificity, 88.6% precision, 87.8% F1 score, and 95.4% ROC (receiver operating characteristics) AUC (area under curve).Conclusions The proposed algorithm effectively classifies VVS patients with over 90% accuracy. However, the study was confined to a small sample size. More clinical datasets are required to ensure that our approach is generalizable.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Tunku Abdul Rahman |
| Uncontrolled Keywords: | Syncope; Head-up tilt test; Machine learning; Explainable artificial intelligence; Partial dependence plot |
| Subjects: | R Medicine > R Medicine (General) |
| Divisions: | Faculty of Medicine Universiti Malaya Medical Centre (UMMC) |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 16 Oct 2024 09:20 |
| Last Modified: | 16 Oct 2024 09:20 |
| URI: | http://eprints.um.edu.my/id/eprint/45396 |
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