Detecting Premature Ventricular Contraction by using Regulated Discriminant Analysis with very sparse training data

Per Lynggaard

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)
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Abstract

Pathological electrocardiogram is often used to diagnose abnormal cardiac disorders where accurate classification of the cardiac beat types is crucial for timely diagnosis of dangerous conditions. However, accurate, timely, and precise detection of arrhythmia-types like premature ventricular contraction is very challenging as these signals are multiform, i.e. a reliable detection of these requires expert annotations. In this paper, a multivariate statistical classifier that is able to detect premature ventricular contraction beats is presented. This novel classifier can be trained with a very sparse amount of expert annotated data. To enable this, the dimensionality of the feature vector is kept very low, it uses strong designed features and a regularization mechanism. This approach is compared to other classifiers by using the MIT-BIH arrhythmia database. It has been found that the average accuracy, specificity, and sensitivity are above 96%, which is superior given the sparse amount of training data.

Original languageEnglish
JournalApplied Artificial Intelligence
Volume33
Issue number3
Pages (from-to)229-248
Number of pages20
ISSN0883-9514
DOIs
Publication statusPublished - 23 Feb 2019

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