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

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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 it uses 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 percent, which is superior given the sparse amount of training data.
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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 it uses 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 percent, which is superior given the sparse amount of training data.
Original languageEnglish
JournalApplied Artificial Intelligence
ISSN0883-9514
DOI
Publication statusE-pub ahead of print - 2019
Publication categoryResearch
Peer-reviewedYes
ID: 290248502