Impact of Data Transformation: An ECG Heartbeat Classification Approach

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dc.contributor.authorLiang, Y.
dc.contributor.authorHussain, A.
dc.contributor.authorAbbott, D.
dc.contributor.authorMenon, C.
dc.contributor.authorWard, R.
dc.contributor.authorElgendi, M.
dc.date.issued2020
dc.description.abstractCardiovascular diseases continue to be a significant global health threat. The electrocardiogram (ECG) signal is a physiological signal that plays a major role in preventing severe and even fatal heart diseases. The purpose of this research is to explore a simplemathematical feature transformation that could be applied to ECG signal segments in order to improve the detection accuracy of heartbeats, which could facilitate automated heart disease diagnosis. Six different mathematical transformation methods were examined and analyzed using 10s-length ECG segments, which showed that a reciprocal transformation results in consistently better classification performance for normal vs. atrial fibrillation beats and normal vs. atrial premature beats, when compared to untransformed features. The second best data transformation in terms of heartbeat detection accuracy was the cubic transformation. Results showed that applying the logarithmic transformation, which is considered the go-to data transformation, was not optimal among the six data transformations. Using the optimal data transformation, the reciprocal, can lead to a 35.6% accuracy improvement. According to the overall comparison tested by different feature engineering methods, classifiers, and different dataset sizes, performance improvement also reached 4.7%. Therefore, adding a simple data transformation step, such as the reciprocal or cubic, to the extracted features can improve current automated heartbeat classification in a timely manner.
dc.description.statementofresponsibilityYongbo Liang, Ahmed Hussain, Derek Abbott, Carlo Menon, Rabab Ward, and Mohamed Elgendi
dc.identifier.citationFrontiers in Digital Health, 2020; 2:610956-1-610956-9
dc.identifier.doi10.3389/fdgth.2020.610956
dc.identifier.issn2673-253X
dc.identifier.issn2673-253X
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]
dc.identifier.urihttps://hdl.handle.net/2440/145736
dc.language.isoen
dc.publisherFrontiers Media S.A.
dc.rights© 2020 Liang, Hussain, Abbott, Menon, Ward and Elgendi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.source.urihttps://doi.org/10.3389/fdgth.2020.610956
dc.subjectfeature mapping; feature representation; feature transformation; feature conversion; feature restructuring; data Wrangling
dc.titleImpact of Data Transformation: An ECG Heartbeat Classification Approach
dc.typeJournal article
pubs.publication-statusPublished

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