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. 2020 Dec 23:2:610956.
doi: 10.3389/fdgth.2020.610956. eCollection 2020.

Impact of Data Transformation: An ECG Heartbeat Classification Approach

Yongbo Liang  1 , Ahmed Hussain  2 , Derek Abbott  3   4 , Carlo Menon  5 , Rabab Ward  1 , Mohamed Elgendi  1   2   5   6
Affiliations

Impact of Data Transformation: An ECG Heartbeat Classification Approach

Yongbo Liang et al. Front Digit Health. .

Abstract

Cardiovascular 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 simple mathematical 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.

Keywords: data Wrangling; feature conversion; feature mapping; feature representation; feature restructuring; feature transformation.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
A work flowchart of this study.
Figure 2
Figure 2
Comparison of classification performance between unfiltered data and filtered data. Note that the reciprocal is the most consistently effective data transformation.
Figure 3
Figure 3
D-value comparison between the original and transformed features. Note that the reciprocal is the most consistently effective data transformation.
Figure 4
Figure 4
Overall impact of data transformations on classifying ECG heartbeats by averaging the unbalanced, RUS, and SMOTE results. Note that the reciprocal is the most consistently effective data transformation.
Figure 5
Figure 5
Overall D-value changes after averaging the unbalanced, RUS, and SMOTE results. Note that the reciprocal is the most consistently effective data transformation.
See this image and copyright information in PMC

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References

    1. Klabunde RE. Cardiovascular Physiology Concepts, 2nd edn. Lippincott Williams & Wilkins (2011).
    1. Akhoondi MPaF. Providing an efficient algorithm for finding R peaks in ECG signals and detecting ventricular abnormalities with morphological features. J Med Signals Sensors. (2016) 6:218–23. 10.4103/2228-7477.195090 - DOI - PMC - PubMed
    1. Tateno K, Glass L. A method for detection of atrial fibrillation using RR intervals. Comp Cardiol. (2000) 27:391–4. 10.1016/j.amjcard.2011.01.028 - DOI
    1. Elgendi M, Jonkman M, De Boer F. In: 2008 7th IEEE International Conference on Cognitive Informatics. (2008). p. 83–8.
    1. Krishna Prasad GJSS. Classification of ECG arrhythmias using multi-resolution analysis and neural networks. In TENCON 2003. Bangalore (2003). p. 227–31.

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