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R-Peaks and Wavelet-Based Feature Extraction on K-Nearest Neighbor for ECG Arrhythmia Classification

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Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications (RoViSP 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1123))

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Abstract

The aim of this research is to classify 17 types of arrhythmias by applying the algorithm developed from combining the morphological and the wavelet-based statistical features. The proposed arrhythmia classification algorithm consists of four stages: pre-processing, detection of R-peaks, feature extraction, and classification. Seven morphological features (MF) that were retrieved from the R-peak locations. Following this, another nine wavelet-based statistical features (SF) were gathered by decomposing wavelets in level 4 from the Daubechies 1 wavelet (Db1). These 16 features are then applied to the k-nearest neighbor (k-NN) algorithm. The accuracy (ACC) of the suggested classification algorithm was assessed by using the MIT-BIH arrhythmia benchmark database (MIT-BIHADB). The experimental results of this work attained an average accuracy (ACC) of 99.00%.

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Author information

Authors and Affiliations

  1. Faculty of Electronic Engineering and Technology (FKTEN), Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia

    A. M. Khairuddin, K. N. F. Ku Azir & C. B. M. Rashidi

  2. Centre of Excellence for Advanced Computing (AdvComp), Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia

    A. M. Khairuddin, K. N. F. Ku Azir & K. N. F. Ku Azir

  3. Centre of Excellence for Advanced Communication Engineering (ACE), Universiti Malaysia Perlis (UniMAP), 02600, Arau, Perlis, Malaysia

    C. B. M. Rashidi

Authors
  1. A. M. Khairuddin
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  2. K. N. F. Ku Azir
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  3. C. B. M. Rashidi
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Corresponding author

Correspondence to K. N. F. Ku Azir .

Editor information

Editors and Affiliations

  1. Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Pulau Pinang, Malaysia

    Nur Syazreen Ahmad

  2. School of Electrical & Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia

    Junita Mohamad-Saleh

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia

    Jiashen Teh

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Khairuddin, A.M., Ku Azir, K.N.F., Rashidi, C.B.M. (2024). R-Peaks and Wavelet-Based Feature Extraction on K-Nearest Neighbor for ECG Arrhythmia Classification. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_66

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