ECG classification using wavelet transform and Discriminant Analysis

Abdullah, Farah Nur Atiqah Francis; Abas, Fazly Salleh; Besar, Rosli

doi:10.1109/icobe.2012.6179003

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Li et al [93] have designed the WCF for the ECG heartbeats using the coefficients of the DWT with the mother wavelet selected from Reverse Bior6.8 (RBior6.8), Fejer-Korovkin22 (FK22), and so forth, and then adopted the Metric Learning to Rank (MLR) to improve the discriminative ability of the feature space, and finally measured the Minority Based Dissimilarity (MBD) between the feature sets for multiple-beat arrhythmia classification. Abdullah et al [94] have concatenated the WCF with the TF which is extracted based on the R peaks detected by the DWT together as the representation of each ECG heartbeat, and then implemented the quadratic discriminant analysis on the basis of this representation for arrhythmia recognition. Here, the WCF contains the mean, the standard deviation, the skewness and the kurtosis of the DWT coefficients; the TF consists of the pre-RR and the post-RR intervals and the ratio of the pre-RR interval over the total period of each heartbeat.…”

Section: D: Besides Neural Network and Support Vector Machine Many mentioning

confidence: 99%

Wavelets for Electrocardiogram: Overview and Taxonomy

2019

IEEE Access

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Physiological and pathological information within electrocardiogram (ECG) is crucial for the diagnosis of heart diseases. Computer-aided diagnosis for the ECG signals has drawn growing research attention up to date. Automatic ECG analysis mainly includes signal denoising, wave detection, and heartbeat classification. These three issues are relevant that the signal denoising can help attenuate the noises and accentuate the typical waves in ECG signals for wave detection, and wave detection can help locate the typical ECG waves and acquire the diagnostically valuable heartbeats based on these waves for the heartbeat classification. The wavelet-based methods play important roles in the three issues, but these methods are scattered and unorganized in the literature. In order to manifest the value of these methods, this paper contributes an overview and taxonomy on them. This paper does the comprehensive summary and systematic categorization on the methods for signal denoising, wave detection, and heartbeat classification according to the deep analysis of their methodological characteristics. By doing so, this paper not only uncovers the inner mechanism that why wavelet-based methods are suitable for ECG analysis but also reveals the designing principles that these methods potentially follow. Finally, this paper has provided an outlook for the developing prospect of ''wavelets for ECG'' in the future. INDEX TERMS Wavelets for electrocardiogram, signal denoising, wave detection, heartbeat classification, overview and taxonomy. I. OUTLINE AND INTRODUCTION A. OUTLINE AND CONTRIBUTION

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Section: D: Besides Neural Network and Support Vector Machine Many mentioning

confidence: 99%