Electrocardiogram Beat Classification Using Support Vector Machine and Extreme Learning Machine

Banupriya, C. V.; Karpagavalli, S.

doi:10.1007/978-3-319-03107-1_22

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…A few approaches employ multi-layered perceptron network for classification of bundle branch blocks but they are not enough for image ECG data of paper based ECG strip [9]. Machine learning methods, support vector machine and extreme learning machine have been tried to classify four different types of heart beats including LBBB, RBBB along with premature ventricular contraction [10]. But most pattern recognition techniques are either applied on electrical signal of ECG or good quality images (scanned copied).…”

Section: Introduction and Past Workmentioning

confidence: 99%

Pattern classification of time plane features of ECG wave from cell-phone photography for machine aided cardiac disease diagnosis

Mitra

Pramanik

Mitra

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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This article reports a robust technique for extracting time plane features of Electrocardiogram (ECG) from digital images of ECG paper strips. We concluded this article reporting performance evaluation of the system developed for machine aided cardiac disease detection. Mostly paper based ECG recordings are used in developing countries and digital photographs of different leads could easily be taken and sent with a mediocre cellular phone set. Apart from extracting the features, the proposed system detects cardiac axis deviation and diagnose if Left or Right Bundle Branch Blockage (LBBB or RBBB) is present while fed with the digital photographs of different leads of ECG strips. Preprocessing of the low-resolution images involves background grid line noise removal, adaptive image binarization by Sauvola's method and Bresenham's line joining algorithm to link the ECG signature, if broken. Pattern extraction mainly delineate the time plane features like P wave, QRS complex and T wave using water reservoir based pattern recognition techniques and Discrete Wavelet Transform (DWT). Cardiac axis deviation detection is done by checking the overall voltage levels of QRS complexes of lead I, II and III.Having the knowledge of cardiac axis completes the requirements to comment on the cardiac blockage like Left or Right Bundle Branch Blockage (LBBB or RBBB). Thus, the proposed algorithm is primarily developed for machine aided diagnosis of LBBB or RBBB from the digital photographs of ECG paper strips.

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Section: Introduction and Past Workmentioning

confidence: 99%