ECG SIGNALS CLASSIFICATION BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS

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MOAZZEN I.; AHMADZADEH M.R.

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Paper Information

Journal: Majlesi Journal of Electrical Engineering Year:2009 | Volume:3 | Issue:3 (10) Page(s): 31-37

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Title

ECG SIGNALS CLASSIFICATION BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS

Author(s)

MOAZZEN I. | AHMADZADEH M.R. | Issue Writer Certificate

Keywords

ELECTROCARDIOGRAMQ2

DISCRETE WAVELET TRANSFORMQ2

PROBABILISTIC NEURAL NETWORKQ3

HISTOGRAMQ3

Abstract

In this paper a very intelligent tool with low computational complexity is presented for Electroencephalogram (ECG) signal classification. The proposed classifier is based on DISCRETE WAVELET TRANSFORM (DWT) and PROBABILISTIC NEURAL NETWORK (PNN). The novelty of this approach is that signal statistics, morphological analysis and DWT of the HISTOGRAM of signal (density estimation) altogether have been used to achieve a higher recognition rate. ECG signals and their density estimation are decomposed into sub-classes using DWT. A PNN is used to classify ECG signals using statistical discriminating features which are extracted from ECG and its sub-classes. Experimental results on five classes of ECG signals from MIT-BIH arrhythmia database show that the proposed method learns very fast, low computational complexity, and a very high performance compared to the previous methods.

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APA: Copy

MOAZZEN, I., & AHMADZADEH, M.R.. (2009). ECG SIGNALS CLASSIFICATION BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS. MAJLESI JOURNAL OF ELECTRICAL ENGINEERING, 3(3 (10)), 31-37. SID. https://sid.ir/paper/188240/en

Vancouver: Copy

MOAZZEN I., AHMADZADEH M.R.. ECG SIGNALS CLASSIFICATION BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS. MAJLESI JOURNAL OF ELECTRICAL ENGINEERING[Internet]. 2009;3(3 (10)):31-37. Available from: https://sid.ir/paper/188240/en

IEEE: Copy

I. MOAZZEN, and M.R. AHMADZADEH, “ECG SIGNALS CLASSIFICATION BASED ON WAVELET TRANSFORM AND PROBABILISTIC NEURAL NETWORKS,” MAJLESI JOURNAL OF ELECTRICAL ENGINEERING, vol. 3, no. 3 (10), pp. 31–37, 2009, [Online]. Available: https://sid.ir/paper/188240/en

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