ECG ARRHYTHMIAS DETECTION USING A NEW INTELLIGENT SYSTEM BASED ON NEURAL NETWORKS AND WAVELET TRANSFORM

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

POORAHANGARYAN FERESHTEH; KIANI AZADEH; KARAMI ALI; ZANJ BAHMAN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS Year:2012 | Volume:9 | Issue:1 Page(s): 33-39

Download Full-Text

Persian Verion

View:

1,558

Download:

Cites:

Information Journal Paper

Title

ECG ARRHYTHMIAS DETECTION USING A NEW INTELLIGENT SYSTEM BASED ON NEURAL NETWORKS AND WAVELET TRANSFORM

Author(s)

POORAHANGARYAN FERESHTEH | KIANI AZADEH | KARAMI ALI | ZANJ BAHMAN | Issue Writer Certificate

Keywords

ECGQ3

ARRHYTHMIAQ3

DISCRETE WAVELET TRANSFORM (DWT)Q3

NEURAL NETWORKQ3

Abstract

In this paper, Automatic electrocardiogram (ECG) ARRHYTHMIAs classification is essential to timely diagnosis of dangerous electromechanical behaviors and conditions of the heart. In this paper, a new method for ECG ARRHYTHMIAs classification using wavelet transform (WT) and NEURAL NETWORKs (NN) is proposed. Here, we have used a discrete wavelet transform (DWT) for processing ECG recordings, and extracting some time frequency features to be used for training a multi-layered perceptron (MLP) NEURAL NETWORK. In fact, the MLP NN performs the classification task. Although many algorithms have been presented for ECG ARRHYTHMIAs detection over the past years, the results reported in the past, have generally been limited to relatively small set of data patterns. Here, we have used 20 recordings of the MIT-BIH ARRHYTHMIAs data base for training and testing our NEURAL NETWORK based classifier. The simulation results show that the classification accuracy of our method is 97% over 420 patterns using 20 files including four ARRHYTHMIAs.

Cites

No record.

References

ICA-BASED CLASSIFICATION OF EEG PATTERN DURING IMAGINED HAND MOVEMENT

Cite

APA: Copy

POORAHANGARYAN, FERESHTEH, KIANI, AZADEH, KARAMI, ALI, & ZANJ, BAHMAN. (2012). ECG ARRHYTHMIAS DETECTION USING A NEW INTELLIGENT SYSTEM BASED ON NEURAL NETWORKS AND WAVELET TRANSFORM. JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS, 9(1), 33-39. SID. https://sid.ir/paper/115758/en

Vancouver: Copy

POORAHANGARYAN FERESHTEH, KIANI AZADEH, KARAMI ALI, ZANJ BAHMAN. ECG ARRHYTHMIAS DETECTION USING A NEW INTELLIGENT SYSTEM BASED ON NEURAL NETWORKS AND WAVELET TRANSFORM. JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS[Internet]. 2012;9(1):33-39. Available from: https://sid.ir/paper/115758/en

IEEE: Copy

FERESHTEH POORAHANGARYAN, AZADEH KIANI, ALI KARAMI, and BAHMAN ZANJ, “ECG ARRHYTHMIAS DETECTION USING A NEW INTELLIGENT SYSTEM BASED ON NEURAL NETWORKS AND WAVELET TRANSFORM,” JOURNAL OF IRANIAN ASSOCIATION OF ELECTRICAL AND ELECTRONICS ENGINEERS, vol. 9, no. 1, pp. 33–39, 2012, [Online]. Available: https://sid.ir/paper/115758/en

Related Journal Papers