The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON SYSTEMS
Transactions ID Number: 52-657
Full Name: Wisnu Jatmiko
Position: Doctor
(Researcher)
Age: ON
Sex: Male
Address: Kampus UI Depok 16424
Country: INDONESIA
Tel: 021 786 3419
Tel prefix: +62
Fax: +62 21 786 3415
E-mail address: wisnuj@cs.ui.ac.id
Other E-mails:
Title of the Paper: Heart Beat Classification Using Wavelet Feature Based on Neural Network
Authors as they appear in the Paper: Wisnu Jatmiko, Nulad W.P., Elly Matul I, I Made Agus Setiawan, and P. Mursanto
Email addresses of all the authors: wisnuj@cs.ui.ac.id,nuladwisnupambudi@gmail.com,elly.matul@gmail.com,madeagus@gmail.com
Number of paper pages: 10
Abstract: Arrhytmia is one of the most crucial problem in cardiology. It can be diagnosed by a standard electrocardiogram (ECG). So far, many methode have been develop for arrhytmia detection, recognition and classification But many methode for arrhytmia beat classification have been yet able to solve unknown category data. This paper will discusses about how to determine the type of arrhythmia in computerize way through classification process that able to solve unkown categorical beat. We use FLVQ to solve the weakness of the other methode of calssification such as to classified unknown category beat. This process is divided into three steps: data preprocessing, feature extraction and classification. Data preprocessing related to how the initial data prepared, in this case, we will reduce the baseline noise with cubic spline, then we cut the signal beat by beat using pivot R peak, while for the feature extraction and selection, we using wavelet algorithm. ECG signal will b!
e classified into four classes: PVC, LBBB, NOR, RBBB and one unknown category beat; using two following algorithms Back-Propagation and Fuzzy Neuro Learning Vector Quantization (FLVQ). The classification will be devided on two phase, at ones phase we will find best fitur for out system. At this phase we use known category beat, the best fiture for out study is 50 fitur, it is from wavelet decompotition level 3. Second phase is added unkown category beat on data test, the unknown category beat is not included on data train. Accuracy of FLVQ in our study is 95.5% for data without unknown category beat at testing step and 87.6% for data with unknown category beat.
Keywords: Ecg, Electrocardiogram, Arrhythmia, Flvq, Back-propagation, Wavelet transforms, Physionet, Mit-bih
EXTENSION of the file: .pdf
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