Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction

Özgür TOMAK; Temel KAYIKÇIOĞLU

Makale detayları

Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction

Yazarlar:

Özgür TOMAK | Temel KAYIKÇIOĞLU

Yayıncı:

TUBITAK

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1555-1571

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000735160

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Arrhythmia, also known as dysrhythmia, is a condition   involving an irregular heartbeat. A problem in the heart may cause problems   in other organs, and as time passes, this will lead to more severe problems.   Arrhythmia must be detected at an early stage to prevent such a problem   occurring in the heart. Detection of arrhythmia from an electrocardiogram is   an easy method that does not need much equipment and does not harm the   patient. The purpose of this research is to find a faster and more accurate   system to classify nine classes of arrhythmia. The St. Petersburg Institute   of Cardiological Technics 12-lead arrhythmia database was used for training   and testing. Data were compressed and preprocessed (denoising, trend   elimination, baseline correction, and normalization) before being sent to   the system for feature calculation. The wavelet coefficients that displayed   the most significant effect on classification were chosen and used as   features. Standard deviation and variance were also added to the feature   set. Later, principal component analysis (PCA) was used to reduce the number   of features further. After deciding the features, the performance of the   basic classification methods and spiking neural network was checked to   determine whether there was a better classifier to be used for our research.   Tenfold cross-validation was applied to the training dataset. Bagged trees   were found to produce better results. The classifiers' performance was   tested by sensitivity, specificity, and accuracy.

Makale detayları

Bagged tree classification of arrhythmia using wavelets for denoising, compression, and feature extraction

Yazarlar: Özgür TOMAK | Temel KAYIKÇIOĞLU

Yayıncı: TUBITAK

Ana Başlıklar: Uygulamalı fizik

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1555-1571

Belge Tipi: Makale

Yayın Tarihi: 5/2018

Belge Dili: İngilizce

Document ID (Org No.): 2000735160

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Özgür TOMAK | Temel KAYIKÇIOĞLU

Yayıncı:

TUBITAK

Ana Başlıklar:

Uygulamalı fizik

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2018 Vol.26 Issue 3, pp.1555-1571

Belge Tipi:

Makale

Yayın Tarihi:

5/2018

Belge Dili:

İngilizce

Document ID (Org No.):

2000735160

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632