Cardiac Arrhythmia Detection using Laplacian Eigenmaps and Wavelet Transform

سال انتشار: 1392
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 934

فایل این مقاله در 6 صفحه با فرمت PDF قابل دریافت می باشد

این مقاله در بخشهای موضوعی زیر دسته بندی شده است:

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این مقاله:

شناسه ملی سند علمی:

ICBME20_050

تاریخ نمایه سازی: 25 فروردین 1394

چکیده مقاله:

Cardiac Arrhythmia is the most common causes of death .These abnormalities of heart may cause sudden cardiac arrest or cause damage to heart. This paper demonstrates theapplication of the Laplacian Eigenmaps (LE) and wavelet transform to the task of cardiac arrhythmia detection. LaplacianEigenmaps is a dimension reduction method which combines the benefits of latent variable models with spectral manifold learningmethods-no local optimum, ability to unfold nonlinear manifolds, and excellent practical scaling to latent spaces of high dimensions.In this research, two dimensional wavelet transform was appliedon ECG signal, and then a modified Laplacian eigenmap mapping was used to reduce the final feature vector size. Finally, a feedforwardneural network is used to classify ECG signal beats. Proposed Laplacian eigenmap were compared with other commonused Laplacian Eigenmaps. Results authenticate superiority of the proposed modified Laplacian eigenmap. Also, some waveletfunctions were tried to see their effect on the overall results. In thisstudy, we achieved average positive predictive accuracy as 99.14%, total accuracy as 99.13% and average specificity as 99.83% on MIT-BIH arrhythmia database

کلیدواژه ها:

نویسندگان

Akbar Esmaeelzadeh

Electrical, Computer and Biomedical Engineering Department, Islamic Azad University, Qazvin Branch, Qazvin, Iran

Karim Faez

Department of Electric Engineering, Amirkabir University of Technology, Tehran, Iran

Ayyoob Jafari

Electrical, Computer and Biomedical Engineering Department, Islamic Azad University, Qazvin Branch, Qazvin, Iran