LSTAR Model for Sudden Cardiac Death Prediction

سال انتشار: 1397
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 332

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شناسه ملی سند علمی:

ICELE03_070

تاریخ نمایه سازی: 18 اسفند 1397

چکیده مقاله:

The unexpected changes in heart signals, followed by stroke and cardiac arrest, are one of the most common causes ofsudden deaths. Analysis of the recorded signals is the responsibility of the expert. The severe changes in cardiacfunction usually occur suddenly and almost at the moment of the incident. Therefore, the probability of error indiagnosis of electrocardiography (ECG) signal is high and in most cases the lifes of patients falls at death risk. The aimof this study is to predict the sudden cardiac death (SCD) by processing of ECG signals. In the proposed method, afterextracting the hearth rate variability (HRV) signal from the ECG signal, we use the discrete wavelet transform (DWT)to desompse it into time-frequency sub-bands. By using the logistic smooth transition autoregressive (LSTAR) model,the sub-bands of DWT transform are modeled and then the model parameters are considered as features. Kernelprincipal component analysis (KPCA) method is used to reduce the number of features and support vector machine(SVM) classifier is employed for classifying healthy and risky individuals. The obtained results demonstrate theefficiencty of the proposed method.

کلیدواژه ها:

DWT ، ECG signal ، LSTAR model ، sudden cardiac death ، support vector machine (SVM)

نویسندگان

Fariba Alizadeh

Department of Electrical & Computer Engineering, Urmia University, Urmia, Iran

Hashem Kalbkhani

Department of Electrical Engineering, Urmia University, Urmia, Iran

Mahrokh G Shayesteh

Department of Electrical Engineering, Urmia University, Urmia, Iran