چکیده

Early diagnosis of heart disease plays an important role in treating the disease and preventing its dangerous consequences. One of the most common cardiac arrhythmias is premature ventricular contraction (PVC). In this research using the extraction of chaotic characteristics of the ECG signal ventricular premature contraction arrhythmia has been detected. Four important characteristics of chaotic systems used in this article are fractal dimension Lyapunov exponent correlation dimension and approximate entropy. Two supervised and unsupervised methods are used to detect arrhythmia. The nearest neighbor method was used as a supervised method for classification and the results were 93.01% accuracy and 90.01% precision. The second method is known as K-Means and was used as an unsupervised method for clustering and the results were 79.06% accuracy and 78.78% precision.

کليدواژه ها

Electrocardiogram premature ventricular contraction (PVC) Chaos KNN K-Means

کد مقاله / لینک ثابت به این مقاله

برای لینک دهی به این مقاله، می توانید از لینک زیر استفاده نمایید. این لینک همیشه ثابت است :

نحوه استناد به مقاله

در صورتی که می خواهید در اثر پژوهشی خود به این مقاله ارجاع دهید، به سادگی می توانید از عبارت زیر در بخش منابع و مراجع استفاده نمایید:
مهدیه قاسمی , 1401 , آنالیز آشوبناک و غیر خطی سیگنال ECG به منظور تشخیص آریتمی PVC , سومین کنفرانس پژوهش های کاربردی در مهندسی برق