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   a feature selection system for improving ecg arrhythmia diagnosis using optimized mlp and goa  
   
نویسنده nemati bizhan ,talib dawood safaa
منبع اولين كنفرانس بين المللي ايده هاي نو در مهندسي برق - 1402 - دوره : 1 - اولین کنفرانس بین المللی ایده های نو در مهندسی برق - کد همایش: 02230-21684 - صفحه:0 -0
چکیده    Abstract the great majority of cardiac patients die less often when heart illnesses are detected early thanks to computer-aided diagnostic (cad) equipment. it is a difficult undertaking to identify heart irregularities since low variations in ecg signals may be difficult for the eye to precisely distinguish. this research proposes the goa-mlp classification model, an effective combination classification model utilizing grasshopper optimization algorithm (goa) and multi-layer perceptron (mlp) for ecg arrhythmia diagnosis. in this method, the neighbourhood component feature selection method is utilized in conjunction with the discrete wavelet transform and higher-order statistics to extract features. the proposed approach to categorizing the five classes of heartbeat categories has been contrasted with conventional neural networks and svm-rbf kernel functions. the accuracy of our suggested system s classification of arrhythmia classes is great (99.66%). the simulation results demonstrate that the goa-mlp approach has a higher classification accuracy than both the svm-rbf and the neural network classifier.
کلیدواژه ecg ,classification ,neighborhood component ,grasshopper optimization algorithm ,higher order statistics
آدرس , iran, , iran
پست الکترونیکی safaatalib90@gmail.com
 
     
   
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