توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱ECG Arrhythmia Classification Using Evolved Multilayer Perceptron Neural Network
نویسنده(ها): ، ،
اطلاعات انتشار: کنفرانس ملی فن آوری، انرژی و داده با رویکرد مهندسی برق و کامپیوتر، سال
تعداد صفحات: ۶
This paper presents evolvable multilayer perceptron neural network (MLPNN) for electrocardiogram heartbeat classification based on a combination of morphological and temporal features. Data has been obtained from the MIT–BIH database to classify heartbeats to one of the five beat classes recommended by AAMI standard. For classification of the ECG signals, a hybrid training algorithm has been used and MLPNN weights have been optimized using genetic algorithm. Then back–propagation algorithm has been used as a local optimization operator. The main advantage of weight evolution by genetic algorithm is to simulate the learning process of a neural network, avoiding the drawbacks of the traditional gradient descent, such as back–propagation. Simulation results demonstrate high average detection accuracy of ECG signal patterns.<\div>

۲ECG signal classification using MLP neural network with hybrid PSO–BP training algorithm
نویسنده(ها): ، ،
اطلاعات انتشار: کنفرانس ملی فن آوری، انرژی و داده با رویکرد مهندسی برق و کامپیوتر، سال
تعداد صفحات: ۷
Electrocardiogram signals (ECG) are the important approach in heart activities monitoring and heart diseases diagnosis. In this paper evolvable multilayer perceptron neural network (MLPNN) is used for heartbeat pattern classification. Multilayer perceptron neural network (MLPNN) is formed of one or more hidden layers which can be trained by back propagation (BP) and\or evolutionary algorithms. MLPNN is trained by combination of particle swarm optimization (PSO) algorithm and back propagation (BP) algorithm, which is used to combine the PSO algorithm’s strong ability in global search and the BP algorithm’s strong ability in local search. MLPNN weights are optimized using particle swarm optimization algorithm. Heart signals are classified in five different classes by trained network according to association for the advancement of medical instrumentation. The inputs of neural network are features which have been extracted from ECG signals. The MIT–BIH arrhythmia database is used for simulation results. Classification accuracy of MLPNN for F signal 88.10%, N signal 96.49%, Q signal 73.68%, V signal 92.83% and S signal 95.93% is obtained. Simulation results show that proposed hybrid PSO–BP algorithm has better performance than BP algorithm in classification accuracy.<\div>
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