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in typical disease diagnosis has been investigated. The real procedure of medical diagnosis which usually is employed by physicians was analyzed and converted to a machine implementable format. Then after selecting some symptoms of eight different diseases, a data set contains the information of a few hundreds cases was configured and applied to a MLP(More)
  • Sara Moein
  • 2010
In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is(More)
IJCS SI proceedi ings are cu urrently ind dexed by: Abstract Electrocardiogram (ECG) is an important biomedical tool for the diagnosis of heart disorders. However, the signal is susceptible to noise and it is essential to remove the noise especially when undertaking automated processing of the signal. In this paper, an intelligent approach based on moving(More)
In this paper, an automated approach for electrocardiogram (ECG) signal noise removing using artificial neural network is investigated. First, 150 of noisy heart signal are collected form MIT-BIH database. Then signals are transformed to frequency domain and cutoff frequency is calculated. Since heart signals are lowpass frequency, a Finite Impulse Response(More)