Authors :
Mohammed H.Almourish; Nishwan.A.Al-khulaidi; Amin A Mokbel; Ahmed Y A Saeed
Volume/Issue :
Volume 5 - 2020, Issue 6 - June
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/38yLUP9
DOI :
10.38124/IJISRT20JUN872
Abstract :
This paper will present an algorithm for
Heart Rate Variability HRV signals classifications. In
this algorithm we used Radon transform of binary
matrix of scatter-gram of heart rate HRV signals to
extract features of binary matrix. Artificial neural
network (ANN) technique with back-propagation
networks (BPN) was used for binary matrix features
classifications. Radon transform with 90 projections
was selected because it presented the best inverse Radon
transform that gave a closer image of the original
scatter-gram. The optimum numbers of neurons in the
hidden layer of BPN is 145 was obtained. Two databases
were formed, one for training and the second for testing
the accuracy of the BPN to recognize on types of heart
rate variability. The two database consist of HRV signal
pathologies, sympathetic activity, normal cardiac,
parasympathetic activity, arrhythmia, availability
problem with breath, existence of stress and the
composition of these pathologies. This algorithm present
the accuracy of diagnosis for sympathetic activity,
normal cardiac, parasympathetic activity, arrhythmia,
availability problem with breath and existence of stress
were 97,396%, 98,438%, 100%, 94,792%, 87,3265%
and 91,146% respectively.
Keywords :
Artificial Neural Network; Heart Rate Variability; Scatter-Gram; Radon Transform; Inverse Radon Transform; Back-Propagation Networks.
This paper will present an algorithm for
Heart Rate Variability HRV signals classifications. In
this algorithm we used Radon transform of binary
matrix of scatter-gram of heart rate HRV signals to
extract features of binary matrix. Artificial neural
network (ANN) technique with back-propagation
networks (BPN) was used for binary matrix features
classifications. Radon transform with 90 projections
was selected because it presented the best inverse Radon
transform that gave a closer image of the original
scatter-gram. The optimum numbers of neurons in the
hidden layer of BPN is 145 was obtained. Two databases
were formed, one for training and the second for testing
the accuracy of the BPN to recognize on types of heart
rate variability. The two database consist of HRV signal
pathologies, sympathetic activity, normal cardiac,
parasympathetic activity, arrhythmia, availability
problem with breath, existence of stress and the
composition of these pathologies. This algorithm present
the accuracy of diagnosis for sympathetic activity,
normal cardiac, parasympathetic activity, arrhythmia,
availability problem with breath and existence of stress
were 97,396%, 98,438%, 100%, 94,792%, 87,3265%
and 91,146% respectively.
Keywords :
Artificial Neural Network; Heart Rate Variability; Scatter-Gram; Radon Transform; Inverse Radon Transform; Back-Propagation Networks.
