Real-time classification of Persian musical Dastgahs using artificial neural network

2012 ◽  
Author(s):  
H. Hajimolahoseini ◽  
R. Amirfattahi ◽  
M. Zekri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Artificial Neural ◽  
Real Time Classification

scholarly journals Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Fengying Ma ◽  
Jingyao Zhang ◽  
Wei Liang ◽  
Jingyu Xue
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Artificial Neural Network ◽  
Real Time ◽  
Classification Model ◽  
Ecg Monitoring ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Artificial Neural

Atrial fibrillation (AF), as one of the most common arrhythmia diseases in clinic, is a malignant threat to human health. However, AF is difficult to monitor in real time due to its intermittent nature. Wearable electrocardiogram (ECG) monitoring equipment has flourished in the context of telemedicine due to its real-time monitoring and simple operation in recent years, providing new ideas and methods for the detection of AF. In this paper, we propose a low computational cost classification model for robust detection of AF episodes in ECG signals, using RR intervals of the ECG signals and feeding them into artificial neural network (ANN) for classification, to compensate the defect of the computational complexity in traditional wearable ECG monitoring devices. In addition, we compared our proposed classifier with other popular classifiers. The model was trained and tested on the AF Termination Challenge Database and MIT-BIH Arrhythmia Database. Experimental results achieve the highest sensitivity of 99.3%, specificity of 97.4%, and accuracy of 98.3%, outperforming most of the others in the recent literature. Accordingly, we observe that ANN using RR intervals as an input feature can be a suitable candidate for automatic classification of AF.

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

Scandinavian test of artificial neural network for classification of myocardial perfusion images

2000 ◽  
Vol 20 (4) ◽  
pp. 253-261 ◽  
Author(s):  
Lindahl ◽  
Toft ◽  
Hesse ◽  
Palmer ◽  
Ali ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Myocardial Perfusion ◽  
Artificial Neural
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