HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

Land Use/Cover Classification over Small Areas Using Conventional Digital Camcorder Imagery Based on Frequency-Based Contextual and Neural Network Classification Techniques

2013 ◽  
Vol 650 ◽  
pp. 658-663
Author(s):  
H.S. Lim ◽  
M.Z. Matjafri ◽  
K. Abdullah
Keyword(s):  
Neural Network ◽  
Land Cover ◽  
Digital Images ◽  
Peninsular Malaysia ◽  
Remotely Sensed Data ◽  
Ground Truth Data ◽  
Error Matrix ◽  
Classification Techniques ◽  
Neural Network Classification ◽  
Satellite Platform

An airborne survey was conducted to produce land cover/use maps. The feasibility of using a conventional digital camcorder to acquire remotely sensed data was investigated, and the imagery for land cover mapping using remote sensing technique was evaluated. The study area was the Universiti Sains Malaysia campus, Penang, located in Peninsular Malaysia. Digital images were taken from a low-attitude light aircraft, Cessna 172Q, at an average altitude of 2.4384 km above sea level. The use of a digital camcorder as a sensor to capture digital images is more economical compared with other airborne sensors. This technique is designed to overcome the problem of obtaining cloud-free photographs from a satellite platform in equatorial regions. Digital video imageries were taken in the red, green, and blue bands. A comparison between frequency-based contextual and neural network classification techniques for analyzing digital camcorder imagery is presented. Frequency-based contextual and neural network classification techniques were applied to the digital camera spectral bands (red, green, and blue) to extract the thematic information from the acquired scenes. The classified map was compared with the ground truth data, and accuracy was evaluated by an error matrix. Results indicate that a conventional digital camcorder can be used to acquire digital imageries for land cover/use mapping of a small area of coverage.

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