Hybrid fuzzy logic committee neural networks for recognition of swallow acceleration signals.
Biological signals are complex and often require intelligent systems for recognition of characteristic signals. In order to improve the reliability of the recognition or automated diagnostic systems, hybrid fuzzy logic committee neural networks were developed and the system was used for recognition of swallow acceleration signals from artifacts. Two sets of fuzzy logic-committee networks (FCN) each consisting of seven member networks were developed, trained and evaluated. The FCN-I was used to recognize dysphagic swallow from artifacts, and the second committee FCN-II was used to recognize normal swallow from artifacts. Several networks were trained and the best seven were recruited into each committee. Acceleration signals from the throat were bandpass filtered, and several parameters were extracted and fed to the fuzzy logic block of either FCN-I or FCN-II. The fuzzified membership values were fed to the committee of neural networks which provided the signal classification. A majority opinion of the member networks was used to arrive at the final decision. Evaluation results revealed that FCN correctly identified 16 out of 16 artifacts and 31 out of 33 dysphagic swallows. In two cases, the decision was ambiguous due to the lack of a majority opinion. FCN-II correctly identified 24 out of 24 normal swallows, and 28 out of 29 artifacts. In one case, the decision was ambiguous due to the lack of a majority opinion. The present hybrid intelligent system consisting of fuzzy logic and committee networks provides a reliable tool for recognition and classification of acceleration signals due to swallowing.
Das
A
A
Department of Biomedical Engineering, Human Interface Laboratory, University of Akron, 4435-0302, Akron, OH, USA.
Reddy
N P
NP
Narayanan
J
J
D000054:Acceleration; D001696:Biomechanical Phenomena; D016022:Case-Control Studies; D003679:Deglutition / Q000502:physiology*; D003680:Deglutition Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted; D017143:Fuzzy Logic; D006801:Humans; D016571:Neural Networks, Computer; D012984:Software; D012985:Software Design