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.

[1]  N. P. Reddy,et al.  Fractal analysis of surface EMG signals from the biceps. , 1997, International journal of medical informatics.

[2]  J. Logemann,et al.  Evaluation and treatment of swallowing disorders , 1983 .

[3]  N. P. Reddy,et al.  Noninvasive acceleration measurements to characterize the pharyngeal phase of swallowing. , 1991, Journal of biomedical engineering.

[4]  N. P. Reddy,et al.  Biofeedback therapy using accelerometry for treating dysphagic patients with poor laryngeal elevation: case studies. , 2000, Journal of rehabilitation research and development.

[5]  N. P. Reddy,et al.  Biomechanical measurements to characterize the oral phase of dysphagia , 1990, IEEE Transactions on Biomedical Engineering.

[6]  Roberto Battiti,et al.  Democracy in neural nets: Voting schemes for classification , 1994, Neural Networks.

[7]  Alice E. Smith,et al.  COMMITTEE NETWORKS BY RESAMPLING , 1998 .

[8]  N P Reddy,et al.  Toward classification of dysphagic patients using biomechanical measurements. , 1994, Journal of rehabilitation research and development.

[9]  T. Fearn.  The Jackknife , 2000 .

[10]  R Sorin,et al.  Electroglottographic evaluation of the swallow. , 1987, Archives of physical medicine and rehabilitation.

[11]  Galina L. Rogova,et al.  Combining the results of several neural network classifiers , 1994, Neural Networks.

[12]  M. Groher,et al.  Dysphagia: Diagnosis and Management , 1992 .

[13]  J. Logemann,et al.  Rehabilitation of oropharyngeal swallowing disorders. , 1994, Acta oto-rhino-laryngologica Belgica.

[14]  Susan E. Langmore,et al.  Endoscopic and Videofluoroscopic Evaluations of Swallowing and Aspiration , 1991, The Annals of otology, rhinology, and laryngology.

[15]  Jo Shapiro,et al.  Evaluation and treatment of swallowing disorders , 1992, Comprehensive therapy.

[16]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[17]  Barbara C. Sonies.  Instrumental Procedures for Dysphagia Diagnosis , 1991 .

[18]  Michael A. Crary,et al.  Surface Electromyographic Characteristics of Swallowing in Dysphagia Secondary to Brainstem Stroke , 1997, Dysphagia.

[19]  J. Palmer,et al.  Electromyography of the pharyngeal musculature: technical considerations. , 1989, Archives of physical medicine and rehabilitation.

[20]  Surekha Palreddy,et al.  Neural networks in computer-aided diagnosis classification of dysphagic patients , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Michael P. Karnell,et al.  Fundamental Frequency and Perturbation Measurement , 1991 .

[22]  Nathan Intrator,et al.  Optimal ensemble averaging of neural networks , 1997 .

[23]  J A Logemann.  Multidisciplinary management of dysphagia. , 1994, Acta oto-rhino-laryngologica Belgica.

[24]  S Suryanarayanan,et al.  A fuzzy logic diagnosis system for classification of pharyngeal dysphagia. , 1995, International journal of bio-medical computing.

[25]  V. Gupta,et al.  Computerized biofeedback systems for treating dysphagic patients for traditional and teletherapy applications , 1998, Proceedings. 1998 IEEE International Conference on Information Technology Applications in Biomedicine, ITAB '98 (Cat. No.98EX188).