Evaluation of Facial Paralysis Using Face Model
Banita1, Poonam Tanwar2
1Banita, Department of Computer Science, Lingaya’s University, Faridabad (Haryana), India.
2Dr. Poonam Tanwar, Department of Computer Science, Manav Rachana Institute of Research and Studies, Faridabad (Haryana), India.
Manuscript received on 18 February 2019 | Revised Manuscript received on 27 February 2019 | Manuscript published on 28 February 2019 | PP: 50-53 | Volume-8 Issue-3, February 2019 | Retrieval Number: C5680028319/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Facial paralysis is a common cause of uneven face dimensions. It is very challenging to diagnose the exact level of facial paralysis. The entity is less recognized in facial palsy and in literature as well. The aim of the study is to investigate the recovery rate of an individual suffering from facial paralysis. The Material and Methods was an observational, manual study done for a period of two years at PGIMS Rohtak. The cases having initial stroke were studied for Conduction velocity changes showing in the form of waveform for Cranial nerve. All the data were analyzed and studied by using fuzzification and MATLAB 7.Total 100 cases of facial paralysis studied for EMG changes. The average age was from 17 to 68 years. The men age group affected was from 17 to 26 years. The clinical representation was pain behind the ear and uneasiness. Other symptoms were pain behind the ear, nausea, stuffiness, changed sense of taste, dropping of mouth, difficulty to close eye etc. The compound motor action potential were recorded with the use of single pair and two pair electrodes in Pathology laboratory. Fuzzy model was used to analyze the system and to detect the exact recovery rate of facial paralyzed patient. If grading system is used to investigate the model followed by fuzzification will help to detect level of facial paralysis and also to detect the exact recovery rate.
Keywords: Classification of Paralysis, Conduction Velocity, Grading system, Fuzzification, 2D and 3D
Scope of the Article: Classification