A Novel Approach for Classifying Native Chinese and Malay Speaking Persons According to Cortical Auditory Evoked Responses

• Published in: The journal of international advanced otology Vol. 15; no. 1; pp. 87 - 93
• Main Authors: Ibrahim, Ibrahim Amer, Ting, Hua-Nong, Moghavvemi, Mahmoud
• Format: Journal Article
• Language: English
• Published: Turkey AVES 01.04.2019; Mediterranean Society for Otology and Audiology; European Academy of Otology and Neurotolog; The European Academy of Otology and Neurotology
• Subjects: Accuracy; Acoustic Stimulation - methods; Adult; Age; Algorithms; Audiometry, Pure-Tone - methods; Brain; Brain research; China - epidemiology; China - ethnology; Classification; Cognitive development; Cognitive science; Decomposition; Dementia; Diagnosis; Electroencephalography; Electroencephalography - instrumentation; Epilepsy; Ethnic Groups - statistics & numerical data; Ethnicity; Evoked Potentials, Auditory - physiology; Evoked responses; Fourier transforms; Hearing aids; Hearing loss; Hearing Loss - physiopathology; Hearing Loss, Sensorineural - diagnosis; Hearing Loss, Sensorineural - ethnology; Hearing Loss, Sensorineural - physiopathology; Humans; Infants; Language; Language thought relationship; Malaysia - ethnology; Male; Medical research; Medical screening; Middle Aged; Native languages; Noise - adverse effects; Original; Otolaryngology; Patients; Physiology; Signal processing; Speech Perception - physiology; Support Vector Machine - standards; Wavelet transforms; China; Malaysia
• ISSN: 1308-7649; 2148-3817
• DOI: 10.5152/iao.2019.4553

This study uses a new approach for classifying the human ethnicity according to the auditory brain responses (electroencephalography [EEG] signals) with a high level of accuracy. Moreover, the study presents three different algorithms used to classify the human ethnicity using auditory brain responses. The algorithms were tested on Malays and Chinese as a case study. The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy. The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%. The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.