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Formulation of a Novel Classification Indices for Classification of Human Hearing Abilities According to Cortical Auditory Event Potential signals

  • Ibrahim Amer Ibrahim
  • Hua-Nong Ting
  • Mahmoud MoghavvemiEmail author
Research Article - Electrical Engineering
  • 13 Downloads

Abstract

The classification of brain response signals as per human hearing ability is a complex undertaking. This study presents a novel formulated index for accurately predicting and classifying human hearing abilities based on the auditory brain responses. Moreover, we presented five classification algorithms to classify hearing abilities [normal hearing and sensorineural hearing loss (SNHL)] based on different auditory stimuli. The brain response signals used were the electroencephalography (EEG) evoked by two auditory stimuli (tones and consonant vowels stimulus). The study was carried out on Malaysian (Malay) citizens with and without normal hearing abilities. A new ranking process for the subjects’ EEG data and as well as ranking the nonlinear features will be used to obtain the maximum classification accuracy. The study formulated classification indices (CVHI, PTHI&HAI); these classification indices classify human hearing abilities based on the brain auditory responses using features in its numerical values. The K-nearest neighbor and support vector machine classifiers were quite accurate in classifying auditory brain responses for brain hearing abilities. The proposed indices are valuable tools for classifying brain responses, especially in the context of human hearing abilities.

Keywords

ElectroEncephaloGram (EEG) Cortical auditory evoked potentials (CAEPs) Regression Empirical mode decomposition (EMD) Classification Cross-validation 

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Notes

Acknowledgements

This research was funded by the University Malaya Research Grant UMRG RP016D-13AET. The authors express their gratitude to all volunteers participated and contributed in conducting the experiment. Furthermore, the authors were high appreciated the help from the staff of the ENT department (UMMC), Pusat Perubatan Universiti Malaya (PPUM). Especially, Dr. Rashidah Daud (Audiologist) and Mr. Marzuki Bin Che (Medical Technician).

Author Contributions

Ibrahim Amer Ibrahim and Dr. Hua-Nong Ting conceived and designed the experiments; Ibrahim Amer Ibrahim, Prof. Dr. Mahmoud Moghavvemi and Dr. Hua-Nong Ting performed the experiments; Ibrahim Amer Ibrahim, Prof. Dr. Mahmoud Moghavvemi and Dr. Hua-Nong Ting contributed reagents/materials/analysis tools; Ibrahim Amer Ibrahim and Dr. Hua-Nong Ting wrote the paper.

Compliance with Ethical Standards

Conflicts of interest

The authors declare no conflict of interest.

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Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Department of Electrical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  3. 3.Department of Biomedical Engineering, Al-Khwarizmi College of EngineeringUniversity of BaghdadBaghdadIraq
  4. 4.Center of Research in Applied Electronics (CRAE), Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  5. 5.University of Science and CultureTehranIran

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