Hasliza A Rahim @ Samsuddin
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Preferred name
Hasliza A Rahim @ Samsuddin
Official Name
A Rahim @ Samsuddin, Hasliza
Alternative Name
A. Rahim, Hasliza
Rahim, Hasliza A.
Rahim Samsuddin, H. A.
Rahim, Hasliza Abdul
Rahim, Hazliza A.
Rahim, H. A.
Rahim, H.
Hasliza,
Rahim H, Abd
RahimAtSamusuddin, Hasliza A.
A Rahim, Hasliza
Rahim At Samsuddin, Hasliza A.
ARahim, H.
Rahim Samsuddin, A. Hasliza
Rahim At Samsuddin, H. A.
Rahim, Hasliza Abd
Rahim, Hasliza
Rahim At Shamsuddin, H. A.
Main Affiliation
Scopus Author ID
57202496362
Researcher ID
ABE-3328-2020

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  • Publication
    A novel unsupervised spectral clustering for pure-tone audiograms towards hearing aid filter bank design and initial configurations
    ( 2022-01-01)
    Elkhouly A.
    ;
    Allan Melvin Andrew
    ;
    Hasliza A Rahim @ Samsuddin
    ;
    Abdulaziz N.
    ;
    Abdulmalek M.
    ;
    Mohd Najib Mohd Yasin
    ;
    Muzammil Jusoh
    ;
    Thennarasan Sabapathy
    ;
    Siddique S.
    The current practice of adjusting hearing aids (HA) is tiring and time-consuming for both patients and audiologists. Of hearing-impaired people, 40–50% are not satisfied with their HAs. In addition, good designs of HAs are often avoided since the process of fitting them is exhausting. To improve the fitting process, a machine learning (ML) unsupervised approach is proposed to cluster the pure-tone audiograms (PTA). This work applies the spectral clustering (SP) approach to group audiograms according to their similarity in shape. Different SP approaches are tested for best results and these approaches were evaluated by Silhouette, Calinski-Harabasz, and Davies-Bouldin criteria values. Kutools for Excel add-in is used to generate audiograms’ population, annotated using the results from SP, and different criteria values are used to evaluate population clusters. Finally, these clusters are mapped to a standard set of audiograms used in HA characterization. The results indicated that grouping the data in 8 groups or 10 results in ones with high evaluation criteria. The evaluation for population audiograms clusters shows good performance, as it resulted in a Silhouette coefficient >0.5. This work introduces a new concept to classify audiograms using an ML algorithm according to the audiograms’ similarity in shape.