Ahmad Nasrul Norali
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Preferred name
Ahmad Nasrul Norali
Official Name
Ahmad Nasrul, Norali
Alternative Name
Norali, Ahmad Nasrul
Norali, A. N.
Norali, A. N.
Norali, Ahmad Nasrul Bin
Main Affiliation
Scopus Author ID
57210551450
Researcher ID
FQQ-2549-2022

Research Output
Now showing 1 - 2 of 2
  • Publication
    Human breathing classification using electromyography signal with features based on mel-frequency cepstral coefficients
    ( 2017-01-01)
    Ahmad Nasrul Norali
    ;
    Abdullah A.H.
    ;
    Zulkifli Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Nataraj S.K.
    Typical method on assessing the human breathing characteristics is based on measurements of breathing air parameters. Another possible method for human breathing assessment is through the analysis of respiratory muscles electromyography (EMG) signal. The EMG signal from different breathing task will be analyzed in order to determine the characteristics of the EMG signal pattern. Thus, feature extraction need to be done on the EMG signals. This paper will look into the use of Mel-Frequency Cepstral Coefficients (MFCC) in providing the features for EMG signal. Analysis is done using different data analysis frame sizes. EMG signal classification is done using K-Nearest Neighbour. Results shows that MFCC is a good feature extraction method for this purpose with classification accuracy exceeds more than 90% for data analysis frame size of 2000 ms, 4000 ms, 5000 ms and 10000 ms.
  • Publication
    Human breathing assessment using Electromyography signal of respiratory muscles
    ( 2017-04-05)
    Ahmad Nasrul Norali
    ;
    Abu Hassan Abdullah
    ;
    Zulkifli Zakaria
    ;
    Norasmadi Abdul Rahim
    ;
    Sathees Kumar Nataraj
    Breathing is one of the human physiological activities that catch the interest of researchers especially in the area of medical diagnosis and human physiological performance. Mostly, the measurement and data are in form of pressure and volume variables of air intake and outflow. However, using airflow pressure and volume require installment of certain sensor usually on subject's mouth which could discomfort the subject. Another possible method for assessing the breathing pattern is through human respiratory muscles, which are via electromyography signal. In this paper, experiment is done on acquiring the electromyography signal from four respiratory muscles namely sternocleidomastoid, scalene, intercostal muscle and diaphragm with subjects performing four different breathing tasks. Analysis-of-variance test has been done on the Electromyography (EMG) feature data of the four muscles for the four breathing tasks. Results of ANOVA analysis, show that the p-values has a significant different in the four breathing tasks for each muscle.
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