Saidatul Ardeenawatie Awang
Thumbnail Image
Preferred name
Saidatul Ardeenawatie Awang
Official Name
Saidatul Ardeenawatie, Awang
Alternative Name
Saidatul, Ardeenawatie
Awang, S. A.
Awang, Saidatul Ardeenawatie
Awang, Saidatul Ardeenaawatie
Awang, Saidatul Ardeenawatie Binti
Awang, Saidatul Ardeenawatiebinti
Saidatul, A.
Main Affiliation
Scopus Author ID
57205231792
Researcher ID
CCJ-6771-2022

Research Output

Filters

3 1
1
3
3
Reset filters

Settings
Now showing 1 - 3 of 3
  • Publication
    Comparison between predicted results and built-in classification results for brain-computer interface (BCI) system
    ( 2021-05-03)
    Ong Z.Y.
    ;
    Saidatul Ardeenawatie Awang
    ;
    Vikneswaran Vijean
    Brain-computer interface (BCI) system is a system of receiving information and transferring responses by communication between a computer and human brain. BCI system acts as assistive device to help the severe motor disabilities patients to live like a normal human being. Classification results used to validate the performances of BCI system. Several classification methods have been used in BCI system. However, previous researchers did not compare the classification results with predicted results. In this study, the predicted results were calculated from the questionnaire which collected from participants after completed the experiments. These predicted results were used to compare with the results from classification learner tool. The built-in classification methods included decision tree, support vector machine (SVM), k-nearest neighbor (KNN) and ensemble classifiers. Based on the results, the average difference of predicted results and built-in classification results for cubic SVM is the smallest which is 2.41% and 1.81% for motor imagery 1 and motor imagery 2 respectively. This finding shows that the cubic SVM classifier can detect the mistake that did by the subjects during the experiment.
  • Publication
    Non-invasive Detection of Ketum Users through Objective Analysis of EEG Signals
    ( 2021-11-25)
    Nawayi S.H.
    ;
    Vikneswaran Vijean
    ;
    Ahmad Faizal Salleh
    ;
    Rashid R.A.
    ;
    Planiappan R.
    ;
    Lim C.C.
    ;
    Fook C.Y.
    ;
    Saidatul Ardeenawatie Awang
    Ketum leaves are traditionaly used for treatment of backpain and reduce fatigue. However, in recent years people use ketum leaves to substitute traditional drugs as they can easily be obtained at a low cost. Currently, a robust test for ketum detection is not available. Although ketum usage detection via test strip is available, however, the method is possible to be polluted by other substances and can be manipulated. Brain signals have unique characteristics and are well-known as a robust method for recognition and disease detection. Thus, this study has been done to distinguish between ketum users and non-users via brain signal characteristics. Eight participants were chosen, four of whom are heavy ketum users and four non-users with no health issues. Data were collected using the eegoSports device in relaxed state. In pre-processing, notch filter and Independent Component Analysis (ICA) were used to remove artifacts. Wavelet Packet Transform (WPT) was used to reduce the large data dimension and extract features from the brain signal. To select the most significant features, T-Test was used. Support Vector Machine (SVM), K-Nearest Neighbour, and Ensemble classifier were used to categorize the input data into ketum users and non-users. Ensemble classifier was found to be able to predict the testing instances with 100% accuracy for open and closed eyes task with Teager energy and energy to standard deviation ratio as the features.
      28  1
  • Publication
    Effect of Mindfulness Meditation toward Improvement of Concentration based on Heart Rate Variability
    ( 2020-12-20)
    Lim Chee Chin
    ;
    Rosli F.F.B.
    ;
    Fook C.Y.
    ;
    Vikneswaran Vijean
    ;
    Saidatul Ardeenawatie Awang
    ;
    Palaniappan R.
    Mindfulness meditation is a type of therapy for a psychological cure like depression and anxiety that can significantly increase peoples' ability to concentrate and focus. Thus, this paper describes the analysis of mindfulness meditation effect toward concentration study in term of heart rate variability (HRV) signal. A memory test is used as a medium to test the concentration level of 20 participants, and their performance of the electrocardiogram signal was recorded. Peaks detection method and Pan-Tompkin method are used to extract the features like PQRST peaks and R-R interval from the ECG signal. Then, the extracted ECG signal features are classified using KNN method for before and after meditation during the memory test. The result shows that the effect of mindfulness meditation can improve the performance of participants' concentration level. The highest accuracy, sensitivity and specificity performance is obtained from the combination of all six features (P, Q, R, S, T peaks, and R-R interval value), which is 84.58 %, 88.77% and 80.39%. The analysis of memory test produces higher memory test score (69.2%), lesser miss selection (60.8%) and shorter taken time to complete the memory test (2.268 minutes) after mindfulness meditation compared to before mindfulness meditation. The R-R interval value represents heart rate variability (HRV) is important to prove that most of the participants are more relax and can handle their stress better after doing mindfulness meditation.
      5  17