Vikneswaran Vijean
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Preferred name
Vikneswaran Vijean
Official Name
Vijean, Vikneswaran
Alternative Name
Vikneswaran
Vikneswaran, V.
Vijean, V.
Main Affiliation
Scopus Author ID
54785424700
Researcher ID
D-2539-2015

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  • 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  25
  • Publication
    DT-CWPT based Tsallis Entropy for Vocal Fold Pathology Detection
    ( 2020-10-26)
    Farah Nazlia Che Kassim
    ;
    Vikneswaran Vijean
    ;
    Muthusamy H.
    ;
    Zulkapli Abdullah
    ;
    Rokiah Abdullah
    ;
    Palaniappan R.
    The study of voice pathology has become one of the valuable methods of vocal fold pathology detection, as the procedure is non-invasive, affordable and can minimise the time needed for the diagnosis. This paper investigates the Dual-Tree Complex Wavelet Packet Transform (DT-CWPT) based Tsallis entropy for vocal fold pathology detection. The proposed method is tested with healthy and pathological voice samples from Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and Saarbruecken Voice Database (SVD). A pairwise classification using k-Nearest Neighbors (k-NN) classifier gave 91.59% and 85.09% accuracy for MEEI and SVD database respectively. Higher classification accuracy of 93.32% for MEEI and 85.16% for SVD database achieved using Support Vector Machine (SVM) classifier.
      4  48