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Vikneswaran Vijean
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
Now showing
1 - 10 of 33
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PublicationPotential of pretreated palm kernel shell on pyrolysis( 2023-01-01)
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PublicationReview Article A Review of Optical Ultrasound Imaging Modalities for Intravascular Imaging( 2023-01-01)
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PublicationComparison between predicted results and built-in classification results for brain-computer interface (BCI) system( 2021-05-03)
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PublicationOptimum Binder Content of Asphaltic Concrete (ACW14) Mixture Incorporating Limestone( 2023-01-01)
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PublicationInvestigation on Medicated Drugs in ECG of Healthy SubjectsHeart diseases are now the leading cause of death worldwide, it is estimated that around 7 million patients who are living in developed countries, lost their lives due to diseases related to their cardiovascular system. In Malaysia, cardiovascular diseases represents one fifth of total deaths in the country in the past three decades. Currently patients need some sort of drugs that help them to stabilize and restore the regular patterns of their heart beat because if the patients cannot manage to restore the normal heart beat pattern, the undesired heart condition could lead life threatening situations. Advancement of biotechnology has enabled the creation of new medicated drugs to provide better treatment options. However, when this treatment option fails and there is a need to provide emergency intervention to the patients in hospitals, the medical experts often need to know about the patients' intake of any medications prior to hospital admittance for providing suitable treatments. Sometimes, this would be a difficult task as the patient might be admitted in semi-conscious or unconscious state. Therefore, this study focusses on identification of different medicated drugs usage through analysis of ECG data of the users. The data for the experiment was obtained from physionet library, which provides ECG data of subjects administered with a combination of Dofetilide, Mexiletine, lidocaine, Moxifloxacin and Diltiazem medicated drugs. The use of morphological and non-linear features derived from the ECG signals were able to provide prediction accuracy of 77.26% using SVM classifier.
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PublicationDiscrimination of healthy controls and selected visually impaired through visually evoked potentials( 2014)This thesis presents a digital signal processing based detection of healthy controls and selected visually impaired through visually evoked potentials (VEP). Visual impairment is a term used by ophthalmologist to describe any kind of vision loss, whether it's partial or total vision loss. Some of the conventionally used techniques for the investigation of vision impairments include fundoscopy imaging, ultrasound imaging, and manual inspection of retina. These techniques have several disadvantages such as poor quality of images produced by the ultrasound imaging, require experts, and are prone to error in manual inspection. The VEP provides an objective method for the diagnostics of vision impairments in patients. VEP is an electrical signal generated by the brain (Occipital Cortex) in response to a visual stimulus. By analyzing these responses, the abnormalities in the visual pathways of a person can be detected. The development of feature extraction and classification algorithms for investigation of vision impairments through VEPs however is still at an infancy level. Therefore, this study was carried out to investigate the time, frequency, and time-scale/frequency characteristics of the single trial transient VEPs, and propose an efficient feature extraction and classification algorithm for distinguishing the vision impairments. Four different feature extraction methods based on time, frequency, wavelet, and Stockwell transform were explored and statistical features were proposed for the VEP analysis. A new feature augmentation technique was proposed to enhance the variation of the data prior to the analysis. Three different feature reduction techniques were used to reduce the dimensional space of the features. Extreme learning machine, least square support vector machine and probabilistic neural networks were employed to evaluate the performance of the features in discriminating the vision impairments. Statistical analysis were used to demonstrate the significance of the preprocessed features, while performance measures such as sensitivity, specificity, positive predictivity, negative predictivity, and overall accuracy was considered for the evaluation of the classifiers. The dataset from two different experimental settings were used in the analysis. The first experiment was conducted to investigate the effect of different sizes of checkerboard stimulus to the resulting evoked responses while the second experiment was perpetrated to investigate the performance of the new colour fusioned checkerboard stimulus in elicitating reliable VEP responses. The experimental investigation elucidate that features derived from the VEP elicited by the proposed stimulus performed well in classifying the vision impairments. Promising 100% accuracy was achieved using the combinations of the proposed stimulus and feature extraction methods.
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PublicationIntelligent fall detection system using traditional and non-traditional machine learning algorithm based on MQTT( 2021-07-21)
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PublicationEarly Detection of Diabetic Foot Ulcers through Wearable Shoe Design( 2022-01-01)
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PublicationInvestigation on Body Mass Index Prediction from Face Images( 2021-03-01)
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PublicationIdentification of habitual smokers through speech signalSmoking is an addictive behavior and can result major health complications. Nowadays, many young adults tend to pick up this unhealthy habits which could potentially harm their health and affects the future workforce of the nation. Most of the habitual smokers have difficulties in ceasing this habit and require external assistance in the form of group therapy, medical interventions to quit smoking. Therefore, the main aim of this study is to investigate the speech signals of the subjects in an effort to identify the habitual smokers non-invasively. Through this detection, young smokers could be identified. Voice samples from VOice ICar fEDerico II from PhysioNet database were used for this study. Wavelet Packet Transform was used to extract non-linear features from the signals. Due to uneven data, ADASYN algorithm was used to produce a balanced dataset through synthetic data sampling. Subspace KNN and SVM classifiers were used for the investigations and classification accuracies up to 83.7% and 94% of AUC curve were observed from the analysis. The results suggests that the proposed method is effective in detecting habitual smokers, and can be considered as an early screening for smoking habits in young adults for targeted rehabilitation strategies.
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