Browsing by Author "Ahmad Anwar Zainuddin"

Muscle spasticity is a condition that occurs in patients with neurological disorders when their muscles are stiff, tight, and resistant to stretching. The current assessment method, relying on the subjective judgment of therapists using the Modified Ashworth Scale (MAS), introduces variability that may affect the rehabilitation process. In addition, many existing computational models are not aligned with clinical standards such as MAS, limiting their practical adoption in clinical settings. They also often overlook direction-specific movement phases and fail to distinguish muscle responses across different axes and muscle groups. To address the limitations, this research aimed to develop a quantitative assessment method by muscle spasticity characteristics based on mechanomyography (MMG) signals and MAS levels, utilising machine learning techniques. A Quantitative Spasticity Assessment Technology (QSAT) platform has been developed which consists of two sensors that were tri-axial accelerometer mechanomyography (ACC-MMG) functioning in measure acceleration of biceps and triceps muscle contraction and potentiometer to measure the angular position of forearm during flexion and extension movement. A comprehensive investigation was conducted to assess muscle spasticity level by recording ACC-MMG signals from patients' forearm musculature during flexion and extension movements using QSAT platform. A total of 30 patients with neurological disorders were classified into five MAS levels (0, 1, 1+, 2, and 3), along with 10 healthy subjects serving as a baseline group. The pre-processed data comprised 48 extracted features from ACC-MMG signals along the x, y, and z axes for both flexion and extension movements of the biceps and triceps. These features corresponded to the longitudinal, lateral, and transverse muscle orientations. For both flexion and extension movements, machine learning models were trained using the selected subset of 25 significant features and the full set of 48 features respectively, with performance comparisons made to identify the most effective approach. Various machine learning models algorithms, including Linear Discriminant Analysis (LDA), Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbour (KNN), were tested. The KNN-based classifier demonstrated the highest performance using a 90% training and 10% testing data split surpassing the performance of other classifiers. Specifically, at k = 15 using Euclidean distance, the KNN achieved an accuracy of 91.29% for flexion using the significant features, with corresponding precision, recall, and F1-score of 91.64%, 91.25%, and 91.47%, respectively. For extension, the same configuration resulted with 96.30% for extension using the full feature set, with precision, recall, and F1-score of 96.53%, 96.30%, and 96.33%, respectively. These results indicate high classification performance, with minimal false positives or false negatives, particularly in distinguishing between different MAS levels. This research suggests that the muscle characteristic model embedded in the QSAT can serve as a standardised and objective assessment tool for measuring the spasticity level of the affected limb using computational method, leading to support clinical evaluations and enabling more effective rehabilitation strategies.

Dengue illness is an infectious tropical disease, transmitted by Aedes mosquitos which poses a serious health threat to the tropical world. Malaysia is among the seriously affected countries with a rapid increase of 133% of new dengue fever cases and 158% of deaths from 2013 to 2016. Currently, the dengue detection methods such as virus isolation, polymerase chain reaction (PCR), and Enzyme-linked immunosorbent assay (ELISA) are popularly used due to its good sensitivities. However, they require long incubation periods of up to 48 hours with tedious processing steps, thus causing the patients to be diagnosed with dengue when they are at a late stage. To overcome these issues, the usage of biosensors have been proposed as an alternative technology for rapid detection of dengue. The major electrical biosensing strategies, including electrochemical, mass, and optical measurement are widely used because of their high sensitivity and real-time measurements. Among them, optical biosensor has the highest sensitivity, but this method is not applicable as point-of-care (PoC) devices due to its complex and expensive equipment. Alternatively, the use of electrochemical and mass detection method such as electrochemical impedance spectroscopy (EIS) and quartz crystal microbalance (QCM) is highly recommended for detection of dengue due to its simplicity, cost-effectiveness and portability. In this work, an integrated electrochemical quartz crystal microbalance (IEQCM) was successfully developed for early dengue detection (NS1 antigen). This IEQCM sensor has a dual-function working electrode that enables in-situ measurements of both EIS and QCM. When the biological target attaches to the working electrode, the QCM detects shifts in resonance frequency due to dampened acoustic waves propagating through the quartz sensor. The same device can also detect impedance changes due to surface reaction via EIS measurement. The design of the biosensor is first simulated using COMSOL and its design parameters such as radius of working electrodes (rWE) and gap (g) between electrodes are optimized. Experimental measurements were conducted using the fabricated IEQCM to validate these simulation results. The best design parameters of sensors were found to have an array of three, 10MHz IEQCM biosensor on a single substrate with rWE of 2000µm which exhibits highest quality factor (Q-factor = 2.77 x 104). All the QCM sensors operate at a frequency of 9.79 MHz ± 1kHz. For EIS sensor, the optimal g between electrodes was found to be 70µm since it produces the highest current density (based on simulation) to enhance electro-migration of ions at sensor interfaces. The dengue NS1 measurement results, suggested that the EIS measurement showed the higher instrumental sensitivity (12.15 percentage decade-1) than QCM measurements (9.87 percentage decade-1) but the QCM measurement provided higher assay sensitivity in this work. This work has shown that IEQCM has the potential to provide rapid, early and accurate dengue detection in point-of-care settings with higher sensitivity and selectivity.