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Fulltext A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

Awais M, Badruddin N, Drieberg M

Sensors (Basel), 2017 Aug 31;17(9).
PMID: 28858220 DOI: 10.3390/s17091991

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system's performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.
Autosomal STR polymorphism in Sikh population of Peninsular Malaysia

Teck TC, Kook SC, Badruddin N, Panneerchelvam S, Norazmi MN

J Forensic Sci, 2005 Sep;50(5):1223-4.
PMID: 16225236 DOI: 10.1520/JFS2005156
Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach

Al-Shargie F, Tang TB, Badruddin N, Kiguchi M

Med Biol Eng Comput, 2018 Jan;56(1):125-136.
PMID: 29043535 DOI: 10.1007/s11517-017-1733-8

Mental stress has been identified as one of the major contributing factors that leads to various diseases such as heart attack, depression, and stroke. To avoid this, stress quantification is important for clinical intervention and disease prevention. This study aims to investigate the feasibility of exploiting electroencephalography (EEG) signals to discriminate between different stress levels. We propose a new assessment protocol whereby the stress level is represented by the complexity of mental arithmetic (MA) task for example, at three levels of difficulty, and the stressors are time pressure and negative feedback. Using 18-male subjects, the experimental results showed that there were significant differences in EEG response between the control and stress conditions at different levels of MA task with p values
Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, et al.

Australas Phys Eng Sci Med, 2015 Mar;38(1):139-49.
PMID: 25649845 DOI: 10.1007/s13246-015-0333-x

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task--Raven's advance progressive metric test and (2) the EEG signals recorded in rest condition--eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53-3.06 and 3.06-6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.
Localized N20 Component of Somatosensory Evoked Magnetic Fields in Frontoparietal Brain Tumor Patients Using Noise-Normalized Approaches

Elaina NS, Malik AS, Shams WK, Badruddin N, Abdullah JM, Reza MF

Clin Neuroradiol, 2018 Jun;28(2):267-281.
PMID: 28116447 DOI: 10.1007/s00062-017-0557-0

PURPOSE: To localize sensorimotor cortical activation in 10 patients with frontoparietal tumors using quantitative magnetoencephalography (MEG) with noise-normalized approaches.
MATERIAL AND METHODS: Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI). The obtained coordinates were compared between groups. Finally, we statistically evaluated the N20m parameters across hemispheres using non-parametric statistical tests.
RESULTS: The N20m sources were accurately localized to Brodmann area 3b in all members of the control group and in seven of the patients; however, the sources were shifted in three patients relative to locations outside the primary somatosensory cortex (SI). Compared with the affected (tumor) hemispheres in the patient group, N20m amplitudes and the strengths of the current sources were significantly lower in the unaffected hemispheres and in both hemispheres of the control group. These results were consistent for both dSPM and sLORETA approaches.
CONCLUSION: Tumors in the sensorimotor cortex lead to cortical functional reorganization and an increase in N20m amplitude and current-source strengths. Noise-normalized approaches for MEG analysis that are integrated with MRI show accurate and reliable localization of sensorimotor function.
Intervening factors between risk of violence and aggressive behaviours among forensic inpatients: a scoping review

Ahmad Badruddin N, Roseliza-Murni A, Kamaluddin MR, Ahmad Badayai AR, Munusamy S

BMC Psychol, 2024 Mar 15;12(1):155.
PMID: 38491550 DOI: 10.1186/s40359-024-01649-1

BACKGROUND: Risk of violence is closely associated with aggression propensity. However, there is a lack of research to explain the mechanisms behind this association, especially among the patients of forensic secure facilities. This review aimed to identify and synthesize the available literature concerning the intervening factors (mediating or moderating factors) in the relationship between the risk of violence and aggressive behavior in forensic secure facilities.
METHODS: Two electronic academic databases were searched: Scopus and Web of Science (WoS) using specific keywords as search terms derived from the PCC framework with no specific time limit. The search strategy was developed based on the JBI Manual for Evidence Synthesis and utilised the PRISMA-ScR guidelines. Data on the risk of violence, intervening factors, and aggressive behavior were extracted from the included studies. Further analysis was performed whereby similar data were grouped and synthesised together.
RESULTS: The initial search produced 342 studies. However, only nine studies fulfilled the inclusion criteria. The nine studies included 1,068 adult forensic inpatients from various psychiatric hospitals. Only mediation studies reported significant mechanisms of influence between the risk of violence and aggressive behavior. It is postulated that the human agency factor may be the underlying factor that influences a person's functioning and the subsequent series of events between the risk of violence and aggression.
CONCLUSIONS: In light of the paucity of evidence in this area, a generalised conclusion cannot be established. More studies are warranted to address the gaps before conclusive recommendations can be proposed to the relevant stakeholders.
Fulltext Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning

Mohd Nazri AK, Yahya N, Khan DM, Mohd Radzi NZ, Badruddin N, Abdul Latiff AH, et al.

Front Neurosci, 2024;18:1524513.
PMID: 39867451 DOI: 10.3389/fnins.2024.1524513

INTRODUCTION: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol abuse alters brain structure, leading to alcohol use disorder (AUD), a condition requiring early diagnosis for effective management. Current diagnostic methods, primarily reliant on subjective questionnaires, could benefit from objective measures.
METHOD: The study proposes a novel EEG-based classification approach, focusing on effective connectivity (EC) derived from resting-state EEG signals in combination with support vector machine (SVM) algorithms. EC estimation is performed using the partial directed coherence (PDC) technique. The analysis is conducted on an EEG dataset comprising 35 individuals with AUD and 35 healthy controls (HCs). The methodology evaluates the efficacy of connectivity features in distinguishing between AUD and HC and subsequently develops and assesses an EEG classification technique using EC matrices and SVM.
RESULT: The proposed methodology demonstrated promising performance, achieving a peak accuracy of 94.5% and an area under the curve (AUC) of 0.988, specifically using frequency bands 29, 36, 45, 46, and 52. Additionally, feature reduction techniques applied to the PDC adjacency matrices in the gamma band further improved classification outcomes. The SVM-based classification achieved an accuracy of 96.37 ± 0.45%, showcasing enhanced performance through the utilization of reduced PDC adjacency matrices.
DISCUSSION: These results highlight the potential of the developed algorithm as a robust diagnostic tool for AUD detection, enhancing precision beyond subjective methods. Incorporating EC features derived from EEG signals can inform tailored treatment strategies, contributing to improved management of AUD.