NUST Institutions Library Catalogue Search for 'kw,wrdl: su-br:an:"119573"'

Effects of Broadband Noise on Sleep Quality /

By Zulfiqar, Zanib . . 78p. , Quality sleep is related to multiple valued performances in human life, including maintaining health and improving outcomes at work. Disturbance of sleep can increase the morbidity rate and can create multiple psychological and physiological issues. Broadband noises were hypothesized to mask disruptive noises and improve sleep quality. However, the efficacy of noise in improving sleep measures remained unclear due to the smaller sample size, uncontrolled noise environment, and sleep duration. The main aim of the study was to quantify sleep quality, sleep fragmentation, sleep latency, and relevant sleep factors by providing broadband noises and to assess whether white noise could be a non-pharmacological treatment for better sleep quality. For that purpose, sleep monitoring devices i.e., Fitbit Charge 4 and 5 as well as multiple questionnaires including PSQI, rMEQ, AASP, and St Mary questionnaire were used for data collection. Both hardware and questionnaire data were used to evaluate the effect of white noise on all the sleep factors i.e., sleep stages, sleep latency, total sleep duration, and overall sleep score. Also, environmental noise was measured by using decibel meters. Mostly healthy participants having the age of 25.07+/-4.66 for the questionnaire-based study and 24.25+/- 2.57 for hardware-based data were selected for data collection. Multiple statistical tests were performed on the collected data. ANOVA tests were performed on Fitbit’s data along with the post-hoc tests. Also, chi-square tests were conducted on questionnaire data. The p-value>0.0.5 in all tests suggested that no significant effect of white noise on sleep quality. The results concluded that white noise does not play a significant role in improving sleep quality. However, it can be used as a placebo effect for better sleep for specific persons. The results are based on both sleep monitoring devices and questionnaires with proper statistical analysis, having greater sample size and under controlled environmental noise. However, this work can be extended by changing the population of the experiment i.e., infants and ICU patients to check the effects of white noise. 30cm.

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Identifying Neurophysiological Correlates of Frontotemporal Dementia: Resting State EEG and Phase Synchronization Analysis /

By Ali, Salwa . . 123p. , The need to develop more efficient neuropsychological biomarkers is paramount in the identification of neurodegenerative diseases, tracking the efficiency of treatment and in an effort to avoid the huge financial cost required. While previous research utilizing neuroimaging techniques has pinpointed changes in functional connectivity (FC) as promising biomarkers for frontotemporal dementia (FTD), the constraints of cost and availability of neuroimaging equipment underscore the necessity for accessible alternatives. Electroencephalography (EEG) has emerged as a viable option due to its increasing robustness, wider usage, and affordability. To this end, the research focuses on a resting-state EEG data created from AD, FTD, and HC groups. Here ground data were obtained from nineteen leads using a clinical EEG device when the subjects were in a resting state and their eyes were closed. Another challenge was to follow strict standards for data quality and quality management for data quality to enhance consistency. It is a cross-sectional study, including data from MiniMental State Examination conducted on each participant, and tapes recorded from 20 AD patients, 20 FTD patients, and 20 HC. The Neuroimaging Data Structure (BIDS) format was utilized to present both preprocessed and raw EEG data. The foremost aim was to determine the Feasibility, Sensitivity, and Specificity of the preprocessed, feature extracted, time-efficient, and artifact reduced EEG-derived FC patterns as markers in FTD. Phase-lock values (PLVs) were computed among nineteen pairs of electrodes across five frequency bands using MATLAB and the Hilbert transform. Significant variations in brain connectivity were identified through statistical analyses. The study revealed significant differences in alpha and beta frequency patterns among the control, Alzheimer's, and FTD groups, particularly in frontal and temporal regions. These differences suggest alterations in neural activity associated with cognitive processing, potentially serving as biomarkers for distinguishing between the three groups. Alterations in beta frequency PLV were noted across various EEG pairs, indicating disruptions in neural communication and coordination. These alterations suggest xvi compensatory mechanisms or hyperactivity in frontal and prefrontal regions, alongside potential cognitive and motor deficits due to decreased PLV in central and temporal regions. While no statistically significant differences were observed in delta and theta frequency synchronization between groups, trends suggest potential regions of interest for further research, aligning with existing literature exploring neural oscillations in neurodegenerative diseases. Similarly, no significant differences were observed in gamma frequency synchronization between groups, indicating relatively preserved neural synchronization in this frequency range across control, Alzheimer's, and FTD patients. In summary, both Alzheimer's and FTD demonstrate significant reductions in alpha and beta frequency values, particularly in frontal and temporal regions, compared to healthy controls. These findings underscore the altered functional network topology in AD and FTD, offering valuable insights into the neural mechanisms underlying these conditions. The study's results contribute to the development of electrophysiological markers, potentially enhancing the clinical diagnosis and understanding of AD and FTD. The specificity and sensitivity of EEG-derived FC patterns highlight their potential as costeffective, accessible biomarkers for neurodegenerative disease. 30cm.

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Analysis of variations in brain states and impact of TES during behavioral task /

By Arshad, Sidra . . 88p. , This research targeted to investigate the effects of low-gamma High Definition transcranial alternating current (HD-tACS) at the left DLPFC and primary motor cortex in healthy individuals performing continuous attention task. We selected an openly accessible dataset from openneuro.org. Dataset includes within participant implementation of High-Definition tACS (HD-tACS), stimulating two cephalic regions (frontal & motor) with biphasic stimulation waveform (30 Hz) with a total 600 stimulation trials in 30 sessions. The physiological data i.e. EOG, ECG, along with EEG and behavioral data were being recorded over the course of two 70 and 70.5 minutes’ sessions. The demographic data were acquired before and after each session together with the wellness questionnaires. The participants were given two stimulation doses separately, with 20 stimulation trials per session. The within-subject results showed significant differences between the pre- and post-stimulation data (p-value <0.05) in the F30, in each frequency band. While in M30 session, there was a significant increase in alpha and beta oscillations (p-value < .05). The gamma oscillations were not altered by low-gamma tACS at M1, whereas the theta oscillations showed a significant decrease. The phase-locking values (PLV) of frontal channels decreased in theta, alpha, beta & gamma bands, suggesting a drop in the attention of participants with the onset of stimulation. Hence, the results indicate, that lowgamma HD-tACS over left DLPFC has the potential to inhibit attention and information processing. And the low-gamma HD-tACS can improve motor function over the left primary motor cortex. 30cm.

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Effect of Tactile Stimulation on Visual Memory Performance /

By Latif, Asma . . 80p. , The human eye continuously perceives information about surroundings to be processed and stored in memory so that it can be retrieved. Environmental factors may have positive and negative effects on memory performance and human cognitive processing. Many studies have addressed the effect of auditory circumstances on spatial tasks and visual memory performance. However, only a few studies highlighted the cross-modal interaction between vision for visual cue and touch for training of same visual pattern in tactile pattern. In addition, very little research has been conducted on the effect of tactile stimulation towards memorizing visual tasks. The main objective of this study is to investigate the effect of visuo-tactile stimulation on adult memory. Sixty-two subjects participated in this behavioral study having normal and corrected to normal vision. Participants are divided into two groups and each subject goes thorough Mini mental state examination and Edinburg handedness inventory. The visual assessment task consists of different shapes along with three-digit numbers. During the memorization period, visual assessment task was displayed on computer screen and tactile stimulation was delivered on index finger of the dominant hand of the participant. The participant was provided with an evaluation sheet containing shapes only. If the shape is paired with its corresponding number, then it was be considered correct. The p-value < 0.05 in visual assessment test showed a significant effect of tactile stimulation on visual memory performance. The findings of this study concluded that participants memorized the object number pair task better in the presence of tactile stimulation as compared to control/no stimulation. One of the conclusions of our work is that combing vision and touch sense may improve cognitive ability and may be provided to people during learning and remembering visual tasks. For future recommendations, heterogenous sample along with brain response can be studied. 30cm.

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Design and Development of an Air-Driven Posture Correction Device /

By Haris, Muhammad. . 129p. , The prevalence of postural kyphosis continues to rise across all demographics because of inactive lifestyles and extended screen time usage which has established itself as a major musculoskeletal issue. The current solutions including static braces and feedback-only wearables, fail to provide adaptability and comfort with active correction features. This restricts their ability to achieve longterm success and user adherence. The research introduces a new wearable air-driven posture correction device which combines real-time sensing with pneumatic actuation to treat flexible thoracic kyphosis. The proposed wearable tech device focuses on posture correction by integrating multiple features into one compact device. The system includes an MPU-6050 inertial measurement unit which tracks the user’s trunk in real time in six degrees of freedom. A microcontroller processes this data and determines if changes to posture exceed a calibrated angular threshold. If so, the system triggers a pneumatic actuation module within an orthopedic vest that has been altered for this purpose. This module includes butyl rubber chambers which are constrained but designed to inflate and mechanically stress the upper back by simulating the action of scapular retractor muscles. To measure the corrective force, force-sensitive resistors (FSRs) are placed where the actuators and the body interface. A closed-loop control system dynamically adaptive and responsive to real time conditions with sensor fusion guarantees timely action and feedback. The prototype was tested by means of both objective and subjective methods, with a full-scale experimental protocol involving 24 healthy participants. Data collected in this study included realtime pitch angle vs actuator force, and subjective user feedback through standardized ergonomic surveys such as the Borg CR10 scale, Corlett & Bishop discomfort map, and the System Usability Scale (SUS). Results indicate that the system successfully minimized thoracic pitch deviation while maintaining safe tactile force levels, achieving average corrective pressures of 7–12 kPa, resulting in notable postural enhancement. The system reliably attained enhancements in posture, surpassing 85% in 22 out of 24 participants. This work contributes a fully automated, textile-integrated pneumatic solution for posture correction, combining real-time sensing, adaptive actuation, and ergonomic design. The proposed xix system offers a replicable framework for intelligent musculoskeletal rehabilitation wearables and lays the groundwork for future closed-loop personalization strategies in postural health technologies. 30cm..

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Predicting Healthy and Pathological EEG Patterns with Machine Learning Algorithms /

By Abbas, Ghulam . . 85p. , Neurological disorders pose major global health challenge, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is important for the diagnosis and management of these disorders. However, manual interpretation of EEG recordings is not only time-consuming but also requires expertise. This problem is compounded by the scarcity of trained neurologists in the healthcare sector, especially in low- and middleincome countries. These limitations emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, have sparked significant interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of distinct path, Hybrid-CNNTransformer, for the automatic detection of abnormal raw EEG data. Through multiple ablation studies, we demonstrated the effectiveness of all parts of proposed model. The performance of our proposed model was evaluated using NMT Scalp EEG Dataset and achieved a high classification accuracy of 87.77%, which outperforms the original baseline model and other research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUH abnormal EEG Corpus V.2.0.0. (TUAB), without any hyperparameter tuning or adjustment. Furthermore, a Explainable AI (XAI) analysis confirmed that the model's decision-making process is not only transparent but also neurologically plausible. 30cm.

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