NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Signal detection.")'

Intelligent and Biosensors (E-Book)

By Vernon S. Somerset. India In-Teh 2010 . 396 p;

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Recent Advances in Biomedical Engineering (E-Book)

By Dr Ganesh R Naik. India In-Teh 2009 . xii,660 P;

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TH-SOF-1233-Seizure Detection from the Time-Frequency Based Multichannel Newborn EEG Signal through the Application of Advanced Noise Filtering and Classification Methods

By MOIZ YUSAF, . Islamabad NUST College of EME 2016

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PR-SOF-1588- DEVELOPMENT OF FUNCTIONAL NEAR INFRARED SPECTROSCOPY (FNIRS) BASED SYSTEM FOR DETECTION OF DISTINCTIVE BRAIN SIGNAL PATTERNS

By NC Zargam Ullah NC Muhammad Usama NC Kashif Ali . ISLAMABAD NUST COLLEGE OF EME 2017

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Microwave power amplifier design with MMIC modules /

By Hausman, Howard,. Boston Artech House 2018 . xxii, 366 pages ; , Minimal Level Cataloging Plus. 27 cm.. 9781630813468

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Through Wall Image Enhancement /

By Riaz, Muhammad Mohsin. Rawalpindi, MCS (NUST), 2013 . xxii,100 -p,

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Brain-Computer Interface for Mental State Detection of Drivers /

By Arif, Saad . . 169p. , Background: Each year millions of vehicles suffer crashes on the roads globally due to deteriorated mental state of the drivers during driving tasks which result in higher casualties. Drowsy driving is the leading cause of high fatality rate which is instigated due to sleep deprivation, fatigue, and anxiety, etc. Vehicular, and driver’s behavioral data-dependent systems detect the drowsiness after its onset when an accident is more likely, and they are also subject to false identification. The drowsy mental state of drivers must be detected earlier for in-time warning to avoid fatal losses, and also the system must be less intrusive, and adaptable for normal driving tasks. Detection systems using physiological signals from human organs are comparatively underexplored in which bodily states of the subject can be identified earlier and with more reliability. It is postulated that all the bodily states are primarily originated from the human brain which could be a promising region to detect drowsiness at an earlier stage. Among many physiological signals, electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) are chosen for this study because they are proved to be more portable, easy to use, non-invasive, and reliable brain modalities for human mental state detection. Aim and Objectives: This study aimed to design a passive brain-computer interface (pBCI) scheme for the earlier detection of driver drowsiness with minimum intrusion into the driving task. Design objectives for the pBCI scheme were to select the channel of interest (COI), online detection with a shorter time window, minimum recalibration and setup time, and development of a widely applicable standard as an inter-subject transfer framework (iSTF) to cater to the inter-subject variability. All these objectives lead to such a pBCI system that is readily available for any subject, anytime, easy to use with minimal design, and yet detecting the drowsiness correctly at an earlier stage to avoid life losses. Methodology: Multichannel EEG and fNIRS brain signals from anterior, posterior, and lateral brain regions of sleep-deprived drowsy subjects were acquired during the simulated driving task for post hoc analysis. Initial pBCIschemes used labeled EEG data acquired from prefrontal (PFC), frontal, and occipital cortices for extracting the eight spectral, and eight temporal xvi features of EEG signals. Seven supervised machine learning classifiers were used to do the cross-validated binary classification of drowsy, and alert brain states. Initial design only achieved a few objectives and generated the need to use different modalities to meet all the requirements. The final pBCI scheme used labeled fNIRS data acquired from PFC and dorsolateral prefrontal cortices (DLPFC) for extracting the six cerebral oxygen regulation (CORE) and three hemodynamic signal features. CORE states of wakefulness and non-rapid eye movement (NREM) sleep stages were used to design a novel standard framework for wide applicability, and sleep stage classification using the vector phase analysis (VPA) approach. VPA was used for classifying microsleep/lapse, and drowsiness detection was done using the proposed brain hemodynamic patterns. Results: 𝛿, 𝜃, 𝛼, 𝛽 band powers as EEG spectral features achieved 82% accuracy in 10 s detection window. Signal skewness, variance, mean, peak as EEG temporal features achieved 87.2% accuracy in 1 s detection window. Ensemble classifier declared F8 as COI for earlier drowsiness detection using both the EEG pBCI schemes. Only the objectives of the short detection window and COI were achieved with the initial designs. In the final design with fNIRS, the novel VPA features: CORE vector gradients, achieved 94.1% accuracy in 5 s detection window for NREM sleep stage classification using ensemble classifier with the least computation time of 44 ms. Precise spatial localization of fNIRS declared AF8 position in right DLPFC as COI. The novel sleep stages-based threshold criteria along with VPA were crossvalidated as a standard iSTF for online microsleep detection with the least recalibration and setup time. Feature selection and achieved results were validated with various statistical significance tests. All the design objectives were attained with the fNIRS-based pBCI scheme. Conclusion: The aim to detect the driver's drowsiness earlier with minimum intrusion into the driving task, is accomplished. The presented fNIRS-based adaptive pBCI scheme is readily available for any subject, anytime, easy to wear with minimal ergonomic design, capable of real-time, correct, and early detection of the driver drowsiness to lessen the life losses in vehicular driving scenarios. The recommended research directions will surely justify, improve, and broaden the application horizons of the presented design 30cm..

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Multi-Class Classification of ECG Data for Comprehensive Cardiac Abnormality Detection Through Machine Learning /

By Nayyab, Rida. . 81p. , Cardiovascular diseases are considered the major cause of death worldwide surpassing cancer. However, despite the broad category of diseases, research has been limited to binary classification i.e. normal and abnormal class leaving behind the accurate classification of specific diseases that affect the ECG waveform. PTB – XL database offers a wide variety of ECG records, but little research is dedicated to extracting morphological features for multi-class classification. Therefore, the paper used the open database to filter the ECG signal records having single unique labels and pre-processed them using the Butterworth bandpass filter and DWT db8. The Bandpass filter corrected baseline wander and reduced noise however, a high signal-to-noise ratio was achieved after applying 8-level DWT. The processed signals were fed into the Pan-Tompkins algorithm to extract R peaks. These peaks served as a baseline to identify other morphological features i.e. P-QRS-T intervals and amplitudes. These extracted features were labelled into 1 normal and 4 abnormal classes. There was a class imbalance in the dataset that could cause bias while training models. Therefore, SMOTE-NC was applied to upsample the dataset. The new dataset was split into the training set and the testing set. These sets were given as inputs to CNN and DNN models for a 5-fold loop. The performance was evaluated for both models using metrics like F1 score, recall, precision and accuracy. The CNN model achieved a mean accuracy of 81% whereas the mean accuracy for DNN was 84%. It was also noted that among the 5 classes, HYP was consistently being classified accurately at 98%. 30cm.

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Automated Schizophrenia Detection Using Multi-Channel EEG Signals from Brain Activity/

By Wazir, Danish Khan.

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Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

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