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Item A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals(IEEE, 2025-06-23) Sakib, Nazmus; Islam, Md Kafiul; Faruk, TasnuvaAnxiety is a widespread mental health condition affecting millions globally, often resulting in significant emotional and physical symptoms. Accurate detection of anxiety levels is essential to provide timely interventions and prevent severe complications. This study explores a machine learning-based approach for multilevel anxiety classification among young adults using EEG signals. The GAD-7 screening tool was used to assess and categorize participants into different anxiety severity groups. EEG data was then recorded, processed, and segmented into 1, 3, and 5 second segments to evaluate the impact of segment duration on classification accuracy. Four channel combinations were tested for comparisons in performance. Feature extraction included eleven time and frequency domain features. The Bagged Trees classifier was applied to classify anxiety levels based on these features. The findings of this work show the potential of EEG-based systems as non-invasive tools for anxiety screening that could support more precise mental health diagnostics.Item Machine Learning Model for Computer-Aided Depression Screening among Young Adults Using Wireless EEG Headset(Hindawi, 2023-05-31) Islam, Md Kafiul; Faruk, Tasnuva; Sakib, NazmusDepression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.Item The Journey of Elastography: Background, Current Status and Future Possibilities in Breast Cancer Diagnosis(Elsevier, 2015) Faruk, Tasnuva; Islam, Md Kafiul; Arefin, Sams; Haq, Md ZahurulElastography is a promising way to assess tissue differences regarding stiffness or elasticity for what was historically assessed manually by palpation. Combined with conventional imaging modalities (eg, ultrasonography [US]), elastography can potentially evaluate the stiffness of a breast lesion and consequently help to detect malignant breast tumor from benign ones. Recent studies show that ultrasonographic elastography (USE) provides higher image quality compared with conventional B-mode US or mammography during breast cancer diagnosis, which eventually helps to reduce false-positive results (ie, increased specificity) and therefore is useful in avoiding breast biopsy. This article reviews the basics of elastography technique, classifications, diagnosis results obtained from clinical studies to date for differentiating malignant breast tumors from benign lesions, and its future possibilities. In addition, this article generalizes different elastography methods, modes, and associated imaging modalities in a simpler way and attempts to identify misconceptions and confusion related to existing elastography techniques. It also makes an effort to identify the gaps of information that need to be filled so that interested researchers can get an overall idea of elastography-based methods in a convenient way to carry out their research on breast elastography for prospective future applications, e.g. breast cancer diagnosis or even in intraoperative breast tumor localization.