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Ahnaf Rashik Hassan
Researcher at Bangladesh University of Engineering and Technology
Publications - 30
Citations - 2769
Ahnaf Rashik Hassan is an academic researcher from Bangladesh University of Engineering and Technology. The author has contributed to research in topics: Boosting (machine learning) & Feature extraction. The author has an hindex of 24, co-authored 29 publications receiving 1983 citations. Previous affiliations of Ahnaf Rashik Hassan include University of Toronto & North South University.
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Computer-aided sleep staging using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and bootstrap aggregating
TL;DR: A single-channel EEG based method for sleep staging using recently introduced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Bootstrap Aggregating (Bagging) is proposed and gives high detection accuracy for sleep stages S1 and REM.
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A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features
TL;DR: Spectral features in the TQWT domain can discriminate sleep-EEG signals corresponding to various sleep states efficaciously and is significantly better than the existing ones in terms of accuracy and Cohen's kappa coefficient.
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Automatic sleep scoring using statistical features in the EMD domain and ensemble methods
TL;DR: The proposed sleep staging algorithm is a data-driven and robust automatic sleep staging scheme that uses single channel EEG signal and its non-REM 1 stage detection accuracy is better than most of the existing works.
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Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting
TL;DR: The automated sleep scoring scheme propounded herein can eradicate the onus of the clinicians, contribute to the device implementation of a sleep monitoring system, and benefit sleep research.
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Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating
TL;DR: The seizure detection method proposed herein can alleviate the burden of medical professionals of analyzing a large bulk of data by visual inspection, speed-up epilepsy diagnosis and benefit epilepsy research.