Ahnaf Rashik Hassan

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In this paper, Dual Tree Complex Wavelet Transform (DTCWT) domain based feature extraction method has been proposed to identify left and right hand motor imagery movements from electroencephalogram (EEG) signals. After first performing auto-correlation of the EEG signals to enhance the weak brain signals and reduce noise, the EEG signals are decomposed into(More)
Traditional sleep scoring based on visual inspection of Electroencephalogram (EEG) signals is onerous for sleep scorers because of the gargantuan volume of data that have to be analyzed per examination. Computer-aided sleep staging can alleviate the onus of the sleep scorers. Again, most of the existing works on automatic sleep staging are multichannel(More)
BACKGROUND Automatic sleep scoring is essential owing to the fact that conventionally a large volume of data have to be analyzed visually by the physicians which is onerous, time-consuming and error-prone. Therefore, there is a dire need of an automated sleep staging scheme. NEW METHOD In this work, we decompose sleep-EEG signal segments using tunable-Q(More)
BACKGROUND AND OBJECTIVE Automatic sleep staging is essential for alleviating the burden of the physicians of analyzing a large volume of data by visual inspection. It is also a precondition for making an automated sleep monitoring system feasible. Further, computerized sleep scoring will expedite large-scale data analysis in sleep research. Nevertheless,(More)
BACKGROUND AND OBJECTIVE Wireless Capsule Endoscopy (WCE) can image the portions of the human gastrointestinal tract that were previously unreachable for conventional endoscopy examinations. A major drawback of this technology is that a large volume of data are to be analyzed in order to detect a disease which can be time-consuming and burdensome for the(More)
Obstructive Sleep Apnea (OSA) is traditionally diagnosed using multiple channel physiological signal. This often leads to incorrect apnea event detection and weakens the performance of OSA diagnosis. Furthermore, there is a dire need of an automatic OSA screening system in order to alleviate the burden of the clinicians and to make a portable home sleep(More)
BACKGROUND AND OBJECTIVE Epileptic seizure detection is traditionally performed by expert clinicians based on visual observation of EEG signals. This process is time-consuming, burdensome, reliant on expensive human resources, and subject to error and bias. In epilepsy research, on the other hand, manual detection is unsuitable for handling large data-sets.(More)
Automated sleep stage classification is essential for alleviating the burden of physicians since a large volume of data have to be analyzed per examination. Most of the existing works in the literature are multichannel based or yield poor classification performance. A single-channel based computerized sleep staging scheme that gives good performance is yet(More)