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One of the common modalities for observing mental activity is electroencephalogram (EEG) signals. However, EEG recording is highly susceptible to various sources of noise and to inter-subject differences. In order to solve these problems, we present a deep recurrent neural network (RNN) architecture to learn robust features and predict the levels of the cognitive load from EEG recordings. Using a deep learning approach, we first transform the EEG time series into a sequence of multispectral images which carries spatial information.

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In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

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6 Views

In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

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12 Views

In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

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1 Views

In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

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2 Views

In the presented work, noisy ECG signal is decomposed into variational mode functions (VMFs) using variational mode decomposition (VMD) technique. The decomposed VMFs represents the different frequency band of the noisy ECG signal. The non-local similarity present in each VMFs were exploited using NLM estimation for effective ECG denoising. The two-stage VMD decomposition and NLM estimation process is performed on different set of VMFs at both stages. The proposed method is tested upon MIT-BIH Arrhythmia database.

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19 Views

In this study, we present a new 64-channel mobile EEG system (NeusenW, Neuracle Inc.), and compare it to a state-of-the-art wired laboratory EEG system and evaluate the EEG signal quality. Previous studies were only performed on seated participants in laboratory environments, and only a very limited number focus on motion conditions. In this study, we instead implemented experiments in standing, walking and running conditions.

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55 Views

Individuals with auditory neuropathy spectrum disorder (ANSD) or auditory processing disorders (APDs) often suffer from temporal processing deficits leading to degraded speech perception. The situation becomes worse in the presence of background noise. Evidence exists that the exaggeration of speech envelope may enhance intelligibility, although a comprehensive evaluation of envelope enhancement algorithms is lacking.

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Localization of exact positions of the fundamental heart sounds (FHS) is an essential step towards automatic analysis of heart sound phonocardiogram (PCG) recordings, the automatic segmentation allows for data-driven classification of heart pathological events. Current approach using probabilistic models such as hidden Markov models (HMMs) has improved accuracy of heart sound segmentation.

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Afternoon sleepiness in daily life reduces arousal level, performance, and so on. It has been cleared that short naps are effective to cancel the sleepiness. Sleep stage 2 is one of important factors about sleeping especially in short time nap. Sleep spindles are especially important hallmarks of sleep stage 2. Therefore, it is necessary to find a spindle for analysis in sleep stage 2.

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