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Parkinson’s disease (PD) produces several speech impairments in the patients. Automatic classification of PD patients is performed considering speech recordings collected in non- controlled acoustic conditions during normal phone calls in a unobtrusive way. A speech enhancement algorithm is applied to improve the quality of the signals. Two different classification approaches are considered: the classification of PD patients and healthy speakers and a multi-class experiment to classify patients in several stages of the disease.

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.

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.

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.

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.

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.

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.

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.

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.