Neurology

An Improved Performance of Deep Learning Based on Convolution Neural Network to Classify the Hand Motion by Evaluating Hyper Parameter.

High accuracy in pattern recognition based on electromyography(EMG) contributes to the effectiveness of prosthetics hand development. This study aimed to improve performance and simplify the deep learning pre-processing based on the convolution neural network (CNN) algorithm for classifying ten hand motion from two raw EMG signals. The main contribution of this study is the simplicity of pre-proce...

Automated human mind reading using EEG signals for seizure detection.

Epilepsy is one of the most occurring neurological disease globally emerged back in 4000 BC. It is affecting around 50 million people of all ages these days. The trait of this disease is recurrent seizures. In the past few decades, the treatments available for seizure control have improved a lot with the advancements in the field of medical science and technology. Electroencephalogram (EEG) is a w...

Partial directed coherence based graph convolutional neural networks for driving fatigue detection.

The mental state of a driver can be accurately and reliably evaluated by detecting the driver's electroencephalogram (EEG) signals. However, tradition...

Automatic Identification of Brain Independent Components in Electroencephalography Data Collected while Standing in a Virtually Immersive Environment - A Deep Learning-Based Approach.

Electroencephalography (EEG) is a commonly used method for monitoring brain activity. Automating an EEG signal processing pipeline is imperative to th...

Deep transfer learning for improving single-EEG arousal detection.

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two...

Emotion Recognition with Refined Labels for Deep Learning.

The traditional emotion classification framework usually fits all the features segments of the same trial to a fixed annotation. Considering the fact ...

Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling.

Epilepsy is a neurological disorder which causes seizures in over 65 million people worldwide. Recently developed implantable therapeutic devices aim ...

EEG-based Depression Detection Using Convolutional Neural Network with Demographic Attention Mechanism.

Electroencephalography (EEG)-based depression detection has become a hot topic in the development of biomedical engineering. However, the complexity a...

Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing.

This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact ...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized ...

Attention Networks for Multi-Task Signal Analysis.

Recent advances in deep learning have enabled the development of automated frameworks for analysing medical images and signals. For analysis of physio...

Parkinson's Disease Classification using Pitch Synchronous Speech Segments and Fine Gaussian Kernels based SVM.

Researchers have been using signal processing based methods to assess speech from Parkinson's disease (PD) patients and identify the contrasting featu...

Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network.

In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use m...

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG.

Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the exis...

Pose Estimation from Electromyographical Data using Convolutional Neural Networks.

This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Nina...

S-Convnet: A Shallow Convolutional Neural Network Architecture for Neuromuscular Activity Recognition Using Instantaneous High-Density Surface EMG Images.

The recent progress in recognizing low-resolution instantaneous high-density surface electromyography (HD-sEMG) images opens up new avenues for the de...

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical p...

Wavelet Spectral Deep-training of Convolutional Neural Networks for Accurate Identification of High-Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Early diagnosis and prognosis of babies with signs of hypoxic-ischemic encephalopathy (HIE) is currently limited and requires reliable prognostic biom...

Deep Convolutional Neural Network and Reverse Biorthogonal Wavelet Scalograms for Automatic Identification of High Frequency Micro-Scale Spike Transients in the Post-Hypoxic-Ischemic EEG.

Diagnosis of hypoxic-ischemic encephalopathy (HIE) is currently limited and prognostic biological markers are required for early identification of at ...

Wavelet Spectral Time-Frequency Training of Deep Convolutional Neural Networks for Accurate Identification of Micro-Scale Sharp Wave Biomarkers in the Post-Hypoxic-Ischemic EEG of Preterm Sheep.

Neonatal hypoxic-ischemic encephalopathy (HIE) evolves over different phases of time during recovery. Some neuroprotection treatments are only effecti...