244 research outputs found
Automated Classification for Electrophysiological Data: Machine Learning Approaches for Disease Detection and Emotion Recognition
Smart healthcare is a health service system that utilizes technologies, e.g., artificial intelligence and
big data, to alleviate the pressures on healthcare systems. Much recent research has focused on the
automatic disease diagnosis and recognition and, typically, our research pays attention on automatic
classifications for electrophysiological signals, which are measurements of the electrical activity.
Specifically, for electrocardiogram (ECG) and electroencephalogram (EEG) data, we develop a
series of algorithms for automatic cardiovascular disease (CVD) classification, emotion recognition
and seizure detection.
With the ECG signals obtained from wearable devices, the candidate developed novel signal
processing and machine learning method for continuous monitoring of heart conditions. Compared to
the traditional methods based on the devices at clinical settings, the developed method in this thesis
is much more convenient to use. To identify arrhythmia patterns from the noisy ECG signals obtained
through the wearable devices, CNN and LSTM are used, and a wavelet-based CNN is proposed to
enhance the performance.
An emotion recognition method with a single channel ECG is developed, where a novel exploitative
and explorative GWO-SVM algorithm is proposed to achieve high performance emotion
classification. The attractive part is that the proposed algorithm has the capability to learn the SVM
hyperparameters automatically, and it can prevent the algorithm from falling into local solutions,
thereby achieving better performance than existing algorithms.
A novel EEG-signal based seizure detector is developed, where the EEG signals are transformed to
the spectral-temporal domain, so that the dimension of the input features to the CNN can be
significantly reduced, while the detector can still achieve superior detection performance
An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion,
abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods
involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these,
neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate
the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS)
based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of
DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DLbased
CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also,
descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic
seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented,
which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation
of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison
has been carried out between research on epileptic seizure detection and prediction. The challenges in
epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In
addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL,
rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which
summarizes the significant findings of the paper
Epileptic seizure detection and prediction based on EEG signal
Epilepsy is a kind of chronic brain disfunction, manifesting as recurrent seizures which is caused by sudden and excessive discharge of neurons. Electroencephalogram (EEG) recordings is regarded as the golden standard for clinical diagnosis of epilepsy disease. The diagnosis of epilepsy disease by professional doctors clinically is time-consuming. With the help artificial intelligence algorithms, the task of automatic epileptic seizure detection and prediction is called a research hotspot.
The thesis mainly contributes to propose a solution to overfitting problem of EEG signal in deep learning and a method of multiple channels fusion for EEG features. The result of proposed method achieves outstanding performance in seizure detection task and seizure prediction task.
In seizure detection task, this paper mainly explores the effect of the deep learning in small data size. This thesis designs a hybrid model of CNN and SVM for epilepsy detection compared with end-to-end classification by deep learning. Another technique for overfitting is new EEG signal generation based on decomposition and recombination of EEG in time-frequency domain. It achieved a classification accuracy of 98.8%, a specificity of 98.9% and a sensitivity of 98.4% on the classic Bonn EEG data.
In seizure prediction task, this paper proposes a feature fusion method for multi-channel EEG signals. We extract a three-order tensor feature in temporal, spectral and spatial domain. UMLDA is a tensor-to-vector projection method, which ensures minimal redundancy between feature dimensions. An excellent experimental result was finally obtained, including an average accuracy of 95%, 94% F1-measure and 90% Kappa index
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies
Epileptic seizures are one of the most crucial
neurological disorders, and their early diagnosis will help the
clinicians to provide accurate treatment for the patients. The
electroencephalogram (EEG) signals are widely used for epileptic
seizures detection, which provides specialists with substantial
information about the functioning of the brain. In this paper,
a novel diagnostic procedure using fuzzy theory and deep
learning techniques is introduced. The proposed method is
evaluated on the Bonn University dataset with six classification
combinations and also on the Freiburg dataset. The tunable-
Q wavelet transform (TQWT) is employed to decompose the
EEG signals into different sub-bands. In the feature extraction
step, 13 different fuzzy entropies are calculated from different
sub-bands of TQWT, and their computational complexities are
calculated to help researchers choose the best set for various
tasks. In the following, an autoencoder (AE) with six layers
is employed for dimensionality reduction. Finally, the standard
adaptive neuro-fuzzy inference system (ANFIS), and also its
variants with grasshopper optimization algorithm (ANFIS-GOA),
particle swarm optimization (ANFIS-PSO), and breeding swarm
optimization (ANFIS-BS) methods are used for classification.
Using our proposed method, ANFIS-BS method has obtained
an accuracy of 99.7
MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG
Seizure type identification is essential for the treatment and management of
epileptic patients. However, it is a difficult process known to be time
consuming and labor intensive. Automated diagnosis systems, with the
advancement of machine learning algorithms, have the potential to accelerate
the classification process, alert patients, and support physicians in making
quick and accurate decisions. In this paper, we present a novel multi-path
seizure-type classification deep learning network (MP-SeizNet), consisting of a
convolutional neural network (CNN) and a bidirectional long short-term memory
neural network (Bi-LSTM) with an attention mechanism. The objective of this
study was to classify specific types of seizures, including complex partial,
simple partial, absence, tonic, and tonic-clonic seizures, using only
electroencephalogram (EEG) data. The EEG data is fed to our proposed model in
two different representations. The CNN was fed with wavelet-based features
extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals
to let our MP-SeizNet jointly learns from different representations of seizure
data for more accurate information learning. The proposed MP-SeizNet was
evaluated using the largest available EEG epilepsy database, the Temple
University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed
model across different patient data using three-fold cross-validation and
across seizure data using five-fold cross-validation, achieving F1 scores of
87.6% and 98.1%, respectively
Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus:A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the world's population. Seizure detection and classification are difficult tasks and are ongoing challenges in biomedical signal processing to enhance medical diagnosis. This paper presents and highlights the unique frequency and amplitude information found within multiple seizure types, including their morphologies, to aid the development of future seizure classification algorithms. Whilst many published works in the literature have reported on seizure detection using electroencephalogram (EEG), there has yet to be an exhaustive review detailing multi-seizure type classification using EEG. Therefore, this paper also includes a detailed review of multi-seizure type classification performance based on the Temple University Hospital Seizure Corpus (TUSZ) dataset for focal and generalised classification, and multi-seizure type classification. Deep learning techniques have a higher overall average performance for focal and generalised classification compared to machine learning techniques, whereas hybrid deep learning approaches have the highest overall average performance for multi-seizure type classification. Finally, this paper also highlights the limitations of the TUSZ dataset and suggests some future work, including the curation of a standardised training and testing dataset from the TUSZ that would allow a proper comparison of classification methods and spur advancement in the field.</p
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