Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited ...by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the performance of the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and κ = 0.79. Our developed model can be applied to other sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
•A new automated model for epileptic seizure detection.•The model employs a combination of recently introduced analytic time-frequency flexible wavelet transform and fractal dimension.•Eight ...different classification problems are investigated pertaining to EEG signals.•The model surpasses all existing models.•Model attains perfect 100% classification sensitivity.
The identification of seizure activities in non-stationary electroencephalography (EEG) is a challenging task. The seizure detection by human inspection of EEG signals is prone to errors, inaccurate as well as time-consuming. Several attempts have been made to develop automatic systems so as to assist neurophysiologists in identifying epileptic seizures accurately. The proposed study brings forth a novel automatic approach to detect epileptic seizures using analytic time-frequency flexible wavelet transform (ATFFWT) and fractal dimension (FD). The ATFFWT has inherent attractive features such as, shift-invariance property, tunable oscillatory attribute and flexible time-frequency covering favorable for the analysis of non-stationary and transient signals. We have used ATFFWT to decompose EEG signals into the desired subbands. Following the ATFFWT decomposition, we calculate FD for each subband. Finally, FDs of all subbands have been fed to the least-squares support vector machine (LS-SVM) classifier. The 10-fold cross validation has been used to obtain stable and reliable performance and to avoid the over fitting of the model. In this study, we investigate various different classification problems (CPs) pertaining to different classes of EEG signals, including the following popular CPs: (i) ictal versus normal (ii) ictal versus inter-ictal (iii) ictal versus non-ictal. The proposed model is found to be outperforming all existing models in terms of classification sensitivity (CSE) as it achieves perfect 100% sensitivity for seven CPs investigated by us. The prominent attribute of the proposed system is that though the model employs only one set of discriminating features (FD) for all CPs, it yields promising classification accuracy. Since, the proposed model attains the perfect classification performance it appears that a system is in place to assist clinicians to diagnose seizures accurately in less time. Further, the proposed system seems useful and attractive, especially, in the rural areas of developing countries where there is a shortage of experienced clinicians and expensive machines like functional magnetic resonance imaging (fMRI).
The novel coronavirus 2019 (COVID-2019), which first appeared in Wuhan city of China in December 2019, spread rapidly around the world and became a pandemic. It has caused a devastating effect on ...both daily lives, public health, and the global economy. It is critical to detect the positive cases as early as possible so as to prevent the further spread of this epidemic and to quickly treat affected patients. The need for auxiliary diagnostic tools has increased as there are no accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease, and can also be assistive to overcome the problem of a lack of specialized physicians in remote villages. In this study, a new model for automatic COVID-19 detection using raw chest X-ray images is presented. The proposed model is developed to provide accurate diagnostics for binary classification (COVID vs. No-Findings) and multi-class classification (COVID vs. No-Findings vs. Pneumonia). Our model produced a classification accuracy of 98.08% for binary classes and 87.02% for multi-class cases. The DarkNet model was used in our study as a classifier for the you only look once (YOLO) real time object detection system. We implemented 17 convolutional layers and introduced different filtering on each layer. Our model (available at (https://github.com/muhammedtalo/COVID-19)) can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
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•Proposed deep model for early detection of COVID-19 cases using X-ray images.•Obtained accuracy of 98.08% and 87.02% for binary and multi-classes.•Proposed heatmaps can help the radiologists to locate the affected regions on chest X-rays.•DarkCovidNet model can assist the clinicians to make faster and accurate diagnosis.
The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area ...affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEnAvg), average Renyi's entropy (RenEnAvg ), average approximate entropy (ApEnAvg), average sample entropy (SpEnAvg) and average phase entropies (S1Avg and S2Avg), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.
The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG ...signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student's t-test, the Wilcoxon test, the receiver operating characteristic (ROC) and entropy. These ranked features are fed to various classifiers, namely k-nearest neighbour (KNN), probabilistic neural network (PNN), fuzzy classifier and least squares support vector machine (LS-SVM), for automated classification of focal and non-focal EEG signals using the minimum number of features. The identification of the focal EEG signals can be helpful to locate the epileptogenic focus.
Electrocardiography (ECG) is widely used for arrhythmia detection nowadays. The machine learning methods with signal processing algorithms have been used for automated diagnosis of cardiac health ...using ECG signals. In this article, discrete wavelet transform (DWT) coupled with novel 1-dimensional hexadecimal local pattern (1D-HLP) technique are employed for automated detection of arrhythmia detection. The ECG signals of 10 s duration are subjected to DWT to decompose up to five levels. The 1D-HLP extracts 512 dimensional features from each level of the five levels of low pass filter. Then, these extracted features are concatenated to obtain 512×6=3072 dimensional feature set. These fused features are subjected to neighborhood component analysis (NCA) feature reduction technique to obtain 64, 128 and 256 features. Finally, these features are subjected to 1 nearest neighborhood (1NN) classifier for classification with 4 distance metrics namely city block, Euclidean, spearman and cosine. We have obtained a classification accuracy of 95.0% in classifying 17 arrhythmia classes using MIT-BIH Arrhythmia ECG dataset. Our results show that the proposed method is more superior than other already reported classical ensemble learning and deep learning methods for arrhythmia detection using ECG signals.
•Classification 17 ECG classes is proposed.•A novel hexadecimal ternary pattern is employed.•Multilevel wavelet is used to extract features deeply.•Obtained classification accuracy of 95.0%.•Proposed method is computationally less intensive and fast.
•MR brain images are classified using a pre-trained model and deep transfer learning techniques.•The proposed model yields 5-fold classification accuracy of 100% on 613 MR images.•The computation ...cost of the complex layer parameters and the long validation process are eliminated.•Automatic classification is provided without any manual feature extraction/selection and pre-processing on the images.
Magnetic resonance imaging (MRI) is the most common imaging technique used to detect abnormal brain tumors. Traditionally, MRI images are analyzed manually by radiologists to detect the abnormal conditions in the brain. Manual interpretation of huge volume of images is time consuming and difficult. Hence, computer-based detection helps in accurate and fast diagnosis. In this study, we proposed an approach that uses deep transfer learning to automatically classify normal and abnormal brain MR images. Convolutional neural network (CNN) based ResNet34 model is used as a deep learning model. We have used current deep learning techniques such as data augmentation, optimal learning rate finder and fine-tuning to train the model. The proposed model achieved 5-fold classification accuracy of 100% on 613 MR images. Our developed system is ready to test on huge database and can assist the radiologists in their daily screening of MR images.
Myocardial infarction (MI) is a silent condition that irreversibly damages the heart muscles. It expands rapidly and, if not treated timely, continues to damage the heart muscles. An ...electrocardiogram (ECG) is generally used by the clinicians to diagnose the MI patients. Manual identification of the changes introduced by MI is a time-consuming and tedious task, and there is also a possibility of misinterpretation of the changes in the ECG. Therefore, a method for automatic diagnosis of MI using ECG beat with flexible analytic wavelet transform (FAWT) method is proposed in this work. First, the segmentation of ECG signals into beats is performed. Then, FAWT is applied to each ECG beat, which decomposes them into subband signals. Sample entropy (SEnt) is computed from these subband signals and fed to the random forest (RF), J48 decision tree, back propagation neural network (BPNN), and least-squares support vector machine (LS-SVM) classifiers to choose the highest performing one. We have achieved highest classification accuracy of 99.31% using LS-SVM classifier. We have also incorporated Wilcoxon and Bhattacharya ranking methods and observed no improvement in the performance. The proposed automated method can be installed in the intensive care units (ICUs) of hospitals to aid the clinicians in confirming their diagnosis.
Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a ...specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP.
In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.
•A novel cascaded RNN architecture with LSTM blocks is proposed for the automated scoring of sleep stages.•A single-channel EEG based automated system.•Use of a publicly available sleep-EDF database.•The method outperforms the state-of-the-art methods in the N1 stage detection.•A first step forward to automated assessment of neurocognitive performance.
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of ...polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
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