Varun Bajaj

Bio: Varun Bajaj is an academic researcher from Indian Institute of Information Technology, Design and Manufacturing, Jabalpur. The author has contributed to research in topics: Hilbert–Huang transform & Support vector machine. The author has an hindex of 26, co-authored 132 publications receiving 2791 citations. Previous affiliations of Varun Bajaj include International Institute of Information Technology & Indian Institutes of Information Technology.

Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition

Abstract: In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and nonseizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.

Transfer learning based histopathologic image classification for breast cancer detection

Abstract: Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.

Convolutional Neural Network Based Approach Towards Motor Imagery Tasks EEG Signals Classification

Abstract: This paper introduces a methodology based on deep convolutional neural networks (DCNN) for motor imagery (MI) tasks recognition in the brain-computer interface (BCI) system. More specifically, the DCNN is used for classification of the right hand and right foot MI-tasks based electroencephalogram (EEG) signals. The proposed method first transforms the input EEG signals into images by applying the time-frequency (T-F) approaches. The used T-F approaches are short-time-Fourier-transform (STFT) and continuous-wavelet-transform (CWT). After T-F transformation the images of MI-tasks EEG signals are applied to the DCNN stage. The pre-trained DCNN model, AlexNet is explored for classification. The efficiency of the proposed method is evaluated on IVa dataset of BCI competition-III. The evaluation metrics such as accuracy, sensitivity, specificity, F1-score, and kappa value are used for measuring the proposed method results quantitatively. The obtained results show that the CWT approach yields better results than the STFT approach. In addition, the proposed method obtained 99.35% accuracy score is the best one among the existing methods accuracy scores.

Deep Learning Approaches for COVID-19 Detection Based on Chest X-ray Images.

Abstract: COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.

Classification of Seizure and Nonseizure EEG Signals Using Empirical Mode Decomposition

Abstract: In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and nonseizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.

Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions

Abstract: We propose new features for classification of epileptic seizure EEG signals.Features were extracted from PSR of IMFs of EEG signals.We define ellipse area of 2D PSR and IQR of Euclidian distance of 3D PSR as features.LS-SVM classifier has been used for classification with the proposed features.Results were compared with other existing methods studied on the same EEG dataset. Epileptic seizure is the most common disorder of human brain, which is generally detected from electroencephalogram (EEG) signals. In this paper, we have proposed the new features based on the phase space representation (PSR) for classification of epileptic seizure and seizure-free EEG signals. The EEG signals are firstly decomposed using empirical mode decomposition (EMD) and phase space has been reconstructed for obtained intrinsic mode functions (IMFs). For the purpose of classification of epileptic seizure and seizure-free EEG signals, two-dimensional (2D) and three-dimensional (3D) PSRs have been used. New features based on the 2D and 3D PSRs of IMFs have been proposed for classification of epileptic seizure and seizure-free EEG signals. Two measures have been defined namely, 95% confidence ellipse area for 2D PSR and interquartile range (IQR) of the Euclidian distances for 3D PSR of IMFs of EEG signals. These measured parameters show significant difference between epileptic seizure and seizure-free EEG signals. The combination of these measured parameters for different IMFs has been utilized to form the feature set for classification of epileptic seizure EEG signals. Least squares support vector machine (LS-SVM) has been employed for classification of epileptic seizure and seizure-free EEG signals, and its classification performance has been evaluated using different kernels namely, radial basis function (RBF), Mexican hat wavelet and Morlet wavelet kernels. Simulation results with various performance parameters of classifier, have been included to show the effectiveness of the proposed method for classification of epileptic seizure and seizure-free EEG signals.