Deba Prasad Dash
@thapar.edu
Assistant Professor, Electrical and Instrumentation Engineering
Thapar Institute of Engineering and Technology
EDUCATION
PhD, Electrical Engineering, IIT Patna
M.Tech, Biomedical Engineering, MIT Manipal
B.Tech, Biomedical Engineering, TAT, BPUT
RESEARCH INTERESTS
Neurosignal processing, Biomechanics, Cognition
Scopus Publications
Scopus Publications
Deba Prasad Dash, Maheshkumar Kolekar, Chinmay Chakraborty, and Mohammad R. Khosravi
Association for Computing Machinery (ACM)
Epilepsy is one of the significant neurological disorders affecting nearly 65 million people worldwide. The repeated seizure is characterized as epilepsy. Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals. In the last decade, various machine learning techniques based on seizure detection approaches were proposed. This paper discusses different machine learning and deep learning techniques for seizure detection using intracranial and surface EEG signals. A wide range of machine learning techniques such as support vector machine (SVM) classifiers, artificial neural network (ANN) classifier, and deep learning techniques such as a convolutional neural network (CNN) classifier, and long-short term memory (LSTM) network for seizure detection are compared in this paper. The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques. Along with EEG, other physiological signals such as electrocardiogram are used to enhance seizure detection accuracy which are discussed in this paper. In recent years deep learning techniques based on seizure detection have found good classification accuracy. In this paper, an LSTM deep learning-network-based approach is implemented for seizure detection and compared with state-of-the-art methods. The LSTM based approach achieved 96.5% accuracy in seizure-nonseizure EEG signal classification. Apart from analyzing the physiological signals, sentiment analysis also has potential to detect seizures. Impact Statement- This review paper gives a summary of different research work related to epileptic seizure detection using machine learning and deep learning techniques. Manual seizure detection is time consuming and requires expertise. So the artificial intelligence techniques such as machine learning and deep learning techniques are used for automatic seizure detection. Different physiological signals are used for seizure detection. Different researchers are working on developing automatic seizure detection using EEG, ECG, accelerometer, and sentiment analysis. There is a need for a review paper that can discuss previous techniques and give further research direction. We have discussed different techniques for seizure detection with an accuracy comparison table. It can help the researcher to get an overview of both surface and intracranial EEG-based seizure detection approaches. The new researcher can easily compare different models and decide the model they want to start working on. A deep learning model is discussed to give a practical application of seizure detection. Sentiment analysis is another dimension of seizure detection and summarizing it will give a new prospective to the reader.
Eva Mishra, R. Murugesan, and Deba Prasad Dash
Springer Nature Singapore
Dharma Rane, Deba Prasad Dash, Alakananda Dutt, Anirban Dutta, Abhijit Das, and Uttama Lahiri
Frontiers Media SA
IntroductionOne’s eye movement (in response to visual tasks) provides a unique window into the cognitive processes and higher-order cognitive functions that become adversely affected in cases with cognitive decline, such as those mild cognitive impairment (MCI) and dementia. MCI is a transitional stage between normal aging and dementia.MethodsIn the current work, we have focused on identifying visual tasks (such as horizontal and vertical Pro-saccade, Anti-saccade and Memory Guided Fixation tasks) that can differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts based on oculomotor Performance indices. In an attempt to identify the optimal combination of visual tasks that can be used to differentiate the participant groups, clustering was performed using the oculomotor Performance indices.ResultsResults of our study with a group of 60 cognitively unimpaired healthy aging individuals, a group with 60 individuals with MCI and a group with 60 individuals with dementia indicate that the horizontal and vertical Anti-saccade tasks provided the optimal combination that could differentiate individuals with MCI and dementia from their cognitively unimpaired healthy aging counterparts with clustering accuracy of ∼92% based on the saccade latencies. Also, the saccade latencies during both of these Anti-saccade tasks were found to strongly correlate with the Neuropsychological test scores.DiscussionThis suggests that the Anti-saccade tasks can hold promise in clinical practice for professionals working with individuals with MCI and dementia.
Deba Prasad Dash, Maheshkumar H Kolekar, and Kamlesh Jha
Springer Science and Business Media LLC
Deba Prasad Dash, , , and Maheshkumar H Kolekar
Journal of Biomedical Research
Epilepsy is one of the most prevalent neurological disorders affecting 70 million people worldwide. The present work is focused on designing an efficient algorithm for automatic seizure detection by using electroencephalogram (EEG) as a noninvasive procedure to record neuronal activities in the brain. EEG signals' underlying dynamics are extracted to differentiate healthy and seizure EEG signals. Shannon entropy, collision entropy, transfer entropy, conditional probability, and Hjorth parameter features are extracted from subbands of tunable Q wavelet transform. Efficient decomposition level for different feature vector is selected using the Kruskal-Wallis test to achieve good classification. Different features are combined using the discriminant correlation analysis fusion technique to form a single fused feature vector. The accuracy of the proposed approach is higher for Q=2 and J=10. Transfer entropy is observed to be significant for different class combinations. Proposed approach achieved 100% accuracy in classifying healthy-seizure EEG signal using simple and robust features and hidden Markov model with less computation time. The proposed approach efficiency is evaluated in classifying seizure and non-seizure surface EEG signals. The system has achieved 96.87% accuracy in classifying surface seizure and nonseizure EEG segments using efficient features extracted from different J level.
Deba Prasad Dash, Maheshkumar H. Kolekar, and Kamlesh Jha
Elsevier BV
Electroencephalography (EEG) is a non-invasive method for the analysis of neurological disorders. Epilepsy is one of the most widespread neurological disorders and often characterized by repeated seizures. This paper intends to conduct an iterative filtering based decomposition of EEG signals to improve upon the accuracy of seizure detection. The proposed approach is evaluated using All India Institute of Medical Science (AIIMS) Patna EEG database and online CHB-MIT surface EEG database. The iterative filtering decomposition technique is applied to extract sub-components from the EEG signal. The feature set obtained from each segmented intrinsic mode function consists of 2-D power spectral density and time-domain features dynamic mode decomposition power, variance, and Katz fractal dimension. The Hidden Markov Model (HMM) based probabilistic model has been designed using the above-stated features representing the seizure and non-seizure EEG events. The EEG signal is classified based on the maximum score obtained from the individual feature-based classifiers. The maximum score derived from each HMM classifier gives the final class information. The proposed decomposition of EEG signals achieved 99.60% and 99.74% accuracy in seizure detection for the online CHB-MIT surface EEG database and AIIMS Patna EEG database, respectively.
Deba Prasad Dash and Maheshkumar H. Kolekar
Springer Singapore
Seizure is defined as a sudden synchronous activity of a group of neurons resulting in an electric surge in the brain. Epilepsy is a brain disorder indicated by repeated seizures. Around 10 million people in India are suffering from epilepsy. Electroencephalogram (EEG) signal being low cost and non-invasive in nature can be used effectively for seizure detection. The present work focuses on developing an efficient epileptic seizure detection system using intracranial EEG signals. Dual tree complex wavelet transform is used to decompose the signal into various sub-frequency bands. Probability features are used to extract efficient indicators for seizure and healthy classes. Discriminant correlation analysis is used to increase the difference between different classes as well as reduce the difference between same classes. The fused feature set is clustered using fuzzy c means clustering algorithm. Hidden Markov model discriminates the seizure class with healthy class with good efficiency. Maximum accuracy of 98.57% is achieved for seizure detection with very low execution time.
Deba Prasad Dash and Maheshkumar H Kolekar
Springer Singapore
Human activity recognition is one of the important and difficult problems in computer vision and machine learning applications. Automated human activity recognition system based on dense flow trajectory and hidden Markov model (HMM) is proposed. A 3D dense trajectory was formed by tracking the scale-invariant points from frame to frame. Maximum of 100 points per frame were considered for tracking. Histogram of gradient and dense trajectory descriptor features were extracted from cleaned trajectory and used for training hidden Markov models for each activity. Analysis of variance test resulted in F value of 1150.89 and 74.29 for histogram of gradient (HOG) and dense trajectory descriptor, respectively, for Weizmann database and 187.08 for combined features for videos recorded at Indian institute of technology Patna. Maximum accuracy of 100% is achieved for Weizmann database and IIT Patna database using hierarchical HMM.
Deba Prasad Dash and Maheshkumar H Kolekar
IEEE
Epilepsy is defined as non communicable neurological disorder and characterized by repeated seizures. Electroencephalogram (EEG) is an efficient tool for analysing brain disorder. Low computation and efficient automatic epileptic seizure detection will be of great use for clinicians. In this research entropy features such as Shannon entropy, collision entropy and hjorth Factors are used as bio-marker for seizures detection. Feature selection is performed based on Analysis of variance (ANOVA) test. Efficiency of other features such as distance entropy and higuchi fractal dimension are evaluated. Hierarchy based Hidden Markov Model is used for classification. Two state ergodic Hidden Markov Model are designed for healthy-seizure, seizure-interseizure and healthy-interseizure classification. Average accuracy achieved for healthy-interseizure, healthy-seizure and seizure-interseizure are 95.62%, 96.67% and 95.00% respectively. The proposed algorithm is cross-validated with higher channel EEG signal.
Deba Prasad Dash and H Maheshkumar Kolekar
Diva Enterprises Private Limited
Epilepsy is a chronic neurological disorder which is indicated by recurrent seizure. According to World Health Organization about 50 million people worldwide and 80% people with epilepsy belongs to low or middle income group. Two million new epilepsy cases occur each year globally as estimated by world health organization. Present method of seizure detection is manual making the process time taking and doctor dependent. The proposed algorithm automatically detects seizures with higher accuracy. Hidden Markov model (HMM) based classification approach is proposed for epileptic seizure detection. Electroencephalogram (EEG) signal was decomposed using empirical mode decomposition. Higuchi's fractal dimension and Shannon, collision, minimum entropy features were extracted from six intrinsic mode function and average feature values were used for classification. Features extracted from the signals were efficient in differentiating seizure, healthy and inter-seizure EEG signals. K means clustering algorithm was used for generating symbol sequence. Baum-Welch algorithm was used training HMM model. Viterbi algorithm was used to find the state sequence for each observed sequence obtained after manual clustering of test signal features. Maximum accuracy of 99.16% was observed for healthy-seizure, 95.00% for seizure-Interseizure and 50.62% for healthy-Interseizure EEG signals classification.
Maheshkumar H. Kolekar and Deba Prasad Dash
IEEE
Recognizing human activity is an important area of research in computer vision application. Manual monitoring of all cameras continuously for longer duration is inefficient making auto-detection of activity important. In this paper shape and optical flow features are fused together and used for human activity recognition. Features extracted are found to be efficient as concluded by ANOVA test. Hidden Markov Model are generated for each activity. System is trained and tested in various indoor and outdoor environment. The method adapted is made shape and angle invariant. Accuracy achieved using least square support vector machine classifier is 80% for all activities. Hidden Markov Model resulted in better accuracy as compared to least square support vector machine classifier with accuracy of 100.00% for walking, 100.00% for hand waving, 90% for bending, 84.61% for running and 90% for side gallop activities. 100% accuracy is achieved in recognizing activity in different angle with respect to camera.
Maheshkumar H. Kolekar, Deba Prasad Dash, and Priti N. Patil
Springer Singapore
Event related potential (ERP) is a non-invasive way to measure person’s cognitive ability or any neuro-cognitive disorder. Familiarity with any stimulus can be indicated by the brain’s instantaneous response to that particular stimulus. In this research work ERP based eye witness identification system is proposed. Electroencephalogram (EEG) signal was passed through butterworth band-pass filter and EEG signal was segmented based on marker. EEG segments were averaged and ERP was extracted from EEG signal. Grey incidence degree based wavelet denoising was performed. ERP was converted to image form and structural similarity index feature was extracted. Radial basis function kernel based support vector machine classifier was used to classify a person with or without crime information. The observed accuracy of proposed approach was 87.50 %.
Maheshkumar H. Kolekar and Deba Prasad Dash
IEEE
Epilepsy is the most common disease of central nervous system. According to World Health Organization about 50 million people worldwide and 80% people from developing regions are suffering from epilepsy. Electroencephalogram (EEG) is one of the non-invasive techniques available for seizure detection. In this paper we have proposed non-linear feature based epileptic seizure detection using least square support vector machine (LSSVM) classifier. We have developed low computational and more accurate system for real time epileptic seizure detection. Symbolic entropy, Lempel-Ziv complexity and sample entropy are extracted and LSSVM classifier is used to classify data into ictal, healthy and inter-ictal EEG signals. LSSVM classifier in One-verse-All approach, One-verse-One approach, and multiclass classifier approach classifies ictal EEG signal with an accuracy of 81.67%, 91.25 % and 82.22 % respectively. Hence, the proposed One-verse-One approach has detected ictal EEG signal with highest accuracy and sensitivity.