Deba Prasad Dash

@thapar.edu

Assistant Professor, Electrical and Instrumentation Engineering
Thapar Institute of Engineering and Technology

Deba Prasad Dash


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https://researchid.co/debaprasad

EDUCATION

PhD, Electrical Engineering, IIT Patna
M.Tech, Biomedical Engineering, MIT Manipal
B.Tech, Biomedical Engineering, TAT, BPUT

RESEARCH INTERESTS

Neurosignal processing, Biomechanics, Cognition
18

Scopus Publications

Scopus Publications

  • EEG Based Epileptic Seizure Detection Using Deep Learning and Machine Learning Model
    Deba Prasad Dash, Maheshkumar H. Kolekar, Eva Mishra
    Lecture Notes in Networks and Systems, 2025
  • A Comparative Study of Different Machine Learning Techniques for Skin Disease Detection
    Deba Prasad Dash, Maheshkumar H. Kolekar, Eva Mishra
    Machine Learning for Disease Detection Prediction and Diagnosis Challenges and Opportunities, 2025
  • Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis
    Deba Prasad Dash, Maheshkumar Kolekar, Chinmay Chakraborty, Mohammad R. Khosravi
    ACM Transactions on Asian and Low Resource Language Information Processing, 2024
    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.
  • Deep Learning Model-Based Approach for Agricultural Crop Price Prediction in Indian Market
    Eva Mishra, R. Murugesan, Deba Prasad Dash
    Lecture Notes in Electrical Engineering, 2024
  • AttRes-UNet: A Dual-Model Approach for Brain Tumor Segmentation
    Samruddhi Maheshkumar Kolekar, Agnesh Chandra Yadav, Suchita Yadav, Daba Prasad Dash
    2024 4th International Conference on Advancement in Electronics and Communication Engineering Aece 2024, 2024
    Accurate segmentation of brain tumors in MRI images is crucial for diagnosis, treatment planning, and disease monitoring. Traditional manual segmentation methods are time- consuming and prone to variability, while standard deep learning architectures such as U-Net face challenges in capturing the complex, heterogeneous structures of brain tumors. In this study, we proposed AttRes-UNet, a novel dual-model architecture combining Attention U-Net and Residual U-Net to enhance segmentation accuracy. Attention Gates allow the model to focus on critical tumor regions, while Residual Blocks improve deep feature learning, enabling the model to handle diverse tumor morphologies with greater precision. We evaluated the model using the BraTS 2021 dataset, achieving high performance with an accuracy of 0.996, a dice coefficient of 0.8340, and an Intersection over Union of 0.8210. These results demonstrate the effectiveness of AttRes-UNet in improving segmentation accuracy, making it a valuable tool for automated brain tumor delineation, ultimately reducing manual workload and improving clinical outcomes.
  • AI and machine learning in medical data processing
    Deba Prasad Dash, Maheshkumar H Kolekar
    Intelligent Multimedia Processing and Computer Vision Techniques and Applications, 2023
    A seizure is defined as a sudden synchronous activity of group of neurons causing sudden movement of the body. Nearly 10 million people from India are suffering from epilepsy. Electroencephalogram (EEG) is a non-invasive technique to measure the neural activity of brain. EEG signal processing and speech signal processing have applications in seizure detection. Sudden neural activity in the brain is reflected in the EEG signal and is processed using machine learning and deep learning techniques for efficient seizure detection. This chapter gives an overview of different speech processing and signal processing techniques for seizure detection. Deep learning and machine learning techniques are implemented and the results are discussed in this chapter. Different techniques are compared to give a future direction to the researcher to work in this field. Long short-term memory (LSTM) network model is applied for seizure detection and the results are discussed in this chapter.
  • Distinctive visual tasks for characterizing mild cognitive impairment and dementia using oculomotor behavior
    Dharma Rane, Deba Prasad Dash, Alakananda Dutt, Anirban Dutta, Abhijit Das, Uttama Lahiri
    Frontiers in Aging Neuroscience, 2023
    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.
  • Surface EEG based epileptic seizure detection using wavelet based features and dynamic mode decomposition power along with KNN classifier
    Deba Prasad Dash, Maheshkumar H Kolekar, Kamlesh Jha
    Multimedia Tools and Applications, 2022
  • EEG-Based Epileptic Seizure Detection Using Least Square SVM with Spectral and Multiscale Key Point Energy Features
    D. Dash, M. Kolekar
    Advances in Intelligent Systems and Computing, 2020
  • Hidden Markov model based epileptic seizure detection using tunable Q wavelet transform
    Deba Prasad Dash, ,, Maheshkumar H Kolekar
    Journal of Biomedical Research, 2020
    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.
  • Multi-channel EEG based automatic epileptic seizure detection using iterative filtering decomposition and Hidden Markov Model
    Deba Prasad Dash, Maheshkumar H. Kolekar, Kamlesh Jha
    Computers in Biology and Medicine, 2020
  • Probability-based approach for epileptic seizure detection using hidden Markov model
    Deba Prasad Dash, Maheshkumar H. Kolekar
    Communications in Computer and Information Science, 2019
  • Dense optical flow trajectory-based human activity recognition using hierarchical hidden markov model
    Deba Prasad Dash, Maheshkumar H Kolekar
    Advances in Intelligent Systems and Computing, 2018
  • Epileptic seizure detection based on EEG signal analysis using hierarchy based hidden markov model
    Deba Prasad Dash, Maheshkumar H Kolekar
    2017 International Conference on Advances in Computing Communications and Informatics Icacci 2017, 2017
  • EEG based epileptic seizure detection using empirical mode decomposition and hidden markov model
    Deba Prasad Dash, H Maheshkumar Kolekar
    Indian Journal of Public Health Research and Development, 2017
  • Hidden Markov Model based human activity recognition using shape and optical flow based features
    Maheshkumar H. Kolekar, Deba Prasad Dash
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2017
  • Support vector machine based extraction of crime information in human brain using ERP image
    Maheshkumar H. Kolekar, Deba Prasad Dash, Priti N. Patil
    Advances in Intelligent Systems and Computing, 2017
  • A nonlinear feature based epileptic seizure detection using least square support vector machine classifier
    Maheshkumar H. Kolekar, Deba Prasad Dash
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2016