Puneet Kumar Jain

Dr. Puneet Kumar Jain is currently associated with the Department of Computer Science and Engineering at the National Institute of Technology Jalandha, India. He earned his PhD from the Indian Institute of Technology Jodhpur, his M.Tech from Delhi Technological University, and his B.Tech from Engineering College Bikaner. As a senior member of IEEE, he also holds the position of vice-secretary of the IEEE Rourkela Subsection. Additionally, he serves as the Professor-in-Charge of the Centre for Automation Technology at the National Institute of Technology Rourkela. Dr. Jain’s areas of interest include signal and image processing, time-frequency analysis, and wearable healthcare. His research is centred on Digital Healthcare, with a particular focus on healthcare technology and algorithm development aimed at improving patient diagnosis, assessment, and remote healthcare. His work has been published in peer-reviewed and SCI-indexed international journals and reputed conferences. Furthermor

EDUCATION

Ph.D. (Indian Institute of Technology Jodhpur, Jodhpur) February 2018
Center for Information and Communication Technology
Thesis Title: Processing of Heart Sound Signal to Monitor Cardiovascular Functions in Real-life Scenario
Thesis supervisor: Dr. Anil Kumar Tiwari
CPI in course work: 7.75

M. Tech. (Delhi Technological University, Delhi) July 2011
Department of Information Technology
Thesis Title: Fuzzy and non-fuzzy region growing techniques for color image segmentation
Thesis supervisor: Susan
CGPA in course work: 8.66

B. Tech. (Government Engineering College Bikaner, Bikaner) June 2009
Department of Computer Science and Engineering
Percentage in course work: 71.5

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Vision and Pattern Recognition, Biomedical Engineering, Signal Processing

Scopus Publications

A Robust Algorithm for Segmentation of Phonocardiography Signal Using Tunable Quality Wavelet Transform
Puneet Kumar Jain, Anil Kumar Tiwari
Journal of Medical and Biological Engineering, 2018
An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal
Puneet Kumar Jain, Anil Kumar Tiwari
Biomedical Signal Processing and Control, 2017
An algorithm for automatic segmentation of heart sound signal acquired using seismocardiography
Puneet Kumar Jain, Anil Kumar Tiwari
2016 International Conference on Systems in Medicine and Biology Icsmb 2016, 2017
Automatic diagnosis of the heart valve diseases generally requires the segmentation of heart sound signal. Henceforth, in this paper a novel algorithm for automatic segmentation of the heart sound signal is proposed. The heart sound signal is acquired using seismocardiography (SCG), which uses a sensor called accelerometer. The accelerometer is of small size and low weight and thus convenient to wear. The proposed algorithm performs in three steps. First, the signal is filtered using the developed denoising algorithm based on discrete wavelet transform. The computational complexity of this algorithm is reduced by processing only two levels, which are expected to have heart sound signal, and other levels are discarded. To improve the performance of denoising, an adaptive threshold is obtained for both the levels separately, and applied. Then, the denoised signal is obtained by reconstructing the thresholded coefficients. In the second step, peaks are detected in the denoised signal using an adaptive threshold, obtained using Otsu's method. Then, false detected peaks and noise contaminated parts of the signal are identified and discarded from further analyses. In the third step, the heart sound components are identified as S1, and S2 based on the energy of the particular component and segmentation is performed. The results of denoising, show that the developed algorithm outperforms the existing method. Further, the segmentation results show that the developed algorithm is able to identify the heart sound components, accurately, even in the presence of noise.
Seismocardiography: An alternate method to estimate electro-mechanical window
Puneet Kumar Jain, Anil Kumar Tiwari, Om Lata Bhagat
2016 International Conference on Systems in Medicine and Biology Icsmb 2016, 2017
Analysis of electromechanical (E-M) window provides an early marker of several cardiovascular diseases. The E-M window is defined as the duration between the electrical systole (QT) and the mechanical systole (QS2). Traditional methods for the estimation of the electrical systole and the mechanical systole are electrocardiography (ECG) and phonocardiography (PCG). PCG is a widely used method due its features such as portability and cost effectiveness. However, it needs to be improvise in terms of its size and weight, to make it convenient to wear for long-term. Further, the PCG signal is susceptible to various noises, which limits its use in clinical environment. Henceforth, in this paper, we presented a novel system to estimate the E-M window using seismocardiogaphy (SCG) in replacement of the PCG. The SCG uses a sensor called accelerometer, which is small in size and low weight and hence it is convenient to wear. Furthermore, it does not require a microphone, as required in case of the PCG, and hence it is robust to environmental noises. We analysed the performance of the proposed system compared to the PCG, for the estimation of the E-M window. For this purpose ECG, PCG, and SCG signals are acquired simultaneously, for 10 subjects and then validated the obtained results using Bland-Altman plots. Results show that the proposed method is a suitable alternative to estimate the E-M window.
A novel method for suppression of motion artifacts from the seismocardiogram signal
Puneet Kumar Jain, Anil Kumar Tiwari
International Conference on Digital Signal Processing DSP, 2016
Seismocardiography (SCG) measures the precordial vibrations using a sensor called accelerometer, which is of small size and low weight. These features support better attachment of it to the subject's body and hence get less affected by the slow motion of the subject. However, noise generated due to the footsteps, while walking, contaminates the SCG signal. Therefore, in this paper, a novel method is proposed to remove these contaminations from the SCG signal. A three-axis accelerometer was attached to the chest wall such that heart sound components are reflected in the z-axis while the motion noise due to footstep will be seen in the x-axis. To remove the noise components from the heart signal (z-axis), the location of footsteps from the x-axis are identified and the corresponding components from the z-axis are removed. After noise removal fundamental heart sounds (FHS), S1 and S2, are identified using Otsu's threshold method. The obtained results show that the proposed algorithm outperforms the state-of-the-art methods. It efficiently removes most of the contamination due to footsteps and identifies the heart sound components.
An adaptive method for shrinking of wavelet coefficients for phonocardiogram denoising
Puneet Kumar Jain, Anil Kumar Tiwari
International Conference on Digital Signal Processing DSP, 2016
Noise suppression from the phonocardiogram (PCG) signal is important to improve the diagnostic efficiency. For the PCG signal, Discrete Wavelet Transform (DWT) based denoising method has been used extensively due to its good performance. However, the performance of this method depends on the threshold value and the way to apply it on the wavelet coefficients. Therefore, in this paper, an adaptive method is proposed to estimate the threshold value for the shrinking of the wavelet coefficients of the PCG signal. For this purpose, a new statistical parameter is obtained by incorporating medical domain knowledge about the PCG. The threshold value is estimated based on the statistical analysis of the wavelet coefficients and the present level of noise. Further, to overcome the issues related to existing threshold functions, soft and hard, new threshold functions, mid and non-linear mid are presented. The proposed method is applied to the PCG signal contaminated with simulated white Gaussian noise, red noise, and pink noise. The obtained results of the proposed method are compared with the results of state-of-the-art methods and they show the superiority of the proposed method.
Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios
Puneet Kumar Jain, Anil Kumar Tiwari, Vijay S. Chourasia
Journal of Medical Engineering and Technology, 2016
This paper presents a system based on Seismocardiography (SCG) to monitor the heart sound signal for the long-term. It uses an accelerometer, which is of small size and low weight and, thus, convenient to wear. Such a system should also be robust to various noises which occur in real life scenarios. Therefore, a detailed analysis is provided of the proposed system and its performance is compared to the performance of the Phoncardiography (PCG) system. For this purpose, both signals of five subjects were simultaneously recorded in clinical and different real life noisy scenarios. For the quantitative analysis, the detection rate of fundamental heart sound components, S1 and S2, is obtained. Furthermore, a quality index based on the energy of fundamental components is also proposed and obtained for the same. Results show that both the techniques are able to acquire the S1 and S2, in clinical set-up. However, in real life scenarios, we observed many favourable features in the proposed system as compared to PCG, for its use for long-term monitoring.
Heart monitoring systems-A review
Puneet Kumar Jain, Anil Kumar Tiwari
Computers in Biology and Medicine, 2014
An adaptive single seed based region growing algorithm for color image segmentation
Puneet Kumar Jain, Seba Susan
2013 Annual IEEE India Conference Indicon 2013, 2013
In this paper an adaptive single seed based region growing algorithm (ASSRG) is proposed for color image segmentation. The proposed method starts with the center pixel of the image as the initial seed. The region growing formula uses three homogeneity criteria local, global and relative, in two steps to label the pixel to a region. It first checks for the color similarity of the pixel with respect to the connected labelled pixel and secondly with the mean value of a growing region. If the similarity criterion is fulfilled then this pixel is included in the growing region. Otherwise the similarity of the pixel with respect to its 8-neighbors is compared with respect to the mean value of a growing region. If the pixel is closer to the growing region as compared to its neighbors then it is included in the growing region, otherwise it is labelled as boundary pixel. After one region is completely grown, the next seed pixel is selected from the boundary pixel stack. Region merging is performed to reduce over segmentation in the results. We have applied our algorithm to Berkley images with successful results and the evaluation of the segmented images has been done using Liu's F-factor, total number of regions segmented and time taken by the algorithm. A fuzzy rule based modification of the algorithm is also proposed to further improve results. The proposed algorithm is also compared with SSRG algorithm using Otsu's threshold, SRGRM algorithm and MRG region growing techniques and is shown to outperform all methods.
A simple single seeded region growing algorithm for color image segmentation using adaptive thresholding
Om Prakash Verma, Madasu Hanmandlu, Seba Susan, Muralidhar Kulkarni, Puneet Kumar Jain
Proceedings 2011 International Conference on Communication Systems and Network Technologies Csnt 2011, 2011
In this paper, we present a region growing technique for color image segmentation. Conventional image segmentation techniques using region growing requires initial seeds selection, which increases computational cost & execution time. To overcome this problem, a single seeded region growing technique for image segmentation is proposed, which starts from the center pixel of the image as the initial seed. It grows region according to the grow formula and selects the next seed from connected pixel of the region. We use intensity based similarity index for the grow formula and Otsu's Adaptive thresholding is used to calculate the stopping criteria for the grow formula. We apply the proposed method to the Berkley segmentation database images and discuss results based on Liu's F-factor that shows efficient segmentation.

RECENT SCHOLAR PUBLICATIONS

Highly specific blood cell classification using a novel deep learning framework with an interactive cloud-based GUI
A Shaik, A Tiwari, PK Jain, S Kumar, C Trujillo, E Banoth
Biomedical Signal Processing and Control 123, 110469 , 2026
2026
A triaxial accelerometer-based approach for motion noise resilient segmentation of seismocardiogram signal
S Sahoo, PK Jain
Physical and Engineering Sciences in Medicine, 1-16 , 2026
2026
Adaptive thresholding of DWT coefficients using UNet for denoising real-life respiratory sounds
S Behera, PK Jain, S Bakshi
Biomedical Signal Processing and Control 111, 108232 , 2026
2026
Citations: 1
AUGAN-Net: A Denoising Model Revolutionizing Automated Remote Heart Sound Signal Analysis
PK Dwibedy, PK Jain, S Bakshi
IEEE Transactions on Consumer Electronics 71 (4), 10530-10541 , 2025
2025
Citations: 1
Quantum LBP-driven heart sound analysis with quality assessment in real-world noisy environments
S Sahoo, PK Jain
Computer Methods and Programs in Biomedicine, 109082 , 2025
2025
A multi-objective approach for secure cluster based routing & attack classification in VANETs
A Behura, A Kumar, PK Jain
Peer-to-Peer Networking and Applications 18 (3), 119 , 2025
2025
Citations: 13
A comparative performance analysis of vehicular routing protocols in intelligent transportation systems
A Behura, A Kumar, PK Jain
Telecommunication Systems 88 (1), 26 , 2025
2025
Citations: 34
A Comparative Performance Assessment for Prediction of Loan Approval in Financial Sector
L Hota, PK Jain, A Kumar
Procedia Computer Science 258, 298-307 , 2025
2025
Citations: 9
Advancing Leukocyte Classification: A Cutting‐Edge Deep Learning Approach for AI‐Driven Clinical Diagnosis
A Shaik, A Tiwari, B Neelapu, PK Jain, E Banoth
International Journal of Imaging Systems and Technology 34 (6), e23204 , 2024
2024
Citations: 5
A Novel Deep Learning Framework for Enhanced Acute Lymphoblastic Leukemia Detection
S Ahmadsaidulu, AS Kanase, PK Jain, E Banoth
Laser Science, JW5A. 39 , 2024
2024
Emerging electrical and computer technologies for smart cities: modelling, solution techniques and applications
OP Mahela, B Khan, PK Jain
CRC Press , 2024
2024
Citations: 2
VANETs for Smart Cities: Opportunities and Upcoming Research Directions
A Behura, PK Jain, A Kumar
Emerging Electrical and Computer Technologies for Smart Cities, 69-97 , 2024
2024
Exploration of Impactful Brain Region and EEG Bands for Affective Computing
H Sonwani, E Banoth, PK Jain
Emerging Electrical and Computer Technologies for Smart Cities, 347-362 , 2024
2024
Time–Frequency Features for the Cardiovascular System
RR Choudhary, M Rani, PK Jain
Emerging Electrical and Computer Technologies for Smart Cities, 313-325 , 2024
2024
Vanets for smart cities
A Behura, PK Jain, A Kumar
Emerging Electrical and Computer Technologies for Smart Cities: Modelling … , 2024
2024
Citations: 3
Recent trends in artificial intelligence applications for smart cities: a review
L Hota, BP Nayak, PK Jain, A Kumar
Emerging Electrical and Computer Technologies for Smart Cities, 51-68 , 2024
2024
Citations: 5
Multidomain features for emotion recognition using EEG signals
S Saha, PK Jain
Emerging Electrical and Computer Technologies for Smart Cities, 326-346 , 2024
2024
Citations: 2
Simplernn based human emotion recognition using eeg signals
H Sonwani, E Banoth, PK Jain
International Conference on Computational Intelligence in Communications and … , 2024
2024
Citations: 1
Research challenges and future perspective for brain stroke lesions segmentation
A Saini, PK Jain
International Conference on Computational Intelligence in Communications and … , 2024
2024
A robust to noise classification method for the heart sound signals using deep learning
S Sahoo, KK Thakur, PK Jain
2024

MOST CITED SCHOLAR PUBLICATIONS

Heart monitoring systems - A review
PK Jain, AK Tiwari
Computers in Biology and Medicine 54, 1-13 , 2014
2014
Citations: 134
An adaptive thresholding method for the wavelet based denoising of phonocardiogram signal
PK Jain, AK Tiwari
Biomedical Signal Processing and Control 38, 388-399 , 2017
2017
Citations: 98
A simple single seeded region growing algorithm for color image segmentation using adaptive thresholding
OP Verma, M Hanmandlu, S Susan, M Kulkarni, PK Jain
2011 International Conference on Communication Systems and Network … , 2011
2011
Citations: 94
An adaptive single seed based region growing algorithm for color image segmentation
PK Jain, S Susan
2013 Annual IEEE India Conference (INDICON), 1-6 , 2013
2013
Citations: 44
Performance analysis of seismocardiography for heart sound signal recording in noisy scenarios
PK Jain, AK Tiwari, VS Chourasia
Journal of Medical Engineering & Technology, 1-13 , 2016
2016
Citations: 37
A comparative performance analysis of vehicular routing protocols in intelligent transportation systems
A Behura, A Kumar, PK Jain
Telecommunication Systems 88 (1), 26 , 2025
2025
Citations: 34
A novel method for suppression of motion artifacts from the seismocardiogram signal
PK Jain, AK Tiwari
2016 IEEE International Conference on Digital Signal Processing (DSP), 6-10 , 2016
2016
Citations: 23
An adaptive method for shrinking of wavelet coefficients for phonocardiogram denoising
PK Jain, AK Tiwari
2016 IEEE International Conference on Digital Signal Processing (DSP), 1-5 , 2016
2016
Citations: 22
A Robust Algorithm for Segmentation of Phonocardiography Signal Using Tunable Quality Wavelet Transform
PK Jain, AK Tiwari
Journal of Medical and Biological Engineering , 2017
2017
Citations: 19
A multi-objective approach for secure cluster based routing & attack classification in VANETs
A Behura, A Kumar, PK Jain
Peer-to-Peer Networking and Applications 18 (3), 119 , 2025
2025
Citations: 13
Heart sound classification using a hybrid of CNN and GRU deep learning models
RR Choudhary, MR Singh, PK Jain
Procedia Computer Science 235, 3085-3093 , 2024
2024
Citations: 13
A lightweight 1-d convolution neural network model for multi-class classification of heart sounds
PK Jain, RR Choudhary, MR Singh
2022 International Conference on Emerging Techniques in Computational … , 2022
2022
Citations: 13
An algorithm for automatic segmentation of heart sound signal acquired using seismocardiography
PK Jain, AK Tiwari
2016 International Conference on Systems in Medicine and Biology (ICSMB … , 2016
2016
Citations: 13
A Comparative Performance Assessment for Prediction of Loan Approval in Financial Sector
L Hota, PK Jain, A Kumar
Procedia Computer Science 258, 298-307 , 2025
2025
Citations: 9
Dealing with class imbalance in sentiment analysis using deep learning and SMOTE
S Kedas, A Kumar, PK Jain
Advances in Data Computing, Communication and Security: Proceedings of … , 2022
2022
Citations: 7
Seismocardiography: An alternate method to estimate electro-mechanical window
PK Jain, AK Tiwari, OL Bhagat
2016 International Conference on Systems in Medicine and Biology (ICSMB … , 2016
2016
Citations: 6
Advancing Leukocyte Classification: A Cutting‐Edge Deep Learning Approach for AI‐Driven Clinical Diagnosis
A Shaik, A Tiwari, B Neelapu, PK Jain, E Banoth
International Journal of Imaging Systems and Technology 34 (6), e23204 , 2024
2024
Citations: 5
Recent trends in artificial intelligence applications for smart cities: a review
L Hota, BP Nayak, PK Jain, A Kumar
Emerging Electrical and Computer Technologies for Smart Cities, 51-68 , 2024
2024
Citations: 5
Vanets for smart cities
A Behura, PK Jain, A Kumar
Emerging Electrical and Computer Technologies for Smart Cities: Modelling … , 2024
2024
Citations: 3
A robust to noise classification method for the heart sound signals using deep learning technique
S Sahoo, KK Thakur, PK Jain
Advances in Artificial Intelligence, 101-122 , 2024
2024
Citations: 3