S I Khan

Scopus Publications

Automated posterior myocardial infarction detection from vectorcardiogram and derived vectorcardiogram signals using MVFBSE-EWT method
Sibghatullah Inayatullah Khan, Ram Bilas Pachori
Digital Signal Processing A Review Journal, 2025
Rainfall Prediction Comparison Between Holt Winter’s and Long Short-term Memory, A Deep Learning Technique
Shravankumar Shivappa Masalvad, Sibghatullah Inyatullah Khan, Anamika Yadav
Iranian Journal of Science and Technology Transactions of Civil Engineering, 2025
Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm
C. Moorthy, Jampani Chandra Sekhar, Sibghatullah Inayatullah Khan, Gaurav Agrawal
Knowledge Based Systems, 2024
Automated Bundle Branch Block Detection Using Multivariate Fourier-Bessel Series Expansion-Based Empirical Wavelet Transform
Sibghatullah I. Khan, Ram Bilas Pachori
IEEE Transactions on Artificial Intelligence, 2024
Bundle branch block (BBB) refers to cardiac condition that causes a delay in the path of electrical impulses, which makes it difficult for the heart to pump blood efficiently throughout the body. Early diagnosing BBB is important in cases where prior heart anomalies exist. Generally, the 12-lead electrocardiogram (ECG) is used to detect the BBB. To ease the ECG recording procedure, vectorcardiography (VCG) has been proposed with three leads ECG system. Manual diagnosis of BBB using ECG is subjective to the expertise of the doctor. To facilitate the doctors, in the present study, we have proposed a novel framework to automatically detect BBB from VCG signals using multivariate Fourier–Bessel series expansion-based empirical wavelet transform (MVFBSE-EWT). The MVFBSE-EWT is applied over the three channels of VCG signal, which results in the varying number of multivariate Fourier–Bessel intrinsic mode functions (MVFBIMFs). To process further, first six number of MVFBIMFs are selected due to their presence in the entire dataset. Each MVFBIMF is represented in higher dimensional phase space. From each phase space trajectory, fractal dimension (FD) is computed with three scales. The feature space is reduced with metaheuristic feature selection algorithm.
Low-error Fixed-Width Booth Multiplier Using Approximation Of Carry Function
Ganjikunta Ganesh Kumar, Sibghatullah Inyatullah Khan, G Prasad Acharya, Shravan Kumar S M
Journal of Applied Science and Engineering, 2024
This paper introduces an innovative solution for increasing precision of fixed-width radix-4 Booth multipliers through variable error compensation functions that leverage Approximation of Carry Function (ACF). Error compensation mechanisms typically comprise two carries-ideal and base carry functions–strategically chosen to minimize mean error. We present three distinct methods-ACF-1, ACF-2, and ACF-3—each employing fixed base values with varying column information (w) and bit lengths (N). Comparative analyses against recent studies demonstrate that our proposed fixed-width Booth multiplier using ACF-1 stands out in terms of accuracy and efficiency tradeoffs.
Multisensory Approach for Stopped Vehicle Detection on Highways to Enhance Collision Prevention
Ganjikunta Ganesh Kumar, Sibghatullah Inyatullah Khan, Mohammed Mahaboob Basha, Mallikarjun Mudda
2nd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2024 Proceedings, 2024
Road accidents caused by stopped vehicles pose a significant threat to road safety. Traditional detection systems, often relying on single-sensor technologies, suffer from limitations such as poor performance in adverse weather conditions and high false positive rates. This research presents a novel multisensory detection system designed to improve the accuracy and reliability of detecting stopped vehicles on highways. By combining multiple sensor modalities, the system effectively overcomes the limitations of individual sensors. Experimental results demonstrate that the multisensory approach surpasses traditional radar and camera-based systems, achieving a remarkable detection accuracy of $\\mathbf{9 5 \\%}$. This significant improvement can contribute to a significant reduction in accidents caused by stopped vehicles. The proposed multisensory detection system offers a promising solution for enhancing road safety and improving traffic management systems.
Automated Eye Movement Classification Based on EMG of EOM Signals Using FBSE-EWT Technique
Sibghatullah Inayatullah Khan, Ram Bilas Pachori
IEEE Transactions on Human Machine Systems, 2023
The accurate automated eye movement classification is gaining importance in the field of human–computer interaction (HCI). The present article aims at the classification of six types of eye movements from electromyogram (EMG) of extraocular muscles (EOM) signals using the Fourier–Bessel series expansion-based empirical wavelet transform (FBSE-EWT) with time and frequency-domain (TAFD) features. The FBSE-EWT of EMG signals results in Fourier–Bessel intrinsic mode functions (FBIMFs), which correspond to the frequency contents in the signal. A hybrid approach is used to select the prominent FBIMFs followed by the statistical and signal complexity-based feature extraction. Furthermore, metaheuristic optimization algorithms are employed to reduce the feature space dimension. The discrimination ability of the reduced feature set is verified by Kruskal–Wallis statistical test. Multiclass support vector machine (MSVM) has been employed for classification. First, the classification has been performed with TAFD features followed by the combination of TAFD and FBSE-EWT-based reduced feature set. The combination of TAFD and FBSE-EWT-based feature set has provided good classification performance. This study demonstrates the efficacy of FBSE-EWT and subsequent metaheuristic feature selection algorithms in classifying the eye movements from EMG of EOM signals. The combination of TAFD and the selected features through salp swarm optimization algorithm has provided maximum classification accuracy of 98.91% with MSVM employing Gaussian and radial basis function kernels. Thus, the proposed approach has the potential to be used in HCI applications involving biomedical signals.
Heart Disease Identification Based on Butterfly Optimization and Machine Learning
Manal Asrar, Joud Bawazir, Sibghatullah I. Khan, Saeed Mian Qaisar
International Conference on Smart Computing and Application Icsca 2023, 2023
This paper aims to make use of The Physionet Challenge 2016 collection of normal and abnormal heart sound recordings that were classified by automated identification of PCG sounds to help detect heart diseases earlier and prevent incidents. People with heart diseases have been increasing and most of them lead to fatalities so the detection of sound signals through PCGs can be applied in machine learning models by extracting features from data. In this study, the dataset's recordings are segmented to be used in variational mode decomposition. Once they are decomposed, those means will be fused together into a set of features given to the Butterfly Optimization Algorithm which will conduct a selection of features. As the features are selected, MATLAB was used to test various machine learning algorithms. Results from Support Vector Machines (SVM) and artificial neural networks were used in this investigation (ANN). The ANN model, which had an accuracy rate of 94.8%, was the most accurate of them.
Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition
Saeed Mian Qaisar, Sibghatulla I. Khan, Kathiravan Srinivasan, Moez Krichen
Journal of King Saud University Computer and Information Sciences, 2023
The concept of mobile healthcare systems is promising. It is based on the cloud connected wireless biomedical wearables. In this scenario, the compression, processing, transmission and power effectiveness with precision are the key terms. A novel technique is presented for arrhythmia identification by processing the electrocardiogram signals. The solution is based on an effective hybridization of the multirate processing, QRS selection, variational mode decomposition, features mining from Modes, Metaheuristic optimization based features selection, and machine learning algorithms. The MIT-BIH dataset is used for experimentation. Performance of the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms is investigated for features selection. A multi-subjects and multi-class dataset is used for testing the performance of classification by following the 10-fold cross validation strategy. The multirate processing with QRS selection and Metaheuristic optimization dependent features selection bring compression and aptitude for the processing and data transmission efficiencies. The system efficiently incorporates the multirate processing while securing an effective signal reconstruction. The respective compression gains and classification accuracies for the Butterfly Optimization Algorithm, Manta Ray Foraging Optimization, and Emperor Penguin Optimization algorithms are 27-fold, 29.45-fold & 46.29-fold and 99.14%, 99.08% & 98.65%.
EEG Signal based Schizophrenia Recognition by using VMD Rose Spiral Curve Butterfly Optimization and Machine Learning
Sibghatullah I. Khan, Saeed Mian Qaisar, Alberto López, Humaira Nisar, Francisco Ferrero
Conference Record IEEE Instrumentation and Measurement Technology Conference, 2023
Schizophrenia is a mental illness that can negatively impact a patient's mental abilities, emotional propensities, and the standard of their private and social lives. Processing EEG data has evolved into a useful tool for tracking and identifying psychological brain states. In this framework, this paper focus on developing an automated approach for recognizing schizophrenia using non-invasive EEG signals. The EEG signals are segmented and onward decomposed by using the Variational Mode Decomposition (VMD). Each mode is termed a variational mode function (VMF). Onward, features from each intended VMF are mined based on a Rose Spiral Curve (RSC). The mined features are concatenated to present an instance. Afterward, the most pertinent features are selected using the Butterfly Optimization Algorithm (BOA). The selected feature set is conveyed to the classification module. Two classification approaches are applied in this study namely, the k-nearest neighbor (k-NN) and Random Forest (RF). The applicability is tested by using a publicly available EEG schizophrenia dataset. The highest accuracy of 89.0 % is secured for the case of RF.
Empirical wavelet transform-Based framework for diagnosis of epilepsy using EEG signals
Sibghatullah I. Khan, Ram Bilas Pachori
AI Enabled Smart Healthcare Using Biomedical Signals, 2022
Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare
Saeed Mian Qaisar, Sibghatullah I. Khan, Dominique Dallet, Ryszard Tadeusiewicz, Paweł Pławiak
Biocybernetics and Biomedical Engineering, 2022
Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features
Sibghatullah I. Khan, Saeed Mian Qaisar, Ram Bilas Pachori
Biomedical Signal Processing and Control, 2022
Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning
Sibghatullah I. Khan, Shruti Bhargava Choubey, Abhishek Choubey, Abhishek Bhatt, Pandya Vyomal Naishadhkumar, Mohammed Mahaboob Basha
Concurrent Engineering Research and Applications, 2022
Automated classification of lung sound signals based on empirical mode decomposition
Sibghatullah I. Khan, Ram Bilas Pachori
Expert Systems with Applications, 2021
Derived vectorcardiogram based automated detection of posterior myocardial infarction using FBSE-EWT technique
Sibghatullah I. Khan, Ram Bilas Pachori
Biomedical Signal Processing and Control, 2021
Analysis of normal and adventitious lung sound signals using empirical mode decomposition and central tendency measure
Sibghatullah I. Khan, Ganjikunta Ganesh Kumar, Pandya Vyomal Naishadkumar, Sarvade Pedda Subba Rao
Traitement Du Signal, 2021
Automated Detection of Posterior Myocardial Infarction from Vectorcardiogram Signals Using Fourier-Bessel Series Expansion Based Empirical Wavelet Transform
Sibghatullah I. Khan, Ram Bilas Pachori
IEEE Sensors Letters, 2021
Design of energy and EDP efficient 1-bit full subtractor based divider circuits for computing systems
M. Mahaboob Basha, Srinivasulu Gundala, Sibghatullah I. Khan
International Journal of System of Systems Engineering, 2021
Preliminary detection of lung diseases in pediatric population using soft computing
Sibghatullah Khan*, , Syed Jahangir Badashah, Mallikarjun Mudda, , and
International Journal of Recent Technology and Engineering, 2019
Area and power efficient pipeline FFT architecture for QPSK-OFDM
Ganesh Kumar G and
International Journal of Advanced Trends in Computer Science and Engineering, 2019
Preliminary diagnosis of coronary artery disease from human heart sounds:A signal processing prospective
International Journal of Advanced Trends in Computer Science and Engineering, 2019
A high-performance signed-unsigned multiplier using vedic mathematics
Ganesh Kumar Ganjikunta, Sibghatullah I. Khan, M. Mahaboob Basha
Journal of Low Power Electronics, 2019
Three-Tier authentication and secure key exchange over insecure channel
Muzammil M. Ahmad, Sibghatullah I. Khan
Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence Icbdaci 2017, 2017
Application of signal processing techniques for preliminary detection of adventitious lung sounds in paediatric population using electronic stethoscope
Sibghatullah I. Khan, Vasif Ahmed, Naresh P. Jawarkar
Proceedings of the 2017 International Conference on Big Data Analytics and Computational Intelligence Icbdaci 2017, 2017
Investigation of some features for preliminary detection of coronary artery disease using electronic stethoscope
Sibghatullah I. Khan, Vasif Ahmed
2016 International Conference on Emerging Trends in Communication Technologies Etct 2016, 2017
Classification of pulmonary crackles and pleural friction rubs using MFCC statistical parameters
Sibghatullah I. Khan, Vasif Ahmed
2016 International Conference on Advances in Computing Communications and Informatics Icacci 2016, 2016
Study of effectiveness of stockwell transform for detection of coronary artery disease from heart sounds
Sibghatullah I. Khan, Vasif Ahmed
Proceedings of the 2016 2nd International Conference on Contemporary Computing and Informatics Ic3i 2016, 2016
Study of electronic stethoscope as prospective analysis tool for cardiac sounds
Sibghatullah I. Khan, Vasif Ahmed
Communications in Computer and Information Science, 2016
Cell phone based remote early detection of respiratory disorders for rural children using modified stethoscope
Sibghatullah I. Khan, Naresh P. Jawarkar, Vasif Ahmed
Proceedings International Conference on Communication Systems and Network Technologies Csnt 2012, 2012

RECENT SCHOLAR PUBLICATIONS

Automated posterior myocardial infarction detection from vectorcardiogram and derived vectorcardiogram signals using MVFBSE-EWT method
SI Khan, RB Pachori
Digital Signal Processing 163, 105244 , 2025
2025
Citations: 9
Rainfall Prediction Comparison Between Holt Winter’s and Long Short-term Memory, A Deep Learning Technique
SS Masalvad, SI Khan, A Yadav
Iranian Journal of Science and Technology, Transactions of Civil Engineering … , 2025
2025
Citations: 7
Low-error Fixed-Width Booth Multiplier Using Approximation Of Carry Function
GG Kumar, SI Khan, GP Acharya, SK SM
Journal of Applied Science and Engineering 28 (10) , 2025
2025
Real-Time Road Surface Analysis for Pothole Detection
GG Kumar, SI Khan, MM Basha, M Mudda, LP Yarrapathni
ITM Web of Conferences 74, 03001 , 2025
2025
Citations: 2
Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm
C Moorthy, JC Sekhar, SI Khan, G Agrawal
Knowledge-Based Systems 302, 112362 , 2024
2024
Citations: 17
Multisensory Approach for Stopped Vehicle Detection on Highways to Enhance Collision Prevention
GG Kumar, SI Khan, MM Basha, M Mudda
2024 2nd International Conference on Self Sustainable Artificial … , 2024
2024
Automated Bundle Branch Block Detection Using Multivariate Fourier–Bessel Series Expansion-Based Empirical Wavelet Transform
RBP Sibghatullah I. Khan
IEEE Transactions on Artificial Intelligence 5 (11), 5643 - 5654 , 2024
2024
Citations: 14
EEG signal based schizophrenia recognition by using VMD rose spiral curve butterfly optimization and machine learning
SI Khan, SM Qaisar, A López, H Nisar, F Ferrero
2023 IEEE International Instrumentation and Measurement Technology … , 2023
2023
Citations: 4
Automated eye movement classification based on EMG of EOM signals using FBSE-EWT technique
SI Khan, RB Pachori
IEEE Transactions on Human-Machine Systems 53 (2), 346-356 , 2023
2023
Citations: 18
Heart Disease Identification Based on Butterfly Optimization and Machine Learning
M Asrar, J Bawazir, SI Khan, SM Qaisar
2023 International Conference on Smart Computing and Application (ICSCA), 1-7 , 2023
2023
Citations: 4
Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition
SM Qaisar, SI Khan, K Srinivasan, M Krichen
Journal of King Saud University-Computer and Information Sciences 35 (1), 26-37 , 2023
2023
Citations: 100
Energy Efficient FIR Filter Design Using Distributed Arithmetic
SIK GANJIKUNTA Ganesh Kumar, MOHAMMED Mahaboob Basha
J. Shanghai Jiao Tong Univ. (Sci.), , 2022
2022
Empirical Wavelet Transform-Based Framework for Diagnosis of Epilepsy Using EEG Signals
RBP Sibghatullah I. Khan
AI-Enabled Smart Healthcare Using Biomedical Signals , 2022
2022
Citations: 11
Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare
SM Qaisar, SI Khan, D Dallet, R Tadeusiewicz, P Pławiak
Biocybernetics and Biomedical Engineering 42 (2), 681-694 , 2022
2022
Citations: 53
Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features
SI Khan, SM Qaisar, RB Pachori
Biomedical Signal Processing and Control 73, 103445 , 2022
2022
Citations: 53
Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning
SI Khan, SB Choubey, A Choubey, A Bhatt, PV Naishadhkumar, ...
Concurrent Engineering 30 (1), 103-115 , 2022
2022
Citations: 43
Automated classification of lung sound signals based on empirical mode decomposition
SI Khan, RB Pachori
Expert Systems with Applications 184, 115456 , 2021
2021
Citations: 63
Derived vectorcardiogram based automated detection of posterior myocardial infarction using FBSE-EWT technique
SI Khan, RB Pachori
Biomedical Signal Processing and Control 70, 103051 , 2021
2021
Citations: 32
Analysis of Normal and Adventitious Lung Sound Signals Using Empirical Mode Decomposition and Central Tendency Measure.
SI Khan, GG Kumar, PV Naishadkumar, SPS Rao
Traitement du Signal 38 (3), 731-738 , 2021
2021
Citations: 11
Automated detection of posterior myocardial infarction from vectorcardiogram signals using Fourier–Bessel series expansion based empirical wavelet transform
SI Khan, RB Pachori
IEEE Sensors Letters 5 (5), 1-4 , 2021
2021
Citations: 38

MOST CITED SCHOLAR PUBLICATIONS

Arrhythmia classification using multirate processing metaheuristic optimization and variational mode decomposition
SM Qaisar, SI Khan, K Srinivasan, M Krichen
Journal of King Saud University-Computer and Information Sciences 35 (1), 26-37 , 2023
2023
Citations: 100
Automated classification of lung sound signals based on empirical mode decomposition
SI Khan, RB Pachori
Expert Systems with Applications 184, 115456 , 2021
2021
Citations: 63
Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare
SM Qaisar, SI Khan, D Dallet, R Tadeusiewicz, P Pławiak
Biocybernetics and Biomedical Engineering 42 (2), 681-694 , 2022
2022
Citations: 53
Automated classification of valvular heart diseases using FBSE-EWT and PSR based geometrical features
SI Khan, SM Qaisar, RB Pachori
Biomedical Signal Processing and Control 73, 103445 , 2022
2022
Citations: 53
Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning
SI Khan, SB Choubey, A Choubey, A Bhatt, PV Naishadhkumar, ...
Concurrent Engineering 30 (1), 103-115 , 2022
2022
Citations: 43
Automated detection of posterior myocardial infarction from vectorcardiogram signals using Fourier–Bessel series expansion based empirical wavelet transform
SI Khan, RB Pachori
IEEE Sensors Letters 5 (5), 1-4 , 2021
2021
Citations: 38
Derived vectorcardiogram based automated detection of posterior myocardial infarction using FBSE-EWT technique
SI Khan, RB Pachori
Biomedical Signal Processing and Control 70, 103051 , 2021
2021
Citations: 32
Cell phone based remote early detection of respiratory disorders for rural children using modified stethoscope
SI Khan, NP Jawarkar, V Ahmed
2012 International Conference on Communication Systems and Network … , 2012
2012
Citations: 21
Automated eye movement classification based on EMG of EOM signals using FBSE-EWT technique
SI Khan, RB Pachori
IEEE Transactions on Human-Machine Systems 53 (2), 346-356 , 2023
2023
Citations: 18
Optimized brain tumor identification via graph sample and aggregate-attention network with Artificial Lizard Search Algorithm
C Moorthy, JC Sekhar, SI Khan, G Agrawal
Knowledge-Based Systems 302, 112362 , 2024
2024
Citations: 17
Design of energy and EDP efficient 1-bit full subtractor based divider circuits for computing systems
MM Basha, S Gundala, SI Khan
International Journal of System of Systems Engineering 11 (3-4), 257-267 , 2021
2021
Citations: 15
Automated Bundle Branch Block Detection Using Multivariate Fourier–Bessel Series Expansion-Based Empirical Wavelet Transform
RBP Sibghatullah I. Khan
IEEE Transactions on Artificial Intelligence 5 (11), 5643 - 5654 , 2024
2024
Citations: 14
A high-performance signed-unsigned multiplier using Vedic mathematics
GK Ganjikunta, SI Khan, MM Basha
Journal of Low Power Electronics 15 (3), 302-308 , 2019
2019
Citations: 13
Empirical Wavelet Transform-Based Framework for Diagnosis of Epilepsy Using EEG Signals
RBP Sibghatullah I. Khan
AI-Enabled Smart Healthcare Using Biomedical Signals , 2022
2022
Citations: 11
Analysis of Normal and Adventitious Lung Sound Signals Using Empirical Mode Decomposition and Central Tendency Measure.
SI Khan, GG Kumar, PV Naishadkumar, SPS Rao
Traitement du Signal 38 (3), 731-738 , 2021
2021
Citations: 11
Automated posterior myocardial infarction detection from vectorcardiogram and derived vectorcardiogram signals using MVFBSE-EWT method
SI Khan, RB Pachori
Digital Signal Processing 163, 105244 , 2025
2025
Citations: 9
Study of effectiveness of stockwell transform for detection of coronary artery disease from heart sounds
SI Khan, V Ahmed
2016 2nd international conference on contemporary computing and informatics … , 2016
2016
Citations: 8
Rainfall Prediction Comparison Between Holt Winter’s and Long Short-term Memory, A Deep Learning Technique
SS Masalvad, SI Khan, A Yadav
Iranian Journal of Science and Technology, Transactions of Civil Engineering … , 2025
2025
Citations: 7
Preliminary Diagnosis of Coronary Artery Disease from Human Heart Sounds: A Signal Processing Prospective
GGK Sibghatullah I Khan, Vasif Ahmed ,M Mahaboob Basha
International Journal of Advanced Trends in Computer Science and Engineering … , 2019
2019
Citations: 7
Investigation of some features for preliminary detection of coronary artery disease using electronic stethoscope
SI Khan, V Ahmed
2016 International Conference on Emerging Trends in Communication … , 2016
2016
Citations: 7

S I Khan

EDUCATION

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS