| Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms PA Karthick, DM Ghosh, S Ramakrishnan Computer methods and programs in biomedicine 154, 45-56, 2018 | 180 | 2018 |
| Surface electromyography based muscle fatigue progression analysis using modified B distribution time–frequency features PA Karthick, S Ramakrishnan Biomedical signal processing and control 26, 42-51, 2016 | 59 | 2016 |
| Analysis of dynamics of EMG signal variations in fatiguing contractions of muscles using transition network approach N Makaram, PA Karthick, R Swaminathan IEEE Transactions on Instrumentation and Measurement 70, 1-8, 2021 | 38 | 2021 |
| Analysis of muscle fatigue progression using cyclostationary property of surface electromyography signals PA Karthick, G Venugopal, S Ramakrishnan Journal of medical systems 40, 1-11, 2016 | 37 | 2016 |
| Prediction of secondary generalization from a focal onset seizure in intracerebral EEG PA Karthick, H Tanaka, HM Khoo, J Gotman Clinical Neurophysiology 129 (5), 1030-1040, 2018 | 30 | 2018 |
| Muscle fatigue analysis using surface EMG signals and time–frequency based medium‐to‐low band power ratio PA Karthick, S Ramakrishnan Electronics Letters 52 (3), 185-186, 2016 | 25 | 2016 |
| Analysis of biceps brachii sEMG signal using Multiscale Fuzzy Approximate Entropy M Navaneethakrishna, PA Karthick, S Ramakrishnan 2015 37th Annual International Conference of the IEEE Engineering in …, 2015 | 20 | 2015 |
| Analysis of surface EMG signals under fatigue and non-fatigue conditions using B-distribution based quadratic time frequency distribution PA Karthick, G Venugopal, S Ramakrishnan Journal of Mechanics in Medicine and Biology 15 (02), 1540028, 2015 | 20 | 2015 |
| Automated detection of subthalamic nucleus in deep brain stimulation surgery for Parkinson’s disease using microelectrode recordings and wavelet packet features PA Karthick, KR Wan, ASA Qi, J Dauwels, NKK King Journal of Neuroscience Methods 343, 108826, 2020 | 18 | 2020 |
| Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features PA Karthick, N Makaram, S Ramakrishnan 2014 36th Annual International Conference of the IEEE Engineering in …, 2014 | 18 | 2014 |
| Could we have missed out the seizure onset: A study based on intracranial EEG PA Karthick, H Tanaka, HM Khoo, J Gotman Clinical Neurophysiology 131 (1), 114-126, 2020 | 15 | 2020 |
| Analysis of muscle fatigue conditions using time-frequency images and GLCM features PA Karthick, M Navaneethakrishna, N Punitha, ARJ Fredo, ... Current Directions in Biomedical Engineering 2 (1), 483-487, 2016 | 15 | 2016 |
| Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals PA Karthick, S Ramakrishnan Biomedical Signal Processing and Control 68, 102603, 2021 | 14 | 2021 |
| Detection of subthalamic nucleus using time-frequency features of microelectrode recordings and random forest classifier PA Karthick, KR Wan, R Yuvaraj, AAQ See, NKK King, J Dauwels 2019 41st annual international conference of the IEEE Engineering in …, 2019 | 14 | 2019 |
| Automated detection of muscle fatigue conditions from cyclostationary based geometric features of surface electromyography signals PA Karthick, S Ramakrishnan Computer methods in biomechanics and biomedical engineering, 1-13, 2021 | 12* | 2021 |
| Analysis of fatigue conditions in triceps brachii muscle using sEMG signals and spectral correlation density function K Marri, KM Navaneetha, J Jose, PA Karthick, S Ramakrishnan 2014 International Conference on Informatics, Electronics & Vision (ICIEV), 1-4, 2014 | 12 | 2014 |
| Surface electromyography-based muscle fatigue analysis using binary and weighted visibility graph features N Makaram, PA Karthick, V Gopinath, R Swaminathan Fluctuation and Noise Letters 20 (02), 2150016, 2021 | 11 | 2021 |
| Variational mode decomposition based differentiation of fatigue conditions in muscles using surface electromyography signals DB Krishnamani, K PA, R Swaminathan IET Signal Processing 14 (10), 745-753, 2020 | 11 | 2020 |
| Automated detection of preterm condition using uterine electromyography based topological features S Vinothini, N Punitha, PA Karthick, S Ramakrishnan Biocybernetics and Biomedical Engineering 41 (1), 293-305, 2021 | 10 | 2021 |
| Analysis of frequency bands of uterine electromyography signals for the detection of preterm birth V Selvaraju, PA Karthick, R Swaminathan Public Health and Informatics, 283-287, 2021 | 8 | 2021 |
Navaneethakrishna MakaramBoston Childrens Hospital, Harvard Medical School在 childrens.harvard.edu 的电子邮件经过验证
