12 Biomedical Engineering Fundamentals
605
60. M. Kumar, R.B. Pachori, U.R. Acharya, Characterization of coronary artery disease using
flexible analytic wavelet transform applied on ECG signals. Biomed. Signal Process. Control
31, 301–308 (2017)
61. M. Kumar, R.B. Pachori, U.R. Acharya, An efficient automated technique for CAD diagnosis
using flexible analytic wavelet transform and entropy features extracted from HRV signals.
Expert Syst. Appl. 63, 165–172 (2016)
62. V. Bajaj, R.B. Pachori, Classification of seizure and nonseizure EEG signals using empirical
mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012)
63. R. Sharma, R.B. Pachori, Classification of epileptic seizures in EEG signals based on phase
space representation of intrinsic mode functions. Expert Syst. Appl. 42(3), 1106–1117 (2015)
64. R.B. Pachori, S. Patidar, Epileptic seizure classification in EEG signals using second-order
difference plot of intrinsic mode functions. Comput. Methods Prog. Biomed. 113(2), 494–502
(2014)
65. R. Sharma, R.B. Pachori, U.R. Acharya, Application of entropy measures on intrinsic mode
functions for the automated identification of focal electroencephalogram signals. Entropy
17(2), 669–691 (2015)
66. V. Joshi, R.B. Pachori, A. Vijesh, Classification of ictal and seizure-free EEG signals using
fractional linear prediction. Biomed. Signal Process. Control 9, 1–5 (2014)
67. V. Bajaj, R.B. Pachori, Automatic classification of sleep stages based on the time-frequency
image of EEG signals. Comput. Methods Prog. Biomed. 112(3), 320–328 (2013)
68. R. Sharma, R.B. Pachori, A. Upadhyay, Automatic sleep stages classification based on iterative
filtering of electroencephalogram signals. Neural Comput. & Applic. 28(10), 2959–2978
(2017)
69. A. Subasi, M. Yilmaz, H.R. Ozcalik, Classification of EMG signals using wavelet neural
network. J. Neurosci. Methods 156(1–2), 360–367 (2006)
70. E. Gokgoz, A. Subasi, Comparison of decision tree algorithms for EMG signal classification
using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)
71. A. Subasi, M.K. Kiymik, Muscle fatigue detection in EMG using time–frequency methods,
ICA and neural networks. J. Med. Syst. 34(4), 777–785 (2010)
72. M. Gonzalez-Izal et al., EMG spectral indices and muscle power fatigue during dynamic
contractions. J. Electromyogr. Kinesiol. 20(2), 233–240 (2010)
73. A. Nishad, A. Upadhyay, R.B. Pachori, U.R. Acharya, Automated classification of hand
movements using tunable-Q wavelet transform based filter-bank with surface electromyogram
signals. Futur. Gener. Comput. Syst. 93, 96–110 (2019)
74. R.B. Pachori, D. Hewson, Assessment of the effects of sensory perturbations using Fourier–
Bessel expansion method for postural stability analysis. J. Intell. Syst. 20(2), 167–186 (2011)
75. G.F. Harris, S.A. Riedel, D. Matesi, P. Smith, Standing postural stability assessment and signal
stationarity in children with cerebral palsy. IEEE Trans. Rehabil. Eng. 1(1), 35–42 (1993)
76. R.B. Pachori, D. Hewson, H. Snoussi, J. Duchêne, Postural time-series analysis using empirical
mode decomposition and second-order difference plots, in International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2009), 2009, pp. 537–540
77. M. Sharma, P. Sharma, R.B. Pachori, V.M. Gadre, Double density dual-tree complex wavelet
transform-based features for automated screening of knee-joint vibroarthrographic signals, in
Machine Intelligence and Signal Analysis, (Springer, Singapore, 2009), pp. 279–290
78. M. Merino, O. Rivera, I. Gómez, A. Molina, E. Dorronzoro, A method of EOG signal
processing to detect the direction of eye movements, in First International Conference on
Sensor Device Technologies and Applications (SENSORDEVICES), 2010, pp. 100–105
605
60. M. Kumar, R.B. Pachori, U.R. Acharya, Characterization of coronary artery disease using
flexible analytic wavelet transform applied on ECG signals. Biomed. Signal Process. Control
31, 301–308 (2017)
61. M. Kumar, R.B. Pachori, U.R. Acharya, An efficient automated technique for CAD diagnosis
using flexible analytic wavelet transform and entropy features extracted from HRV signals.
Expert Syst. Appl. 63, 165–172 (2016)
62. V. Bajaj, R.B. Pachori, Classification of seizure and nonseizure EEG signals using empirical
mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012)
63. R. Sharma, R.B. Pachori, Classification of epileptic seizures in EEG signals based on phase
space representation of intrinsic mode functions. Expert Syst. Appl. 42(3), 1106–1117 (2015)
64. R.B. Pachori, S. Patidar, Epileptic seizure classification in EEG signals using second-order
difference plot of intrinsic mode functions. Comput. Methods Prog. Biomed. 113(2), 494–502
(2014)
65. R. Sharma, R.B. Pachori, U.R. Acharya, Application of entropy measures on intrinsic mode
functions for the automated identification of focal electroencephalogram signals. Entropy
17(2), 669–691 (2015)
66. V. Joshi, R.B. Pachori, A. Vijesh, Classification of ictal and seizure-free EEG signals using
fractional linear prediction. Biomed. Signal Process. Control 9, 1–5 (2014)
67. V. Bajaj, R.B. Pachori, Automatic classification of sleep stages based on the time-frequency
image of EEG signals. Comput. Methods Prog. Biomed. 112(3), 320–328 (2013)
68. R. Sharma, R.B. Pachori, A. Upadhyay, Automatic sleep stages classification based on iterative
filtering of electroencephalogram signals. Neural Comput. & Applic. 28(10), 2959–2978
(2017)
69. A. Subasi, M. Yilmaz, H.R. Ozcalik, Classification of EMG signals using wavelet neural
network. J. Neurosci. Methods 156(1–2), 360–367 (2006)
70. E. Gokgoz, A. Subasi, Comparison of decision tree algorithms for EMG signal classification
using DWT. Biomed. Signal Process. Control 18, 138–144 (2015)
71. A. Subasi, M.K. Kiymik, Muscle fatigue detection in EMG using time–frequency methods,
ICA and neural networks. J. Med. Syst. 34(4), 777–785 (2010)
72. M. Gonzalez-Izal et al., EMG spectral indices and muscle power fatigue during dynamic
contractions. J. Electromyogr. Kinesiol. 20(2), 233–240 (2010)
73. A. Nishad, A. Upadhyay, R.B. Pachori, U.R. Acharya, Automated classification of hand
movements using tunable-Q wavelet transform based filter-bank with surface electromyogram
signals. Futur. Gener. Comput. Syst. 93, 96–110 (2019)
74. R.B. Pachori, D. Hewson, Assessment of the effects of sensory perturbations using Fourier–
Bessel expansion method for postural stability analysis. J. Intell. Syst. 20(2), 167–186 (2011)
75. G.F. Harris, S.A. Riedel, D. Matesi, P. Smith, Standing postural stability assessment and signal
stationarity in children with cerebral palsy. IEEE Trans. Rehabil. Eng. 1(1), 35–42 (1993)
76. R.B. Pachori, D. Hewson, H. Snoussi, J. Duchêne, Postural time-series analysis using empirical
mode decomposition and second-order difference plots, in International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2009), 2009, pp. 537–540
77. M. Sharma, P. Sharma, R.B. Pachori, V.M. Gadre, Double density dual-tree complex wavelet
transform-based features for automated screening of knee-joint vibroarthrographic signals, in
Machine Intelligence and Signal Analysis, (Springer, Singapore, 2009), pp. 279–290
78. M. Merino, O. Rivera, I. Gómez, A. Molina, E. Dorronzoro, A method of EOG signal
processing to detect the direction of eye movements, in First International Conference on
Sensor Device Technologies and Applications (SENSORDEVICES), 2010, pp. 100–105