604
R. B. Pachori and V. Gupta
36. G. Keiser, Biophotonics – Concepts to Applications (Springer Singapore, Singapore, 2016)
37. M. Kutz, Standard Handbook of Biomedical Engineering and Design (McGraw-Hill, New
York, 2003)
38. A. Phinyomark, P. Phukpattaranont, C. Limsakul, Feature reduction and selection for EMG
signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)
39. T.N.S. Tengku Zawawi et al., A review of electromyography signal analysis techniques for
musculoskeletal disorders. Indones. J. Electr. Eng. Comput. Sci. 11(3), 1136 (2018)
40. B. Boashash, Time-Frequency Signal Analysis and Processing: a Comprehensive Reference
(Academic Press, Amsterdam, 2015)
41. N.E. Huang, Hilbert-Huang Transform and Its Applications, vol 16 (World Scientific, Hackensack, 2014)
42. C.L. Nikias, J.M. Mendel, Signal processing with higher-order spectra. IEEE Signal Process.
Mag. 10(3), 10–37 (1993)
43. C.L. Phillips, J.M. Parr, E.A. Riskin, Signals, Systems, and Transforms (Prentice Hall, Upper
Saddle River, 1995)
44. S. Mukhopadhyay, P. Sircar, Parametric modelling of ECG signal. Med. Biol. Eng. Comput.
34(2), 171–174 (1996)
45. R.V. Andreao, B. Dorizzi, J. Boudy, ECG signal analysis through hidden Markov models. IEEE
Trans. Biomed. Eng. 53(8), 1541–1549 (2006)
46. P.E. McSharry, G.D. Clifford, L. Tarassenko, L.A. Smith, A dynamical model for generating
synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003)
47. J.-C. Nunes, A. Nait-Ali, Hilbert transform-based ECG modeling. Biomed. Eng. 39(3), 133–
137 (2005)
48. J. Pardey, S. Roberts, L. Tarassenko, A review of parametric modelling techniques for EEG
analysis. Med. Eng. Phys. 18(1), 2–11 (1996)
49. O.A. Elsayed, A. Eldeib, F. Elhefnawi, Parametric modeling of ICTAL epilepsy EEG signal
using Prony method. Int. J. Comput. Sci. Softw. Eng. (IJCSSE) 3(1), 86–89 (2014)
50. D. Graupe, K.H. Kohn, A. Kralj, S. Basseas, Patient controlled electrical stimulation via EMG
signature discrimination for providing certain paraplegics with primitive walking functions. J.
Biomed. Eng. 5(3), 220–226 (1983)
51. M.H. Sherif, R.J. Gregor, J. Lyman, Effects of load on myoelectric signals: the ARIMA
representation. IEEE Trans. Biomed. Eng. 5, 411–416 (1981)
52. A. Iwata, N. Suzumura, K. Ikegaya, Pattern classification of the phonocardiogram using linear
prediction analysis. Med. Biol. Eng. Comput. 15(4), 407–412 (1977)
53. A. Iwata, N. Ishii, N. Suzumura, K. Ikegaya, Algorithm for detecting the first and the second
heart sounds by spectral tracking. Med. Biol. Eng. Comput. 18(1), 19–26 (1980)
54. Z. Moussavi, Fundamentals of respiratory sounds and analysis. Synth. Lect. Biomed. Eng. 1(1),
1–68 (2006)
55. R.R. Sharma, A. Kumar, R.B. Pachori, U.R. Acharya, Accurate automated detection of
congestive heart failure using eigenvalue decomposition based features extracted from HRV
signals. Biocybern. Biomed. Eng. 39, 312 (2019)
56. M. Kumar, R.B. Pachori, U.R. Acharya, Use of accumulated entropies for automated detection
of congestive heart failure in flexible analytic wavelet transform framework based on shortterm HRV signals. Entropy 19(3), 92 (2017)
57. S. Patidar, R.B. Pachori, Classification of heart disorders based on tunable-Q wavelet transform
of cardiac sound signals, in Chaos Modeling and Control Systems Design, (Springer, Cham,
2015), pp. 239–264
58. M. Kumar, R.B. Pachori, U.R. Acharya, Automated diagnosis of atrial fibrillation ECG signals
using entropy features extracted from flexible analytic wavelet transform. Biocybern. Biomed.
Eng. 38(3), 564–573 (2018)
59. M. Kumar, R.B. Pachori, U.R. Acharya, Automated diagnosis of myocardial infarction ECG
signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9),
488 (2017)
R. B. Pachori and V. Gupta
36. G. Keiser, Biophotonics – Concepts to Applications (Springer Singapore, Singapore, 2016)
37. M. Kutz, Standard Handbook of Biomedical Engineering and Design (McGraw-Hill, New
York, 2003)
38. A. Phinyomark, P. Phukpattaranont, C. Limsakul, Feature reduction and selection for EMG
signal classification. Expert Syst. Appl. 39(8), 7420–7431 (2012)
39. T.N.S. Tengku Zawawi et al., A review of electromyography signal analysis techniques for
musculoskeletal disorders. Indones. J. Electr. Eng. Comput. Sci. 11(3), 1136 (2018)
40. B. Boashash, Time-Frequency Signal Analysis and Processing: a Comprehensive Reference
(Academic Press, Amsterdam, 2015)
41. N.E. Huang, Hilbert-Huang Transform and Its Applications, vol 16 (World Scientific, Hackensack, 2014)
42. C.L. Nikias, J.M. Mendel, Signal processing with higher-order spectra. IEEE Signal Process.
Mag. 10(3), 10–37 (1993)
43. C.L. Phillips, J.M. Parr, E.A. Riskin, Signals, Systems, and Transforms (Prentice Hall, Upper
Saddle River, 1995)
44. S. Mukhopadhyay, P. Sircar, Parametric modelling of ECG signal. Med. Biol. Eng. Comput.
34(2), 171–174 (1996)
45. R.V. Andreao, B. Dorizzi, J. Boudy, ECG signal analysis through hidden Markov models. IEEE
Trans. Biomed. Eng. 53(8), 1541–1549 (2006)
46. P.E. McSharry, G.D. Clifford, L. Tarassenko, L.A. Smith, A dynamical model for generating
synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003)
47. J.-C. Nunes, A. Nait-Ali, Hilbert transform-based ECG modeling. Biomed. Eng. 39(3), 133–
137 (2005)
48. J. Pardey, S. Roberts, L. Tarassenko, A review of parametric modelling techniques for EEG
analysis. Med. Eng. Phys. 18(1), 2–11 (1996)
49. O.A. Elsayed, A. Eldeib, F. Elhefnawi, Parametric modeling of ICTAL epilepsy EEG signal
using Prony method. Int. J. Comput. Sci. Softw. Eng. (IJCSSE) 3(1), 86–89 (2014)
50. D. Graupe, K.H. Kohn, A. Kralj, S. Basseas, Patient controlled electrical stimulation via EMG
signature discrimination for providing certain paraplegics with primitive walking functions. J.
Biomed. Eng. 5(3), 220–226 (1983)
51. M.H. Sherif, R.J. Gregor, J. Lyman, Effects of load on myoelectric signals: the ARIMA
representation. IEEE Trans. Biomed. Eng. 5, 411–416 (1981)
52. A. Iwata, N. Suzumura, K. Ikegaya, Pattern classification of the phonocardiogram using linear
prediction analysis. Med. Biol. Eng. Comput. 15(4), 407–412 (1977)
53. A. Iwata, N. Ishii, N. Suzumura, K. Ikegaya, Algorithm for detecting the first and the second
heart sounds by spectral tracking. Med. Biol. Eng. Comput. 18(1), 19–26 (1980)
54. Z. Moussavi, Fundamentals of respiratory sounds and analysis. Synth. Lect. Biomed. Eng. 1(1),
1–68 (2006)
55. R.R. Sharma, A. Kumar, R.B. Pachori, U.R. Acharya, Accurate automated detection of
congestive heart failure using eigenvalue decomposition based features extracted from HRV
signals. Biocybern. Biomed. Eng. 39, 312 (2019)
56. M. Kumar, R.B. Pachori, U.R. Acharya, Use of accumulated entropies for automated detection
of congestive heart failure in flexible analytic wavelet transform framework based on shortterm HRV signals. Entropy 19(3), 92 (2017)
57. S. Patidar, R.B. Pachori, Classification of heart disorders based on tunable-Q wavelet transform
of cardiac sound signals, in Chaos Modeling and Control Systems Design, (Springer, Cham,
2015), pp. 239–264
58. M. Kumar, R.B. Pachori, U.R. Acharya, Automated diagnosis of atrial fibrillation ECG signals
using entropy features extracted from flexible analytic wavelet transform. Biocybern. Biomed.
Eng. 38(3), 564–573 (2018)
59. M. Kumar, R.B. Pachori, U.R. Acharya, Automated diagnosis of myocardial infarction ECG
signals using sample entropy in flexible analytic wavelet transform framework. Entropy 19(9),
488 (2017)