empirical mode decomposition based
filtering and Riemannian geometry.
Expert Systems with Applications. 2018;
95:201-211
[16] Martis RJ, Acharya UR, Tan JH,
Petznick A, Yanti R, Chua CK, et al.
Application of empirical mode
decomposition (EMD) for automated
detection of epilepsy using EEG signals.
International Journal of Neural Systems.
2012;22(06):1250027
[17] Zhu G, Li Y, Wen PP, Wang S, Xi M.
Epileptogenic focus detection in
intracranial EEG based on delay
permutation entropy. In: AIP
Conference Proceedings; AIP; Vol. 1559,
No. 1; 2013. pp. 31–36
[18] Sharma R, Pachori RB, Gautam S.
Empirical mode decomposition based
classification of focal and non-focal EEG
signals. In: 2014 International
Conference on Medical Biometrics;
IEEE; 2014. pp. 135–140
[19] Sharma R, Pachori R, Acharya U. An
integrated index for the identification of
focal electroencephalogram signals
using discrete wavelet transform and
entropy measures. Entropy. 2015;17(8):
5218-5240
[20] Chen W, Wang Z, Xie H, Yu W.
Characterization of surface EMG signal
based on fuzzy entropy. IEEE
Transactions on Neural Systems and
Rehabilitation Engineering. 2007;15(2):
266-272
[21] Lv Z, Wu XP, Li M, Zhang D. A
novel eye movement detection
algorithm for EOG driven human
computer interface. Pattern Recognition
Letters. 2010;31(9):1041-1047
[22] Li T, Zhou M. ECG classification
using wavelet packet entropy and
random forests. Entropy. 2016;18(8):285
[23] Pincus SM. Approximate entropy as
a measure of system complexity.
Proceedings of the National Academy of
Sciences. 1991;88(6):2297-2301
[24] Holzinger A, Stocker C, Bruschi M,
Auinger A, Silva H, Gamboa H, Fred A.
On applying approximate entropy to
ECG signals for knowledge discovery on
the example of big sensor data. In:
International Conference on Active
Media Technology; Springer: Berlin,
Heidelberg; 2012. pp. 646–657
[25] Ahmad SA, Chappell PH. Surface
EMG classification using moving
approximate entropy. In: 2007
International Conference on Intelligent
and Advanced Systems; IEEE. 2007.
pp. 1163–1167
[26] Fasil OK, Rajesh R. Time-domain
exponential energy for epileptic EEG
signal classification. Neuroscience
Letters. 2019;694:1-8
[27] Richman JS, Moorman JR.
Physiological time-series analysis using
approximate entropy and sample
entropy. American Journal of
Physiology. Heart and Circulatory
Physiology. 2000;278(6):H2039-H2049
[28] Jie X, Cao R, Li L. Emotion
recognition based on the sample entropy
of EEG. Bio-medical Materials and
Engineering. 2014;24(1):1185-1192
[29] Absolo D, Hornero R, Espino P,
Alvarez D, Poza J. Entropy analysis of the
EEG background activity in Alzheimer’s
disease patients. Physiological
Measurement. 2006;27(3):241
[30] Cao C, Slobounov S. Application of
a novel measure of EEG non-stationarity
as Shannon-entropy of the peak
frequency shifting for detecting residual
abnormalities in concussed individuals.
Clinical Neurophysiology. 2011;122(7):
1314-1321
[31] Yulmetyev RM, Emelyanova NA,
Gafarov FM. Dynamical Shannon
entropy and information Tsallis entropy
76
Advances in Neural Signal Processing
filtering and Riemannian geometry.
Expert Systems with Applications. 2018;
95:201-211
[16] Martis RJ, Acharya UR, Tan JH,
Petznick A, Yanti R, Chua CK, et al.
Application of empirical mode
decomposition (EMD) for automated
detection of epilepsy using EEG signals.
International Journal of Neural Systems.
2012;22(06):1250027
[17] Zhu G, Li Y, Wen PP, Wang S, Xi M.
Epileptogenic focus detection in
intracranial EEG based on delay
permutation entropy. In: AIP
Conference Proceedings; AIP; Vol. 1559,
No. 1; 2013. pp. 31–36
[18] Sharma R, Pachori RB, Gautam S.
Empirical mode decomposition based
classification of focal and non-focal EEG
signals. In: 2014 International
Conference on Medical Biometrics;
IEEE; 2014. pp. 135–140
[19] Sharma R, Pachori R, Acharya U. An
integrated index for the identification of
focal electroencephalogram signals
using discrete wavelet transform and
entropy measures. Entropy. 2015;17(8):
5218-5240
[20] Chen W, Wang Z, Xie H, Yu W.
Characterization of surface EMG signal
based on fuzzy entropy. IEEE
Transactions on Neural Systems and
Rehabilitation Engineering. 2007;15(2):
266-272
[21] Lv Z, Wu XP, Li M, Zhang D. A
novel eye movement detection
algorithm for EOG driven human
computer interface. Pattern Recognition
Letters. 2010;31(9):1041-1047
[22] Li T, Zhou M. ECG classification
using wavelet packet entropy and
random forests. Entropy. 2016;18(8):285
[23] Pincus SM. Approximate entropy as
a measure of system complexity.
Proceedings of the National Academy of
Sciences. 1991;88(6):2297-2301
[24] Holzinger A, Stocker C, Bruschi M,
Auinger A, Silva H, Gamboa H, Fred A.
On applying approximate entropy to
ECG signals for knowledge discovery on
the example of big sensor data. In:
International Conference on Active
Media Technology; Springer: Berlin,
Heidelberg; 2012. pp. 646–657
[25] Ahmad SA, Chappell PH. Surface
EMG classification using moving
approximate entropy. In: 2007
International Conference on Intelligent
and Advanced Systems; IEEE. 2007.
pp. 1163–1167
[26] Fasil OK, Rajesh R. Time-domain
exponential energy for epileptic EEG
signal classification. Neuroscience
Letters. 2019;694:1-8
[27] Richman JS, Moorman JR.
Physiological time-series analysis using
approximate entropy and sample
entropy. American Journal of
Physiology. Heart and Circulatory
Physiology. 2000;278(6):H2039-H2049
[28] Jie X, Cao R, Li L. Emotion
recognition based on the sample entropy
of EEG. Bio-medical Materials and
Engineering. 2014;24(1):1185-1192
[29] Absolo D, Hornero R, Espino P,
Alvarez D, Poza J. Entropy analysis of the
EEG background activity in Alzheimer’s
disease patients. Physiological
Measurement. 2006;27(3):241
[30] Cao C, Slobounov S. Application of
a novel measure of EEG non-stationarity
as Shannon-entropy of the peak
frequency shifting for detecting residual
abnormalities in concussed individuals.
Clinical Neurophysiology. 2011;122(7):
1314-1321
[31] Yulmetyev RM, Emelyanova NA,
Gafarov FM. Dynamical Shannon
entropy and information Tsallis entropy
76
Advances in Neural Signal Processing