Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals
Abstract
:1. Introduction
2. Methodology
2.1. Dataset
2.2. Empirical Mode Decomposition
- In the whole dataset, the number of extrema and the number of zero-crossings should be either equal or differ at most by one.
- The mean value of two envelopes, one defined by connecting local maxima and the other defined by connecting local minima, at any point is zero.
- Extract extrema (maxima and minima) from signal x(t).
- Obtain the envelope εmax(t) by connecting all of the maxima and similarly obtain the envelope εmin(t) by connecting all of the minima using cubic spline interpolation.
- Compute the average of εmax(t) and εmin(t) as:
- Extract D(t) from x(t) as:
- Check whether the D(t) satisfies the two basic conditions of IMF.
- Repeat Steps i–v, for D(t), until it satisfies the conditions of IMF.
2.3. Entropy-Based Features Extraction
2.3.1. Average Spectral Entropies
2.3.2. Average Approximate Entropy
2.3.3. Average Sample Entropy
2.3.4. Average Phase Entropies
2.3.5. Least Squares Support Vector Machine
2.3.6. Performance Evaluation
3. Results
4. Discussion
- We have obtained a higher classification accuracy compared to previously reported works using the same database.
- Our method is rigorous and repetitive, as we have performed a ten-fold cross-validation.
- The developed software can be used for automated identification of focal and non-focal EEG signals.
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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IMF | Features | Focal EEG Signals | Non-Focal EEG Signals |
---|---|---|---|
IMF1 | ShEnAvg | 6.2540 ± 0.1708 | 6.2806 ± 0.2899 |
RenEnAvg | 5.7793 ± 0.2306 | 5.7941 ± 0.4683 | |
ApEnAvg | 0.9899 ± 0.2088 | 1.1289 ± 0.1050 | |
SpEnAvg | 2.6911 ± 0.2758 | 2.9814 ± 0.4337 | |
S1Avg | 0.8243 ± 0.0400 | 0.8397 ± 0.0294 | |
S2Avg | 0.2650 ± 0.0529 | 0.2535 ± 0.0596 | |
IMF2 | ShEnAvg | 5.5284 ± 0.1673 | 5.4603 ± 0.2285 |
RenEnAvg | 5.0784 ± 0.2054 | 4.9755 ± 0.2708 | |
ApEnAvg | 0.6265 ± 0.0901 | 0.6366 ± 0.0931 | |
SpEnAvg | 2.5932 ± 0.2947 | 2.8691 ± 0.3757 | |
S1Avg | 0.6934 ± 0.0498 | 0.7059 ± 0.0656 | |
S2Avg | 0.0406 ± 0.0270 | 0.0362 ± 0.0239 | |
IMF3 | ShEnAvg | 4.7766 ± 0.2099 | 4.6954 ± 0.1942 |
RenEnAvg | 4.3318 ± 0.2423 | 4.2373 ± 0.2163 | |
ApEnAvg | 0.5322 ± 0.0855 | 0.5495 ± 0.0819 | |
SpEnAvg | 2.3958 ± 0.3202 | 2.7408 ± 0.3527 | |
S1Avg | 0.6422 ± 0.0506 | 0.6251 ± 0.0657 | |
S2Avg | 0.3390 ± 0.0516 | 0.3349 ± 0.0577 | |
IMF4 | ShEnAvg | 4.0436 ± 0.1791 | 4.0154 ± 0.1629 |
RenEnAvg | 3.606 ± 0.2053 | 3.5705 ± 0.2169 | |
ApEnAvg | 0.3337 ± 0.0632 | 0.3543 ± 0.0629 | |
SpEnAvg | 2.2111 ± 0.3325 | 2.2928 ± 0.3279 | |
S1Avg | 0.4983 ± 0.0591 | 0.4758 ± 0.0706 | |
S2Avg | 0.0473 ± 0.0196 | 0.0434 ± 0.0169 | |
IMF5 | ShEnAvg | 3.3572 ± 0.2202 | 3.3676 ± 0.1906 |
RenEnAvg | 2.9394 ± 0.2728 | 2.958 ± 0.2293 | |
ApEnAvg | 0.1546 ± 0.0388 | 0.1564 ± 0.0279 | |
SpEnAvg | 1.8450 ± 0.3640 | 1.5742 ± 0.3653 | |
S1Avg | 0.4762 ± 0.0473 | 0.4639 ± 0.0564 | |
S2Avg | 0.3998 ± 0.0515 | 0.4259 ± 0.0290 | |
IMF6 | ShEnAvg | 2.6842 ± 0.2384 | 2.6886 ± 0.1775 |
RenEnAvg | 2.2904 ± 0.2502 | 2.312 ± 0.2072 | |
ApEnAvg | 0.0711 ± 0.0200 | 0.0648 ± 0.0111 | |
SpEnAvg | 1.2377 ± 0.3969 | 0.9484 ± 0.3337 | |
S1Avg | 0.3337 ± 0.0514 | 0.3174 ± 0.0622 | |
S2Avg | 0.0656 ± 0.0159 | 0.0740 ± 0.0176 | |
IMF7 | ShEnAvg | 2.0076 ± 0.3008 | 2.018 ± 0.2033 |
RenEnAvg | 1.64 ± 0.3107 | 1.6385 ± 0.2201 | |
ApEnAvg | 0.0361 ± 0.0101 | 0.0333 ± 0.0051 | |
SpEnAvg | 0.6229 ± 0.3246 | 0.4393 ± 0.2127 | |
S1Avg | 0.3401 ± 0.0426 | 0.3413 ± 0.0405 | |
S2Avg | 0.4432 ± 0.0147 | 0.4424 ± 0.0046 | |
IMF8 | ShEnAvg | 1.4991 ± 0.279 | 1.4757 ± 0.2509 |
RenEnAvg | 1.144 ± 0.2705 | 1.1327 ± 0.2564 | |
ApEnAvg | 0.0190 ± 0.0056 | 0.0176 ± 0.0034 | |
SpEnAvg | 0.1837 ± 0.2630 | 0.1120 ± 0.0836 | |
S1Avg | 0.1869 ± 0.0507 | 0.1953 ± 0.0435 | |
S2Avg | 0.0803 ± 0.0097 | 0.0788 ± 0.0022 | |
IMF9 | ShEnAvg | 1.1696 ± 0.2795 | 1.0887 ± 0.2935 |
RenEnAvg | 0.7892 ± 0.2541 | 0.7416 ± 0.2378 | |
ApEnAvg | 0.0091 ± 0.0037 | 0.0087 ± 0.0023 | |
SpEnAvg | 0.0497 ± 0.1537 | 0.0217 ± 0.0173 | |
S1Avg | 0.2525 ± 0.0502 | 0.2587 ± 0.0373 | |
S2Avg | 0.4468 ± 0.0057 | 0.4473 ± 0.0015 | |
IMF10 | ShEnAvg | 0.7005 ± 0.3021 | 0.8159 ± 0.3198 |
RenEnAvg | 0.396 ± 0.2094 | 0.4755 ± 0.2346 | |
ApEnAvg | 0.0039 ± 0.0022 | 0.0039 ± 0.0014 | |
SpEnAvg | 0.0108 ± 0.0334 | 0.0055 ± 0.0046 | |
S1Avg | 0.0900 ± 0.0437 | 0.0893 ± 0.0316 | |
S2Avg | 0.0811 ± 0.0028 | 0.0813 ± 0.0008 |
Feature | IMF | p-Value |
---|---|---|
ShEnAvg | IMF3 | 4.52 × 10−2 |
RenEnAvg | IMF2 | 3.47 × 10−2 |
IMF3 | 4.23 × 10−2 | |
ApEnAvg | IMF1 | 5.79 × 10−5 |
SpEnAvg | IMF1 | 1.26 × 10−4 |
IMF2 | 8.98 × 10−5 | |
IMF3 | 1.52 × 10−6 | |
IMF5 | 3.40 × 10−4 | |
IMF6 | 1.50 × 10−4 | |
IMF7 | 1.17 × 10−3 | |
S1Avg | IMF1 | 3.08 × 10−2 |
S2Avg | IMF5 | 2.43 × 10−3 |
IMF6 | 1.35 × 10−2 |
Kernel | Kernel Parameter | ACC | SEN | SPF | PPV | NPV | MCC |
---|---|---|---|---|---|---|---|
RBF | σ = 18.2 | 86.00 | 88.00 | 84.00 | 86.81 | 88.00 | 0.73 |
Mexican hat wavelet | a = 70.6 | 84.00 | 86.00 | 82.00 | 86.67 | 87.56 | 0.71 |
Morlet wavelet | a = 24.6 | 87.00 | 90.00 | 84.00 | 87.29 | 90.50 | 0.76 |
Method | Classification Accuracy (%) |
---|---|
DPE and SVM [65] | 84 |
SpEnAvg, AVIF and LS-SVM [66] | 85 |
Proposed method | 87 |
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Sharma, R.; Pachori, R.B.; Acharya, U.R. Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals. Entropy 2015, 17, 669-691. https://doi.org/10.3390/e17020669
Sharma R, Pachori RB, Acharya UR. Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals. Entropy. 2015; 17(2):669-691. https://doi.org/10.3390/e17020669
Chicago/Turabian StyleSharma, Rajeev, Ram Bilas Pachori, and U. Rajendra Acharya. 2015. "Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals" Entropy 17, no. 2: 669-691. https://doi.org/10.3390/e17020669
APA StyleSharma, R., Pachori, R. B., & Acharya, U. R. (2015). Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals. Entropy, 17(2), 669-691. https://doi.org/10.3390/e17020669