© The Institution of Engineering and Technology
In this letter, the authors propose a new entropy measure for analysis of time series. This measure is termed as the state space correlation entropy (SSCE). The state space reconstruction is used to evaluate the embedding vectors of a time series. The SSCE is computed from the probability of the correlations of the embedding vectors. The performance of SSCE measure is evaluated using both synthetic and real valued signals. The experimental results reveal that, the proposed SSCE measure along with SVM classifier have sensitivity value of 91.60%, which is higher than the performance of both sample entropy and permutation entropy features for detection of shockable ventricular arrhythmia.
References
-
-
1)
-
2. Yusaf, M., Nawaz, R., Iqbal, J.: ‘Robust seizure detection in EEG using 2D DWT of time–frequency distributions’, Electron. Lett., 2016, 52, (11), pp. 902–903 (doi: 10.1049/el.2016.0630).
-
2)
-
16. Li, X., Ouyang, G., Richards, D.A.: ‘Predictability analysis of absence seizures with permutation entropy’, Epilepsy Res., 2007, 77, pp. 70–74 (doi: 10.1016/j.eplepsyres.2007.08.002).
-
3)
-
13. Tripathy, R.K., Sharma, L.N., Dandapat, S.: ‘A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification’, Healthc. Technol. Lett., 2014, 1, pp. 98–103 (doi: 10.1049/htl.2014.0080).
-
4)
-
2. Qiao, L., Rajagopalan, C., Clifford, G.D.: ‘Ventricular fibrillation and tachycardia classification using a machine learning approach’, IEEE Trans. Biomed. Eng., 2014, 61, (6), pp. 1607–1613 (doi: 10.1109/TBME.2013.2275000).
-
5)
-
6. Tripathy, R.K., Sharma, L.N., Dandapat, S.: ‘Detection of shockable ventricular arrhythmia using variational mode decomposition’, J. Med. Syst., 2016, 40, pp. 1–13 (doi: 10.1007/s10916-015-0365-5).
-
6)
-
7. Olofsen, E., Sleigh, J.W., Dahan, A.: ‘Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect’, Br. J. Anaesth. , 2008, 101, pp. 810–821 (doi: 10.1093/bja/aen290).
-
7)
-
20. Bandt, C., Pompe, B.: ‘Permutation entropy: a natural complexity measure for time series’, Phys. Rev. Lett., 2002, 88, p. 174102 (doi: 10.1103/PhysRevLett.88.174102).
-
8)
-
1. Rostaghi, M., Azami, H.: ‘Dispersion entropy: A measure for time-series analysis’, IEEE Signal Process. Lett., 2016, 23, pp. 610–614 (doi: 10.1109/LSP.2016.2542881).
-
9)
-
24. Moody, G.B., Mark, R.G.: ‘The impact of the MIT-BIH Arrhythmia Database’, IEEE Eng. Med. Biol., 2001, 20, (3), pp. 45–50 (doi: 10.1109/51.932724).
-
10)
-
29. Acharya, R., Joseph, P., Kannathal, N., Lim, C.M., Suri, J.S.: ‘Heart rate variability: a review’, Med. Biol. Eng. Comput., 2006, 44, (12), pp. 1031–1051 (doi: 10.1007/s11517-006-0119-0).
-
11)
-
15. Zao, L., Cavalcante, D., Coelho, R.: ‘Time-frequency feature and AMS-GMM mask for acoustic emotion classification’, IEEE Signal Process. Lett., 2014, 21, pp. 620–624 (doi: 10.1109/LSP.2014.2311435).
-
12)
-
25. Goldberger, A.L., Amaral, L.A.N., Glass, L., et al: ‘PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals’, Circulation, 2000, 101, (23), pp. e215–e220 (doi: 10.1161/01.CIR.101.23.e215).
-
13)
-
4. Li, P., Liu, C., Li, K., et al: ‘PAssessing the complexity of short-term heartbeat interval series by distribution entropy’, Med. Biol. Eng. Comput., 2015, 53, pp. 77–87 (doi: 10.1007/s11517-014-1216-0).
-
14)
-
14. Burkhardt, F., Paeschke, A., Rolfes, M., et al: ‘A database of German emotional speech’, Interspeech, 2005, 5, pp. 1517–1520.
-
15)
-
12. Clifford, G., Tarassenko, L., Townsend, N.: ‘One-pass training of optimal architecture auto-associative neural network for detecting ectopic beats’, IET Electron.Lett., 2001, 37, pp. 1126–1127 (doi: 10.1049/el:20010762).
-
16)
-
5. Andrzejak, R.G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C.E.: ‘Indications of nonlinear deterministic and finite dimensional structures in time series of brain electrical activity: dependence on recording region and brain state’, Phys. Rev. E, 2001, 64, p. 061907 (doi: 10.1103/PhysRevE.64.061907).
-
17)
-
2. Richman, J.S., Moorman, J.R.: ‘Physiological time-series analysis using approximate entropy and sample entropy’, Am.J.Physiol.-Heart Circulatory Physiol., 2000, 278, pp. 2039–2049.
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