We designed this study to investigate the usability of time and frequency measures—signal energy, spectral energy, spectral entropy—and to compare the performances of five different classifiers with the goal of developing a patient-independent seizure detection system. To achieve this objective, we gathered relatively uniform scalp-recorded EEG data from patients with well-characterized TLE and applied time- and frequency-domain measures to detect the electrical onset of seizures. Although most studies have included temporal and extratemporal focal seizures to achieve generality, we restricted our investigation to temporal lobe seizures.7,17,43,45–47 Compared with scalp-recorded EEG, intracranial EEG recordings are largely free of artifacts. However, as intracranial EEG is invasive and expensive, it is undertaken on only in a minority of selected patients with antiepileptic drug-resistant epilepsy, in whom scalp-recorded EEG data have failed to provide localization of the seizure onset. Because of these reasons, studies that have used intracranial EEG are likely to give different results from ours.36,40,44,48
An SVM-based algorithm applied to pediatric patients with a variety of seizure types detected the seizures within 8 ± 3.2 seconds of electrical onset with 94% sensitivity and a false alarm rate of 0.25 hours.31 In another study, recurrent neural network–based system trained using spectral, wavelet, statistical, and complexity measures identified seizures with mean preonset and onset detection latency of −51 seconds (range: −1,140 to −61 seconds) and +4 second (−12 to +51 seconds), respectively.32 A clinical seizure onset detection and warning system with tunable threshold mechanism showed a sensitivity of 76% with median detection latency of 10 seconds.13 An SVM-based detection system using intracranial EEG identified the electrical onset of seizures with median/mean latency (5 seconds/6.9 seconds) and with 97% sensitivity.15 A patient-specific seizure detection algorithm using Combined Seizure Index of wavelet coefficients extracted from multichannel scalp EEG identified 90.5% of the 63 seizures with median detection latency of 7 seconds.46 However, most of the systems proposed in previous studies were patient-dependent and required expert input for channel selection and training data to reconfigure the system for another patient. A generic SVM system using 6 ictal morphologies identified 91 seizures of 57 patients with a median delay of +1.6 seconds (range: −4 to +10 seconds) detection latency and >96% sensitivity. In the system proposed by Meyer et al.,43 each sample of the feature vector was mapped to a probability by comparing the 5 second history with a 25 second baseline history to obtain generalizability across patients.
We aimed to develop a self-sustaining system and therefore normalized each segment using the maximum and minimum values computed from all 29 seizure recordings. The proposed algorithm makes the EEG comparable across patients and does not need any change to use the system for another patient. The features computed for each windowed signal on N − 1 patient recordings were used to train the classifier, and the remaining recording was used to validate the classifier. Each classifier identified the electrical onset of seizure at a different latency compared with manual detection. Comparing the detection latency and performance of the classifiers, the SVM algorithm performed better by detecting the electrical onset well before the clinical onset, with sensitivity and specificity of 80% and 86%, respectively. In 25/29 seizures, the electrical onset was detected by the SVM classifier with median/mean latency of 2 seconds/5.8 seconds (range: −4 to +32 seconds), whereas two seizures are identified at the clinical onset, and one seizure was identified 2 seconds after the clinical onset. The LDA and NB classifiers performed with very low false-positive rates but a high missed seizure rate. The DT and KNN algorithms had good sensitivity but a comparatively low specificity.
We found that for all the 29 seizure recordings, the profile of wavelet energy correlated with spectral energy in 1 to 3, 3 to 6, 6 to 12, 12 to 25, 25 to 50, 50 to 100, and 100 to 200 Hz bands. In a previous study, we observed that the spectral energy in 1 to 3, 3 to 6, 6 to 12, and 12 to 25 Hz increased at the electrical onset of seizure.51 Hence, we combined the lower frequency bands, and the entire signal was divided into 1 to 25, 25 to 100, and 100 to 200 Hz bands for the measurement of spectral energy. The selection of these frequency bands made the analysis and comparisons simple and efficient due to the resulting reduced feature set, yielding better distinguishability of normal and seizure patterns. The spectral entropy in the 3 to 12 Hz band used for seizure electrical onset detection was not analyzed in previous studies with scalp EEG recordings. The spectral entropy in the 1 to 200 Hz frequency band has not shown large changes at the electrical onset of seizures. The largest decrease in entropy was obtained in the 3 to 12 Hz frequency band, and therefore, this was chosen to measure the spectral entropy.
We acknowledge the following limitations of our study. Among our 29 seizure recordings, we could identify the electrical onset in 26 seizures by the classifiers. Among the rest, for two seizures, the clinical onset was detected by all the five classifiers and one seizure was identified by none of the classifiers. Because the algorithm was trained with 29 EEG samples recorded in a routine clinical environment with the presence of movement and myogenic artifacts, the classifiers produced relatively low specificity. Among the 29 seizures, 4 seizures (P4_s1, P13_s1, P19_s1, and P20_s2) were identified with low sensitivity. In all the four seizures, the spectral entropy showed significant variations at the electrical onset of seizure. The significant changes in the profile of signal energy were observed at the clinical onset of P13_s1 and P20_s2 seizures. The seizures P4_s1, P13_s1, and P19_s1 showed no significant variations in the spectral energy in 1 to 25 Hz bands. Because of the lack of significant variations in E lowband and signal energy measures during the seizure activity, the detection of these four seizures resulted in more false normal. Two seizures P15_s1 and P21_s1 were identified with low specificity. Having a good profile of E lowband and signal energy measures, the decrease in the spectral entropy values of normal EEG leads to the misclassification of normal as seizure condition. In particular, the spectral entropy pattern of the normal EEG of P15_s1 overlapped with the seizure region, the seizure detection was resulted in false alarm. However, we admit that the performance of classification was poor in the above-stated scenarios, which emphasizes the need for further research focused to address these issues.
Among the five classifiers, the SVM was found most suitable for the design of an automated system for clinical study. Also, we observed that the performance of the system mainly depended on three factors: (1) normalization scheme, (2) feature extraction, and (3) machine learning algorithm. If the above three parameters are properly selected, the design of a patient-independent system is feasible, and it can be used in a clinical environment. An automated seizure detection system would be helpful for timing ictal SPECT studies as well as for warning patients about upcoming clinical seizures. Such an automated seizure detection system would assist neurologists in visual analysis of long-term EEG recordings and considerably reduce the time spend for reviewing the data.
We investigated the usability of time and frequency measures to improve the performance of a patient-independent system for detection of the electrical onset of seizures in patients with TLE. We selected signal energy, spectral energy in the 1 to 25 Hz band, and spectral entropy in the 3 to 12 Hz band as useful features and trained five classifiers using them. An SVM-based system distinguished normal and seizure patterns with sensitivity and specificity of 80% and 86%, respectively. If the system is trained with data from a larger number of patients, the resulting patient-independent system might be of value in the presurgical evaluation of patients with drug-resistant TLE.
1. Blair RDG. Temporal lobe epilepsy
Res Treat 2012;2012:1–10.
2. Radhakrishnan K. Challenges in the management of epilepsy
in resource-poor countries. Nat Rev Neurol 2009;5:323–330.
3. Engel J Jr. Mesial temporal lobe epilepsy
: what have we learned? Neuroscientist 2001;7:340–352.
4. Carney PR, Myers S, Geyer JD. Seizure prediction: methods. Epilepsy
5. Winterhalder M, Maiwald T, Voss HU, Aschenbrenner-Scheibe R, Timmer J, Schulze-Bonhage A. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy
6. Maiwald T, Winterhalder M, Aschenbrenner-Scheibe R, Voss HU, Schulze-Bonhage A, Timmer J. Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic. Physica D 2004;194:357–368.
7. Bandarabadi M, Teixeira CA, Rasekhi J, Dourado A. Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol 2015;126:237–248.
8. Liu HS, Zhang T, Yang FS. A multistage, multimethod approach for automatic detection and classification of epileptiform EEG
. IEEE Trans Biomed Eng 2002;49:1557–1566.
9. Putten MJ, Kind T, Visser F, Lagerburg V. Detecting temporal lobe seizures from scalp EEG
recordings: a comparison of various features. Clin Neurophysiol 2005;116:2480–2489.
10. Shiau D-S, Halford JJ, Kelly KM. Singularity-based automated seizure detection system for scalp EEG
monitoring. Cybern Syst Anal 2010;46:922–935.
11. Gotman J. Automatic detection of seizures and spikes. J Clin Neurophysiol 1999;16:130–140.
12. Indiradevi KP, Elias E, Sathidevi PS, Dinesh Nayak S, Radhakrishnan K. A multilevel wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. Comput Biol Med 2008;38:805–816.
13. Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG
. Clin Neurophysiol 2005;116:427–442.
14. Quintero-Rinc Pereyra M, D'Giano C, Batatia H, Risk M. A new algorithm for epilepsy
seizure onset detection and spread estimation from EEG
signals. J Phys Conf Ser 2016;705:012032.
15. Kharbouch A, Shoeb A, Guttag J, Cash SS. An algorithm for seizure onset detection using intracranial EEG
16. Putten MJ. The revised brain symmetry index. Clin Neurophysiol 2007;118:2362–2367.
17. Temko A, Thomas E, Marnane W, Lightbody G, Boylan G. EEG
-based neonatal seizure detection with support vector machines. Clin Neurophysiol 2011;122:464–473.
18. Logesparan L, Casson AJ, Imtiaz SA, Rodriguez-Villegas E. Discriminating between best performing features for seizure detection and data selection. The 35th IEEE Annual International Conference on Engineering in Medicine and Biology Society. Osaka, Japan, July 2013;1692–1695.
19. Logesparan L, Casson AJ, Rodriguez-Villegas E. Optimal features for online seizure detection. Med Biol Eng Comput 2012;50:659–669.
20. Kannathal N, Choo ML, Acharya UR, Sadasivan PK. Entropies for detection of epilepsy
. Comput Methods Prog Biomed 2005;80:187–194.
21. Song Y, Crowcroft J, Zhang J. Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. J Neurosci Methods 2012;210:132–146.
22. Wang L, Xue W, Li Y, et al. Automatic epileptic seizure detection in EEG
signals using multi-domain feature extraction and nonlinear analysis. Entropy 2017;19:1–17.
23. Acharya UR, Molinari F, Vinitha SS, Chattopadhyay S, Kwan-Hoong N, Suri JS. Automated diagnosis of epileptic EEG
using entropies. Biomed Signal Process Control 2012;7:401–408.
24. Khamis H, Mohamed A, Simpson S. Seizure state detection of temporal lobe seizures by autoregressive spectral analysis of scalp EEG
. Clin Neurophysiol 2009;120:1479–1488.
25. Iscan Z, Zmray D, Tamer D. Classification of electroencephalogram signals with combined time and frequency features. Expert Syst Appl 2011;38:10499–10505.
26. Kumar SP, Sriram N, Benakop PG, Jinaga BC. Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl 2010;37:3284–3291.
27. Sitt JD, King JR, El Karoui I, et al. Large scale screening of neural signatures of consciousness in patients in a vegetative or minimally conscious state. Brain 2014;137:2258–2270.
28. Shoeb A, Guttag J. Application of machine learning to epileptic seizure detection. Proceedings of the 27th International Conference on Machine Learning. Haifa, Israel, June 2010;975–982.
29. Kafashan M, Ryu S, Hargis MJ, et al. EEG
dynamical correlates of focal and diffuse causes of coma. BMC Neurol 2017;17:197.
30. Ramgopal S, Thome-Souza S, Jackson M, et al. Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
31. Shoeb A, Edwards H, Connolly J, Bourgeois B, Treves ST, Guttag J. Patient-specific seizure onset detection. Epilepsy
32. Minasyan GR, Chatten JB, Chatten MJ, Harner RN. Patient-specific early seizure detection from scalp EEG
. J Clin Neurophysiol 2010;27:163–178.
33. Chua EC, Patel K, Fitzsimons M, Bleakley CJ. Improved patient specific seizure detection during the pre- surgical evaluation. Clin Neurophysiol 2011;122:672–679.
34. McSharry PE, Smith LA, Tarassenko L. Comparison of predictability of epileptic seizures by a linear and a nonlinear method. IEEE Trans Biomed Eng 2003;50:628–633.
35. Mormann F, Andrzejak RG, Elger CE, Lehnertz K. Seizure prediction: the long and winding road. Brain 2007;130:314–333.
36. Aarabi A, Fazel-Rezai R, Aghakhani Y. A fuzzy rule-based system for epileptic seizure detection in intracranial EEG
. Clin Neurophysiol 2009;120:1648–1657.
37. Geng D, Zhou W, Zhang Y, Geng S. Epileptic seizure detection based on improved wavelet neural networks in long-term intracranial EEG
. Biocyber Biomed Eng 2016;36:375–384.
38. Le Van Quyen M, Martinerie J, Navarro V, et al. Anticipation of epileptic seizures from standard EEG
record-ings. Lancet 2001;357:183–188.
39. Orosco L, Correa AG, Diez P, Laciar E. Patient non-specific algorithm for seizures detection in scalp EEG
. Comput Biol Med 2016;71:128–134.
40. Mormann F, Kreuz T, Rieke C, et al. On the predictability of epileptic seizures. Clin Neurophysiol 2005;116:569–587.
41. Rasekhi J, Mollaei MR, Bandarabadi M, Teixeira CA, Dourado A. Epileptic seizure prediction based on ratio and differential linear univariate features. J Med Signals Sens 2015;5:1–11.
42. Moghim N, Corne DW. Predicting epileptic seizures in advance. PLoS One 2014;9:e99334.
43. Meier R, Dittrich H, Schulze-Bonhage A, Aertsen A. Detecting epileptic seizures in long-term human EEG
: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns. Clin Neurophysiol 2008;25:119–131.
44. Blanco S, Garay A, Coulombie D. Comparison of frequency bands using spectral entropy
for epileptic seizure prediction. ISRN Neurol 2013;2013:1–5.
45. Aschenbrenner-Scheibe R, Maiwald T, Winterhalder M, Voss HU, Timmer J, Schulze-Bonhage A. How well can epileptic seizures be predicted? An evaluation of nonlinear method. Brain 2003;126:2616–2626.
46. Zandi AS, Javidan M, Dumont GA, Tafreshi R. Automated real-time epileptic seizure detection in scalp EEG
recordings using an algorithm based on wavelet packet transform. IEEE Trans Biomed Eng 2010;57:1639–1651.
47. Rasekhi J, Mollaei MR, Bandarabadi M, Teixeira CA, Dourado A. Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods. J Neurosci Methods 2013;217:9–16.
48. Esteller R, Echauz J, D'Alessandro M, Worrell G. Continuous energy variation during the seizure cycle: towards an on-line accumulated energy. Clin Neurophysiol 2005;3:517–526.
49. Litt B, Esteller R, Echauz J, et al. Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 2001;30:51–64.
50. Park Y, Luo L, Parhi KK, Netoff T. Seizure prediction with spectral power of EEG
using cost-sensitive support vector machines. Epilepsia 2011;52:1761–1770.
51. Sridevi V, RamasubbaReddy M, Srinivasan K, Radhakrishnan K, Rathore C, Dinesh Nayak S. Study of significance of spectral and wavelet energy measures to detect the electrical onset of seizures. Proceedings of the IEEE International Conference on Inventive Computing and Informatics, India, November 2017;660–663.
52. Malmivuo J, Plonsey R. Bioelectromagnetism. New York: Oxford University Press, 1995.