Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform
Source abstract
Abstract
Classification of electroencephalogram (EEG) signals for humans can be achieved via artificial intelligence (AI) techniques. Especially, the EEG signals associated with seizure epilepsy can be detected to distinguish between ... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · ECG Monitoring and Analysis · Advanced Computing and Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (55)
Show source-provided references
1. Multimodal fused emotion recognition about expression-EEG interaction and collaboration using deep learning10.1109/ACCESS.2020.3010311 ↗
2. EEG-Based human emotion prediction using an LSTM model
3. The improved ELM algorithms optimized by bionic WOA for EEG classification of brain computer interface10.1109/ACCESS.2021.3076347 ↗
4. Epileptic seizure detection for multi-channel EEG with deep convolutional neural network
5. Performance comparison and current challenges of using machine learning techniques in cybersecurity10.3390/en13102509 ↗
6. A survey on machine learning techniques for cyber security in the last decade10.1109/ACCESS.2020.3041951 ↗
7. An investigation of credit card default prediction in the imbalanced datasets10.1109/ACCESS.2020.3033784 ↗
8. A review of content-based and context-based recommendation systems10.3991/ijet.v16i03.18851 ↗
9. A comparative performance analysis of data resampling methods on imbalance medical data10.1109/ACCESS.2021.3102399 ↗
10. Corporate bankruptcy prediction: An approach towards better corporate world10.1093/comjnl/bxaa056 ↗
11. Cyber threat detection using machine learning techniques: A performance evaluation perspective
12. EEG classification by factoring in sensor spatial configuration10.1109/ACCESS.2021.3054670 ↗
13. A framework on wavelet-based nonlinear features and extreme learning machine for epileptic seizure detection10.1016/j.bspc.2013.11.010 ↗
14. A survey on applications of artificial intelligence in fighting against COVID-19
15. An ultrasound standard plane detection model of fetal head based on multi-task learning and hybrid knowledge graph10.1016/j.future.2022.04.011 ↗
16. A disease diagnosis and treatment recommendation system based on big data mining and cloud computing10.1016/j.ins.2018.01.001 ↗
17. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients10.1103/PhysRevE.86.046206 ↗
18. Evaluating the performance of machine learning techniques in the classification of wisconsin breast cancer
19. MAFC: Multi-agent fog computing model for healthcare critical tasks management10.3390/s20071853 ↗
20. Decision support system for focal EEG signals using tunable-Q wavelet transform10.1016/j.jocs.2017.03.022 ↗
21. Entropy features for focal EEG and non focal EEG10.1016/j.jocs.2018.02.002 ↗
22. Classification of focal and non focal EEG using entropies10.1016/j.patrec.2017.05.007 ↗
23. An automatic detection of focal EEG signals using new class of time–frequency localized orthogonal wavelet filter banks10.1016/j.knosys.2016.11.024 ↗
24. Classification of focal and nonfocal EEG signals using features derived from Fourier-based rhythms10.1142/S0219519417400024 ↗
25. A novel approach for automated detection of focal EEG signals using empirical wavelet transform10.1007/s00521-016-2646-4 ↗
26. Classification of focal and non focal epileptic seizures using multi-features and SVM classifier
27. Epileptogenic focus detection in intracranial EEG based on delay permutation entropy10.1063/1.4824993 ↗
28. Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection10.1002/ima.22199 ↗
29. Feature extraction and selection of a combination of entropy features for real-time epilepsy detection
30. Genetic algorithm based feature selection for classification of focal and non-focal intracranial electroencephalographic signals
31. Epileptic focus localization based on bivariate empirical mode decomposition and entropy
32. Epileptic seizure classification from intracranial EEG signals: A comparative study EEG-based seizure classification
33. End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis10.1007/s10489-020-01859-1 ↗
34. Long short term memory networks for anomaly detection in time series
35. Advantage and drawback of support vector machine functionality
36. Industry 4.0-oriented deep learning models for human activity recognition10.1109/ACCESS.2021.3125733 ↗
37. Optimal portfolio choice under a liability constraint10.1023/A:1018996712442 ↗
38. Vapnik · The Nature of Statistical Learning Theory · 200010.1007/978-1-4757-3264-1 ↗
39. Wavelet-based feature extraction for support vector machines for screening balance impairments in the elderly
40. Performance analysis of support vector machine (SVM) for optimization of fuzzy based epilepsy risk level classifications using different types of kernel functions from EEG signal parameters
41. A study of cross-validation and bootstrap for accuracy estimation and model selection
42. A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks10.1016/j.engappai.2022.105461 ↗
43. An automatic prediction of epileptic seizures using cloud computing and wireless sensor networks10.1007/s10916-016-0579-1 ↗
44. Classification of EEG signals recorded during right/left hand movement imagery using fast walsh hadamard transform based features
45. A Cloud-based seizure alert system for epileptic patients that uses higher-order statistics10.1109/MCSE.2016.82 ↗
46. Focal EEG signal detection based on constant-bandwidth TQWT filter-banks
47. Time-domain exponential energy for epileptic EEG signal classification10.1016/j.neulet.2018.10.062 ↗
48. Mixture correntropy for robust learning10.1016/j.patcog.2018.02.010 ↗
49. Empirical mode decomposition based classification of focal and non-focal seizure EEG signals
50. Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals10.3390/e17020669 ↗
51. An integrated index for the identification of focal electroencephalogram signals using discrete wavelet transform and entropy measures10.3390/e17085218 ↗
52. Epileptic seizures diagnosis using amalgamated extremely focused EEG signals and brain MRI10.32604/cmc.2023.032552 ↗
53. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework10.1016/j.eswa.2012.02.040 ↗
54. Combined neural network model employing wavelet coefficients for EEG signals classification10.1016/j.dsp.2008.07.004 ↗
55. Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG