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EEG Analysis with Wavelet Transform under Music Perception Stimulation. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:9725762. [PMID: 34956582 PMCID: PMC8694970 DOI: 10.1155/2021/9725762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 10/29/2021] [Accepted: 11/08/2021] [Indexed: 11/17/2022]
Abstract
In order to improve the classification accuracy and reliability of emotional state assessment and provide support and help for music therapy, this paper proposes an EEG analysis method based on wavelet transform under the stimulation of music perception. Using the data from the multichannel standard emotion database (DEAP), α, ß, and θ rhythms are extracted in frontal (F3 and F4), temporal (T7 and T8), and central (C3 and C4) channels with wavelet transform. EMD is performed on the extracted EEG rhythm to obtain intrinsic mode function (IMF) components, and then, the average energy and amplitude difference eigenvalues of IMF components of EEG rhythm waves are further extracted, that is, each rhythm wave contains three average energy characteristics and two amplitude difference eigenvalues so as to fully extract EEG feature information. Finally, emotional state evaluation is realized based on a support vector machine classifier. The results show that the correct rate between no emotion, positive emotion, and negative emotion can reach more than 90%. Among the pairwise classification problems among the four emotions selected, the classification accuracy obtained by this EEG feature extraction method is higher than that obtained by general feature extraction methods, which can reach about 70%. Changes in EEG α wave power were closely correlated with the polarity and intensity of emotion; α wave power varied significantly between "happiness and fear," "pleasure and fear," and "fear and sadness." It has a good application prospect in both psychological and physiological research of emotional perception and practical application.
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Wei L, Boutouil H, R Gerbatin R, Mamad O, Heiland M, Reschke CR, Del Gallo F, F Fabene P, Henshall DC, Lowery M, Morris G, Mooney C. Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy. J Neural Eng 2021; 18. [PMID: 34607322 DOI: 10.1088/1741-2552/ac2ca0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Objective.Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals.Approach.In this study, a machine learning-based seizure detection approach, 'Epi-AI', which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach.Main results.Epi-AI demonstrated a superior sensitivity, 91.4%-98.8%, and specificity, 93.1%-98.8%, in Models I-III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I-III ranged from 66.9%-91.3% sensitivity and 60.8%-97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I-III at 73.2%-80.1% and 75.8%-98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity.Significance.Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.
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Affiliation(s)
- Lan Wei
- School of Computer Science, University College Dublin, Dublin, Ireland.,FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Halima Boutouil
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Rogério R Gerbatin
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Omar Mamad
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Mona Heiland
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Cristina R Reschke
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,School of Pharmacy and Biomolecular Sciences, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Federico Del Gallo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy.,School of Pharmacy, University of Camerino, Macerata, Italy
| | - Paolo F Fabene
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - David C Henshall
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland
| | - Madeleine Lowery
- School of Electrical & Electronic Engineering, University College Dublin, Dublin, Ireland
| | - Gareth Morris
- FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,Department of Physiology & Medical Physics, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,These authors contributed equally
| | - Catherine Mooney
- School of Computer Science, University College Dublin, Dublin, Ireland.,FutureNeuro SFI Research Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland.,These authors contributed equally
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J. P, Subathra MSP, Mohammed MA, Maashi MS, Garcia-Zapirain B, Sairamya NJ, George ST. Detection of Focal and Non-Focal Electroencephalogram Signals Using Fast Walsh-Hadamard Transform and Artificial Neural Network. SENSORS 2020; 20:s20174952. [PMID: 32883006 PMCID: PMC7506968 DOI: 10.3390/s20174952] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 08/17/2020] [Accepted: 08/20/2020] [Indexed: 11/16/2022]
Abstract
The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.
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Affiliation(s)
- Prasanna J.
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India; (P.J.); (N.J.S.)
| | - M. S. P. Subathra
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India;
| | - Mazin Abed Mohammed
- College of Computer Science and Information Technology, University of Anbar, 11, Ramadi, Anbar, Iraq;
| | - Mashael S. Maashi
- Software Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
| | | | - N. J. Sairamya
- Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India; (P.J.); (N.J.S.)
| | - S. Thomas George
- Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Tamil Nadu 641114, India
- Correspondence:
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