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Sánchez-Reolid R, López de la Rosa F, Sánchez-Reolid D, López MT, Fernández-Caballero A. Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228886. [PMID: 36433482 PMCID: PMC9695360 DOI: 10.3390/s22228886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 05/14/2023]
Abstract
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
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Affiliation(s)
- Roberto Sánchez-Reolid
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | | | - Daniel Sánchez-Reolid
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - María T. López
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
- CIBERSAM-ISCIII (Biomedical Research Networking Center in Mental Health, Instituto de Salud Carlos III), 28016 Madrid, Spain
- Correspondence:
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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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Raghu S, Sriraam N, Gommer ED, Hilkman DMW, Temel Y, Rao SV, Hegde AS, Kubben PL. Cross-database evaluation of EEG based epileptic seizures detection driven by adaptive median feature baseline correction. Clin Neurophysiol 2020; 131:1567-1578. [PMID: 32417698 DOI: 10.1016/j.clinph.2020.03.033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 03/04/2020] [Accepted: 03/12/2020] [Indexed: 01/15/2023]
Abstract
OBJECTIVE In long-term electroencephalogram (EEG) signals, automated classification of epileptic seizures is desirable in diagnosing epilepsy patients, as it otherwise depends on visual inspection. To the best of the author's knowledge, existing studies have validated their algorithms using cross-validation on the same database and less number of attempts have been made to extend their work on other databases to test the generalization capability of the developed algorithms. In this study, we present the algorithm for cross-database evaluation for classification of epileptic seizures using five EEG databases collected from different centers. The cross-database framework helps when sufficient epileptic seizures EEG data are not available to build automated seizure detection model. METHODS Two features, namely successive decomposition index and matrix determinant were extracted at a segmentation length of 4 s (50% overlap). Then, adaptive median feature baseline correction (AM-FBC) was applied to overcome the inter-patient and inter-database variation in the feature distribution. The classification was performed using a support vector machine classifier with leave-one-database-out cross-validation. Different classification scenarios were considered using AM-FBC, smoothing of the train and test data, and post-processing of the classifier output. RESULTS Simulation results revealed the highest area under the curve-sensitivity-specificity-false detections (per hour) of 1-1-1-0.15, 0.89-0.99-0.82-2.5, 0.99-0.73-1-1, 0.95-0.97-0.85-1.7, 0.99-0.99-0.92-1.1 using the Ramaiah Medical College and Hospitals, Children's Hospital Boston-Massachusetts Institute of Technology, Temple University Hospital, Maastricht University Medical Centre, and University of Bonn databases respectively. CONCLUSIONS We observe that the AM-FBC plays a significant role in improving seizure detection results by overcoming inter-database variation of feature distribution. SIGNIFICANCE To the best of the author's knowledge, this is the first study reporting on the cross-database evaluation of classification of epileptic seizures and proven to be better generalization capability when evaluated using five databases and can contribute to accurate and robust detection of epileptic seizures in real-time.
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Affiliation(s)
- S Raghu
- Department of Neurosurgery, School for Mental Health and Neuroscience of the Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands; Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Natarajan Sriraam
- Center for Medical Electronics and Computing, MS Ramaiah Institute of Technology, Bengaluru, India.
| | - Erik D Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Danny M W Hilkman
- Department of Clinical Neurophysiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | | | | | - Pieter L Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands
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Sriraam N, Tamanna K, Narayan L, Khanum M, Raghu S, Hegde AS, Kumar AB. Multichannel EEG based inter-ictal seizures detection using Teager energy with backpropagation neural network classifier. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:1047-1055. [DOI: 10.1007/s13246-018-0694-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2018] [Accepted: 10/09/2018] [Indexed: 10/28/2022]
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Sriraam N, Raghu S, Tamanna K, Narayan L, Khanum M, Hegde AS, Kumar AB. Automated epileptic seizures detection using multi-features and multilayer perceptron neural network. Brain Inform 2018; 5:10. [PMID: 30175391 PMCID: PMC6170940 DOI: 10.1186/s40708-018-0088-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 08/10/2018] [Indexed: 11/12/2022] Open
Abstract
Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule–Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h−1, respectively, using multi-features. Further, the results indicate the proposed study is suitable for real-time seizure recognition from multi-channel EEG recording. The graphical user interface was developed in MATLAB to provide an automated biomarker for normal and epileptic EEG signals.
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Affiliation(s)
- N Sriraam
- Centre for Medical Electronics and Computing, Ramaiah Institute of Technology (Affiliated to VTU Belgaum), Bengaluru, India.
| | - S Raghu
- Centre for Medical Electronics and Computing, Ramaiah Institute of Technology (Affiliated to VTU Belgaum), Bengaluru, India
| | - Kadeeja Tamanna
- Centre for Medical Electronics and Computing, Ramaiah Institute of Technology (Affiliated to VTU Belgaum), Bengaluru, India
| | - Leena Narayan
- Centre for Medical Electronics and Computing, Ramaiah Institute of Technology (Affiliated to VTU Belgaum), Bengaluru, India
| | - Mehraj Khanum
- Centre for Medical Electronics and Computing, Ramaiah Institute of Technology (Affiliated to VTU Belgaum), Bengaluru, India
| | - A S Hegde
- Institute of Neuroscience, Ramaiah Medical College and Hospitals, Bengaluru, India
| | - Anjani Bhushan Kumar
- Institute of Neuroscience, Ramaiah Medical College and Hospitals, Bengaluru, India
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Ghaderyan P, Abbasi A. A novel cepstral-based technique for automatic cognitive load estimation. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.07.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Ghaderyan P, Abbasi A. An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations. Int J Psychophysiol 2016; 110:91-101. [PMID: 27780715 DOI: 10.1016/j.ijpsycho.2016.10.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 10/16/2016] [Accepted: 10/17/2016] [Indexed: 12/17/2022]
Abstract
Automatic workload estimation has received much attention because of its application in error prevention, diagnosis, and treatment of neural system impairment. The development of a simple but reliable method using minimum number of psychophysiological signals is a challenge in automatic workload estimation. To address this challenge, this paper presented three different decomposition techniques (Fourier, cepstrum, and wavelet transforms) to analyze electrodermal activity (EDA). The efficiency of various statistical and entropic features was investigated and compared. To recognize different levels of an arithmetic task, the features were processed by principal component analysis and machine-learning techniques. These methods have been incorporated into a workload estimation system based on two types: feature-level and decision-level combinations. The results indicated the reliability of the method for automatic and real-time inference of psychological states. This method provided a quantitative estimation of the workload levels and a bias-free evaluation approach. The high-average accuracy of 90% and cost effective requirement were the two important attributes of the proposed workload estimation system. New entropic features were proved to be more sensitive measures for quantifying time and frequency changes in EDA. The effectiveness of these measures was also compared with conventional tonic EDA measures, demonstrating the superiority of the proposed method in achieving accurate estimation of workload levels.
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Affiliation(s)
- Peyvand Ghaderyan
- Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
| | - Ataollah Abbasi
- Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran.
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