{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:23:00Z","timestamp":1778080980596,"version":"3.51.4"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T00:00:00Z","timestamp":1751414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Manipal Academy of Higher Education, Manipal"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04148-1","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T12:39:50Z","timestamp":1751459990000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Enhanced EEG Signal Processing for Accurate Epileptic Seizure Detection"],"prefix":"10.1007","volume":"6","author":[{"given":"S. A.","family":"Karthik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. N.","family":"Bharath","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B. R.","family":"Ramji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiran","family":"Puttegowda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"B.","family":"Aruna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D. S. Sunil","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"4148_CR1","doi-asserted-by":"publisher","first-page":"104566","DOI":"10.1016\/j.bspc.2022.104566","volume":"82","author":"M Shen","year":"2023","unstructured":"Shen M, Wen P, Song B, Li Y. Real-time epilepsy seizure detection based on EEG using tunable-Q wavelet transform and convolutional neural network. Biomed Signal Process Control. 2023;82:104566.","journal-title":"Biomed Signal Process Control"},{"key":"4148_CR2","doi-asserted-by":"crossref","unstructured":"Thangarajoo R, Gandhi M, Srivastava G, Haque F, Hamid S, Ashrif A, Bakar A. and M. Bhuiyan. Machine learning-based epileptic seizure detection methods using wavelet and EMD-based decomposition techniques: A review. Sensors 21, no. 24 (2021).","DOI":"10.3390\/s21248485"},{"key":"4148_CR3","doi-asserted-by":"crossref","unstructured":"Mir W, Ahmad M, Anjum, Shahab S. Deep-EEG: an optimized and robust framework and method for EEG-based diagnosis of epileptic seizure. Diagnostics 13, no. 4 (2023): 773.","DOI":"10.3390\/diagnostics13040773"},{"key":"4148_CR4","doi-asserted-by":"publisher","first-page":"103645","DOI":"10.1016\/j.bspc.2022.103645","volume":"76","author":"G Kaushik","year":"2022","unstructured":"Kaushik G, Gaur P, Sharma RR, Ram Bilas Pachori. EEG signal based seizure detection focused on Hjorth parameters from tunable-Q wavelet sub-bands. Biomed Signal Process Control. 2022;76:103645.","journal-title":"Biomed Signal Process Control"},{"key":"4148_CR5","doi-asserted-by":"crossref","unstructured":"Abdallah T, Jrad N, Hajjar SE, Abdallah F. Anne Humeau-Heurtier, and Patrick Van Bogaert. A Novel Unsupervised approach for accurate epileptic seizure detection. In 2024 32nd European Signal Processing Conference (EUSIPCO), pp. 1426\u20131430. IEEE, 2024.","DOI":"10.23919\/EUSIPCO63174.2024.10715300"},{"issue":"1","key":"4148_CR6","first-page":"159","volume":"14","author":"Nas\u0131m Pour","year":"2021","unstructured":"Pour Nas\u0131m. Mostafa, and Y\u00fccel \u00f6zbek. Epileptic seizure detection based on Eeg signal using boosting classifiers. Erzincan Univ J Sci Technol. 2021;14(1):159\u201367.","journal-title":"Erzincan Univ J Sci Technol"},{"key":"4148_CR7","doi-asserted-by":"crossref","unstructured":"Bhattacherjee I. Real-Time Epileptic Seizure Detection using Machine Learning Techniques. In 2022 9th International Conference on Computing for Sustainable Global Development (INDIACom), pp. 01\u201307. IEEE, 2022.","DOI":"10.23919\/INDIACom54597.2022.9763176"},{"key":"4148_CR8","doi-asserted-by":"crossref","unstructured":"Ahmad I, Liu Z, Li L, Ullah I, Aboyeji ST, Wang X, Samuel OW et al. Robust epileptic seizure detection based on biomedical signals using an advanced multi-view deep feature learning approach. IEEE J Biomedical Health Inf (2024).","DOI":"10.1109\/JBHI.2024.3396130"},{"issue":"43","key":"4148_CR9","doi-asserted-by":"publisher","first-page":"3838","DOI":"10.17485\/IJST\/v16i43.2356","volume":"16","author":"MZ Ahamed","year":"2023","unstructured":"Ahamed MZ, Mastani SA. Power and area optimized deep learning framework for accurate automatic epileptic seizure detection. Indian J Sci Technol. 2023;16(43):3838\u201345.","journal-title":"Indian J Sci Technol"},{"key":"4148_CR10","doi-asserted-by":"crossref","unstructured":"Hasan S, Ahmad O, Yasin E, Taib O, Nassif M, Joudeh. and Saben Audaall. Advanced Machine Learning Techniques for Precise EEG Analysis and Epileptic Seizure Detection. In 2024 22nd International Conference on Research and Education in Mechatronics (REM), pp. 354\u2013358. IEEE, 2024.","DOI":"10.1109\/REM63063.2024.10735614"},{"key":"4148_CR11","doi-asserted-by":"crossref","unstructured":"Sudheesh KV, Ravi V, Almeshari M, Alzamil Y, Sunil Kumar DS. A new deep learning model based on neuroimaging for predicting Alzheimer\u2019s disease. Open Bioinf J 16, 1 (2023).","DOI":"10.2174\/0118750362260635230922051326"},{"issue":"1","key":"4148_CR12","doi-asserted-by":"publisher","first-page":"015002","DOI":"10.1088\/1741-2552\/acb089","volume":"20","author":"X Zhao","year":"2023","unstructured":"Zhao X, Yoshida N, Ueda T. Hidenori sugano, and Toshihisa tanaka. Epileptic seizure detection by using interpretable machine learning models. J Neural Eng. 2023;20(1):015002.","journal-title":"J Neural Eng"},{"issue":"2","key":"4148_CR13","doi-asserted-by":"publisher","first-page":"025041","DOI":"10.1088\/2057-1976\/ad29a3","volume":"10","author":"M Ghaempour","year":"2024","unstructured":"Ghaempour M, Hassanli K, Abiri E. An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal. Biomedical Phys Eng Express. 2024;10(2):025041.","journal-title":"Biomedical Phys Eng Express"},{"key":"4148_CR14","doi-asserted-by":"crossref","unstructured":"Parameshachari B, Sunil Kumar DS, Sudheesh KV, Deepak R, Deepak HA. Classification of Alzheimer\u2019s Disease Using 2D\/3D Convolutional Neural Networks. In 2023 International Conference on Applied Intelligence and Sustainable Computing (ICAISC), pp. 1\u20135. IEEE, 2023.","DOI":"10.1109\/ICAISC58445.2023.10200305"},{"key":"4148_CR15","doi-asserted-by":"crossref","unstructured":"Zwawi A, Krkara M, Samir M. Elbuni. Epileptic Seizure Detection Using Single-Channel EEG and Artificial Intelligence Techniques. In 2023 IEEE 3rd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), pp. 248\u2013253. IEEE, 2023.","DOI":"10.1109\/MI-STA57575.2023.10169734"},{"issue":"11","key":"4148_CR16","doi-asserted-by":"publisher","first-page":"5780","DOI":"10.3390\/ijerph18115780","volume":"18","author":"A Shoeibi","year":"2021","unstructured":"Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, et al. Epileptic seizures detection using deep learning techniques: a review. Int J Environ Res Public Health. 2021;18(11):5780.","journal-title":"Int J Environ Res Public Health"},{"issue":"1","key":"4148_CR17","doi-asserted-by":"publisher","first-page":"16916","DOI":"10.1038\/s41598-024-67855-4","volume":"14","author":"J Zhang","year":"2024","unstructured":"Zhang J, Zheng S, Chen W, Du G, Fu Q, Jiang H. A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction. Sci Rep. 2024;14(1):16916.","journal-title":"Sci Rep"},{"key":"4148_CR18","doi-asserted-by":"crossref","unstructured":"Kumar G, Sasi, Jai Sharma K. Detection of Epileptic Seizures using Novel Multi-Layered Convolution Neural Network in Comparison with Fully Convolutional Neural Network to Improve the Accuracy. In 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), pp. 1\u20134. IEEE, 2023.","DOI":"10.1109\/ICONSTEM56934.2023.10142661"},{"key":"4148_CR19","doi-asserted-by":"crossref","unstructured":"Joshi S, Rout PK, Samanta IS, Cherukuri M. and Kunjabihari Swain. Epileptic Seizure Detection using Denoising Autoencoder. In 2024 International Conference on Emerging Systems and Intelligent Computing (ESIC), pp. 5\u201310. IEEE, 2024.","DOI":"10.1109\/ESIC60604.2024.10481560"},{"issue":"03","key":"4148_CR20","doi-asserted-by":"publisher","first-page":"2450012","DOI":"10.1142\/S0129065724500126","volume":"34","author":"X Dong","year":"2024","unstructured":"Dong X, Wen Y, Ji D, Yuan S, Liu Z, Shang W, Zhou W. Epileptic seizure detection with an end-to-end Temporal convolutional network and bidirectional long short-term memory model. Int J Neural Syst. 2024;34(03):2450012.","journal-title":"Int J Neural Syst"},{"key":"4148_CR21","doi-asserted-by":"crossref","unstructured":"Choubey H, Pandey A, Mahor V, Dubey R, Manjhvar AK, Chaudhari S. A Deep Learning Based Neural Network for Detection of Epileptic Seizure. In International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology, pp. 155\u2013162. Singapore: Springer Nature Singapore, 2022.","DOI":"10.1007\/978-981-99-1431-9_12"},{"issue":"11","key":"4148_CR22","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.3390\/s24113360","volume":"24","author":"B Wang","year":"2024","unstructured":"Wang B, Xu Y, Peng S, Wang H, Li F. Detection method of epileptic seizures using a neural network model based on multimodal dual-stream networks. Sensors. 2024;24(11):3360.","journal-title":"Sensors"},{"key":"4148_CR23","doi-asserted-by":"crossref","unstructured":"Gar\u00e7\u00e3o VM, Abreu M, Peralta AR, Bentes C, Fred A, Hugo P. Da silva. A novel approach to automatic seizure detection using computer vision and independent component analysis. Epilepsia 64, 9 (2023): 2472\u201383.","DOI":"10.1111\/epi.17677"},{"issue":"13","key":"4148_CR24","doi-asserted-by":"publisher","first-page":"5783","DOI":"10.3390\/app14135783","volume":"14","author":"AD Urbina Fredes, Sebasti\u00e1n","year":"2024","unstructured":"Urbina Fredes, Sebasti\u00e1n AD, Firoozabadi P, Adasme D, Zabala-Blanco. Pablo Palacios j\u00e1tiva, and Cesar Azurdia-Meza. Enhanced epileptic seizure detection through Wavelet-based analysis of EEG signal processing. Appl Sci. 2024;14(13):5783.","journal-title":"Appl Sci"},{"key":"4148_CR25","doi-asserted-by":"crossref","unstructured":"Ouichka O, Echtioui A, Hamam H. Deep learning models for predicting epileptic seizures using iEEG signals. Electronics 11, no. 4 (2022): 605.","DOI":"10.3390\/electronics11040605"},{"key":"4148_CR26","doi-asserted-by":"crossref","unstructured":"Chirasani SK, Reddy, Manikandan S. A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism. Soft Computing 26, no. 11 (2022): 5389\u20135397.","DOI":"10.1007\/s00500-022-07122-8"},{"key":"4148_CR27","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.ymeth.2021.07.006","volume":"202","author":"Y Jiang","year":"2022","unstructured":"Jiang Y, Lu Y, Yang L. An epileptic seizure prediction model based on a time-wise attention simulation module and a pretrained ResNet. Methods. 2022;202:117\u201326.","journal-title":"Methods"},{"key":"4148_CR28","doi-asserted-by":"publisher","first-page":"102469","DOI":"10.1016\/j.bspc.2021.102469","volume":"66","author":"J Oliva","year":"2021","unstructured":"Oliva J. Tales, and Jo\u00e3o Lu\u00eds Garcia rosa. Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection. Biomed Signal Process Control. 2021;66:102469.","journal-title":"Biomed Signal Process Control"},{"key":"4148_CR29","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1016\/j.neunet.2020.01.017","volume":"124","author":"S Raghu","year":"2020","unstructured":"Raghu S, Sriraam N, Temel Y, Rao SV, Pieter L. Kubben. EEG based multi-class seizure type classification using convolutional neural network and transfer learning. Neural Netw. 2020;124:202\u201312.","journal-title":"Neural Netw"},{"key":"4148_CR30","doi-asserted-by":"publisher","first-page":"119010","DOI":"10.1016\/j.eswa.2022.119010","volume":"213","author":"L Ilias","year":"2023","unstructured":"Ilias L, Askounis D, John Psarras. Multimodal detection of epilepsy with deep neural networks. Expert Syst Appl. 2023;213:119010.","journal-title":"Expert Syst Appl"},{"key":"4148_CR31","doi-asserted-by":"publisher","first-page":"121727","DOI":"10.1016\/j.eswa.2023.121727","volume":"238","author":"M Anita","year":"2024","unstructured":"Anita M, Meena Kowshalya A. Automatic epileptic seizure detection using MSA-DCNN and LSTM techniques with EEG signals. Expert Syst Appl. 2024;238:121727.","journal-title":"Expert Syst Appl"},{"key":"4148_CR32","doi-asserted-by":"crossref","unstructured":"Sun Q, Liu Y, Li S, Wang C. Automatic epileptic seizure detection using PSO-based feature selection and multilevel spectral analysis for EEG signals, Journal of Sensors, vol. 2022, Article ID 6585800, 16 pages, 2022.","DOI":"10.1155\/2022\/6585800"},{"key":"4148_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2174\/0118749445286599240311102956","volume":"17","author":"K Puttegowda","year":"2024","unstructured":"Puttegowda K, Sunil Kumar DS, Sahana Mallu, Vijay CP, Vinayakumar Ravi, Sushmitha BC. Automatic COVID-19 prediction with comprehensible machine learning models. Open Public Health J. 2024;17:1.","journal-title":"Open Public Health J"},{"key":"4148_CR34","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.aej.2022.10.014","volume":"65","author":"AA Ein Shoka","year":"2023","unstructured":"Ein Shoka AA, Dessouky MM, El-Sayed A, El-Din E, Hemdan. An efficient CNN based epileptic seizures detection framework using encrypted EEG signals for secure telemedicine applications. Alexandria Eng J. 2023;65:399\u2013412.","journal-title":"Alexandria Eng J"},{"key":"4148_CR35","doi-asserted-by":"crossref","unstructured":"KR AB, Srinivasan S, Mathivanan SK, et al. A multidimensional hybrid CNN-BiLSTM framework for epileptic seizure detection using electroencephalogram signal scrutiny. Syst Soft Comput. 2023;5. Article ID 200062.","DOI":"10.1016\/j.sasc.2023.200062"},{"key":"4148_CR36","doi-asserted-by":"publisher","first-page":"20605","DOI":"10.1007\/s00521-023-08832-2","volume":"35","author":"S Shanmugam","year":"2023","unstructured":"Shanmugam S, Dharmar S. A CNN-LSTM hybrid network for automatic seizure detection in EEG signals. Neural Comput Appl. 2023;35:20605\u201317.","journal-title":"Neural Comput Appl"},{"key":"4148_CR37","doi-asserted-by":"crossref","unstructured":"Shen M, Yang F, Wen P, Song B, Li Y. A real-time epilepsy seizure detection approach based on EEG using short-time Fourier transform and Google-Net convolutional neural network. Heliyon 10, no. 11 (2024).","DOI":"10.1016\/j.heliyon.2024.e31827"},{"key":"4148_CR38","doi-asserted-by":"publisher","first-page":"9404","DOI":"10.1038\/s41598-025-90315-6","volume":"15","author":"Z Huang","year":"2025","unstructured":"Huang Z, Yang Y, Ma Y, et al. EEG detection and recognition model for epilepsy based on dual attention mechanism. Sci Rep. 2025;15:9404. https:\/\/doi.org\/10.1038\/s41598-025-90315-6.","journal-title":"Sci Rep"},{"key":"4148_CR39","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s12911-024-02845-0","volume":"25","author":"X Cao","year":"2025","unstructured":"Cao X, Zheng S, Zhang J, et al. A hybrid CNN-Bi-LSTM model with feature fusion for accurate epilepsy seizure detection. BMC Med Inf Decis Mak. 2025;25:6. https:\/\/doi.org\/10.1186\/s12911-024-02845-0.","journal-title":"BMC Med Inf Decis Mak"},{"key":"4148_CR40","doi-asserted-by":"publisher","first-page":"14313","DOI":"10.1038\/s41598-025-95831-z","volume":"15","author":"Y Berrich","year":"2025","unstructured":"Berrich Y, Guennoun Z. EEG-based epilepsy detection using CNN-SVM and DNN-SVM with feature dimensionality reduction by PCA. Sci Rep. 2025;15:14313. https:\/\/doi.org\/10.1038\/s41598-025-95831-z.","journal-title":"Sci Rep"},{"key":"4148_CR41","doi-asserted-by":"crossref","unstructured":"Betgeri S, Santosh M, Shukla D, Kumar SB, Khan MA, Khan, Nora A. Alkhaldi Enhancing Seizure Detect Hybrid XGBoost Recurr Neural Networks Neurosci Inf (2025): 100206.","DOI":"10.1016\/j.neuri.2025.100206"},{"key":"4148_CR42","doi-asserted-by":"publisher","first-page":"16951","DOI":"10.1038\/s41598-025-01747-z","volume":"15","author":"T Tuncer","year":"2025","unstructured":"Tuncer T, Dogan S. An explainable EEG epilepsy detection model using friend pattern. Sci Rep. 2025;15:16951. https:\/\/doi.org\/10.1038\/s41598-025-01747-z.","journal-title":"Sci Rep"},{"key":"4148_CR43","doi-asserted-by":"publisher","first-page":"17710","DOI":"10.1038\/s41598-023-44318-w","volume":"13","author":"G Yogarajan","year":"2023","unstructured":"Yogarajan G, Alsubaie N, Rajasekaran G, et al. EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network. Sci Rep. 2023;13:17710. https:\/\/doi.org\/10.1038\/s41598-023-44318-w.","journal-title":"Sci Rep"},{"key":"4148_CR44","doi-asserted-by":"publisher","first-page":"10051","DOI":"10.1007\/s00521-022-07809-x","volume":"35","author":"D Raab","year":"2023","unstructured":"Raab D, Theissler A, Spiliopoulou M. XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series. Neural Comput Applic. 2023;35:10051\u201368. https:\/\/doi.org\/10.1007\/s00521-022-07809-x.","journal-title":"Neural Comput Applic"},{"key":"4148_CR45","doi-asserted-by":"publisher","first-page":"3884","DOI":"10.1109\/TNSRE.2023.3317093","volume":"31","author":"S Liu","year":"2023","unstructured":"Liu S, Wang J, Li S, Cai L. Epileptic seizure detection and prediction in EEGs using power spectra density parameterization. IEEE Trans Neural Syst Rehabil Eng. 2023;31:3884\u201394. https:\/\/doi.org\/10.1109\/TNSRE.2023.3317093.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"6","key":"4148_CR46","doi-asserted-by":"publisher","first-page":"061907","DOI":"10.1103\/PhysRevE.64.061907","volume":"64","author":"RG Andrzejak","year":"2001","unstructured":"Andrzejak RG, Lehnertz K, Rieke C, Mormann F, David P, Elger CE. 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(6):061907.","journal-title":"Phys Rev E"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04148-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04148-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T12:39:55Z","timestamp":1751459995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04148-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,2]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["4148"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04148-1","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,2]]},"assertion":[{"value":"14 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research Involving Human and \/or Animals"}},{"value":"Not Applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"Not Applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"608"}}