{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:27:28Z","timestamp":1776443248534,"version":"3.51.2"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T00:00:00Z","timestamp":1610150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s00521-020-05672-2","type":"journal-article","created":{"date-parts":[[2021,1,9]],"date-time":"2021-01-09T17:38:21Z","timestamp":1610213901000},"page":"9089-9108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Deep neural network architectures for dysarthric speech analysis and recognition"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1523-2982","authenticated-orcid":false,"given":"Brahim Fares","family":"Zaidi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sid Ahmed","family":"Selouani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malika","family":"Boudraa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammed","family":"Sidi Yakoub","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,9]]},"reference":[{"issue":"2","key":"5672_CR1","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1044\/jshr.1202.246","volume":"12","author":"FL Darley","year":"1969","unstructured":"Darley FL, Aronson AE, Brown JR (1969) Differential diagnostic patterns of dysarthria. J Speech Hear Res 12(2):246\u2013269. https:\/\/doi.org\/10.1044\/jshr.1202.246","journal-title":"J Speech Hear Res"},{"key":"5672_CR2","unstructured":"Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. In: Proceedings of the 14th international conference on artificial intelligence and statistics, vol 15, pp 315\u2013323, Lauderdale, FL, USA"},{"key":"5672_CR3","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1016\/B978-0-444-52901-5.00022-8","volume":"110","author":"P Enderby","year":"2013","unstructured":"Enderby P (2013) Disorders of communication: dysarthria. Handb Clin Neurol 110:273\u2013281. https:\/\/doi.org\/10.1016\/B978-0-444-52901-5.00022-8","journal-title":"Handb Clin Neurol"},{"issue":"3","key":"5672_CR4","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1080\/07434610012331279044","volume":"16","author":"K Hux","year":"2000","unstructured":"Hux K, Rankin-Erickson J, Manasse N, Lauritzen E (2000) Accuracy of three speech recognition systems: case study of dysarthric speech. Augment Altern Commun 16(3):186\u2013196. https:\/\/doi.org\/10.1080\/07434610012331279044","journal-title":"Augment Altern Commun"},{"issue":"1","key":"5672_CR5","first-page":"13","volume":"41","author":"E Rosengren","year":"2000","unstructured":"Rosengren E (2000) Perceptual analysis of dysarthric speech in the enable project. J TMH-QPSR 41(1):13\u201318","journal-title":"J TMH-QPSR"},{"key":"5672_CR6","unstructured":"Le Scaon R (2015) Projet 3A: D\u00e9tection du langage d\u2019un locuteur sur enregistrement audio."},{"issue":"4","key":"5672_CR7","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3109\/07434618.2010.532508","volume":"26","author":"SK Fager","year":"2010","unstructured":"Fager SK, Beukelman DR, Jakobs T, Hosom JP (2010) Evaluation of a speech recognition prototype for speakers with moderate and severe dysarthria: a preliminary report. Augment Altern Commun 26(4):267\u2013277. https:\/\/doi.org\/10.3109\/07434618.2010.532508","journal-title":"Augment Altern Commun"},{"issue":"2","key":"5672_CR8","doi-asserted-by":"publisher","first-page":"173","DOI":"10.3109\/13682828609012275","volume":"21","author":"W Ziegler","year":"1986","unstructured":"Ziegler W, von Cramon D (1986) Spastic dysarthria after acquired brain injury: an acoustic study. Br J Disord Commun 21(2):173\u2013187. https:\/\/doi.org\/10.3109\/13682828609012275","journal-title":"Br J Disord Commun"},{"key":"5672_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/540409","author":"S Selouani","year":"2009","unstructured":"Selouani S, Sidi Yakoub M, O\u2019Shaughnessy D (2009) Alternative speech communication system for persons with severe speech disorders. EURASIP J Adv Signal Process. https:\/\/doi.org\/10.1155\/2009\/540409","journal-title":"EURASIP J Adv Signal Process"},{"issue":"8","key":"5672_CR10","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1016\/j.medengphy.2005.11.002","volume":"28","author":"PD Polur","year":"2006","unstructured":"Polur PD, Miller GE (2006) Investigation of an HMM\/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals. Med Eng Phys 28(8):741\u2013748. https:\/\/doi.org\/10.1016\/j.medengphy.2005.11.002","journal-title":"Med Eng Phys"},{"key":"5672_CR11","doi-asserted-by":"publisher","unstructured":"Hasegawa-Johnson M, Gunderson J, Perlman A, Huang T (2006) HMM-based and SVM-based recognition of the speech of talkers with spastic dysarthria. In: 2006 IEEE international conference on acoustics speech and signal processing (ICASSP), vol 3, p III, Toulouse, France. https:\/\/doi.org\/10.1109\/ICASSP.2006.1660840","DOI":"10.1109\/ICASSP.2006.1660840"},{"issue":"1","key":"5672_CR12","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.aei.2014.01.001","volume":"28","author":"SR Shahamiri","year":"2014","unstructured":"Shahamiri SR, Salim SSB (2014) Artificial neural networks as speech recognisers for dysarthric speech: identifying the best-performing set of MFCC parameters and studying a speaker-independent approach. Adv Eng Inform 28(1):102\u2013110. https:\/\/doi.org\/10.1016\/j.aei.2014.01.001","journal-title":"Adv Eng Inform"},{"key":"5672_CR13","first-page":"190","volume":"26","author":"M Hermans","year":"2013","unstructured":"Hermans M, Schrauwen B (2013) Training and analysing deep recurrent neural networks. Adv Neural Inf Process Syst 26:190\u2013198","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"5672_CR14","doi-asserted-by":"publisher","first-page":"53","DOI":"10.3233\/PRM-2012-0196","volume":"5","author":"S Fager","year":"2012","unstructured":"Fager S, Bardach L, Russell S, Higginbotham J (2012) Access to augmentative and alternative communication: new technologies and clinical decision-making. J Pediatr Rehabil Med 5(1):53\u201361. https:\/\/doi.org\/10.3233\/PRM-2012-0196","journal-title":"J Pediatr Rehabil Med"},{"key":"5672_CR15","unstructured":"Das D, Lee CSG (2018) Cross-Scene trajectory level intention inference using gaussian process regression and na\u00efve registration. Department of Electrical and Computer Engineering Technical Reports, Paper 491. https:\/\/docs.lib.purdue.edu\/ecetr\/491\/"},{"key":"5672_CR16","doi-asserted-by":"publisher","unstructured":"Oue S, Marxer R, Rudzicz F (2015) Automatic dysfluency detection in dysarthric speech using deep belief networks. In: Proceedings of SLPAT 2015: 6th workshop on speech and language processing for assistive technologies (SLPAT), pp 60\u201364, Dresden, Germany. https:\/\/doi.org\/10.18653\/v1\/W15-5111","DOI":"10.18653\/v1\/W15-5111"},{"key":"5672_CR17","unstructured":"Burkert P, Trier F, Afzal M Z, Dengel A, Liwicki M (2015) Dexpression: deep convolutional neural network for expression recognition. arXiv:1509.05371v1"},{"issue":"8","key":"5672_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"issue":"5","key":"5672_CR19","doi-asserted-by":"publisher","first-page":"643","DOI":"10.4218\/etrij.2017-0260","volume":"40","author":"A Farhadipour","year":"2018","unstructured":"Farhadipour A, Veisi H, Asgari M, Keyvanrad MA (2018) Dysarthric speaker identification with different degrees of dysarthria severity using deep belief networks. Electron Telecommun Res Inst (ETRI) J 40(5):643\u2013652. https:\/\/doi.org\/10.4218\/etrij.2017-0260","journal-title":"Electron Telecommun Res Inst (ETRI) J"},{"issue":"3","key":"5672_CR20","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1109\/TNSRE.2018.2802914","volume":"26","author":"NM Joy","year":"2018","unstructured":"Joy NM, Umesh S (2018) Improving acoustic models in TORGO dysarthric speech database. IEEE Trans Neural Syst Rehabil Eng 26(3):637\u2013645. https:\/\/doi.org\/10.1109\/TNSRE.2018.2802914","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"5672_CR21","doi-asserted-by":"publisher","unstructured":"Jiao Y, Tu M, Berisha V, Liss J (2018) Simulating dysarthric speech for training data augmentation in clinical speech applications. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6009\u20136013, Calgary, AB, Canada. https:\/\/doi.org\/10.1109\/ICASSP.2018.8462290","DOI":"10.1109\/ICASSP.2018.8462290"},{"key":"5672_CR22","doi-asserted-by":"publisher","unstructured":"Tu M, Berisha V, Liss J (2017) Interpretable objective assessment of dysarthric speech based on deep neural networks. In: Proceedings of the annual conference of the international speech communication association, INTERSPEECH 2017, pp 1849\u20131853, Stockholm, Sweden. https:\/\/doi.org\/10.21437\/Interspeech.2017-1222","DOI":"10.21437\/Interspeech.2017-1222"},{"key":"5672_CR23","doi-asserted-by":"publisher","unstructured":"Ijitona T B, Soraghan J J, Lowit A, Di-Caterina G, Yue H (2017) Automatic detection of speech disorder in dysarthria using extended speech feature extraction and neural networks classification. In: IET 3rd international conference on intelligent signal processing (ISP 2017), pp 1\u20136, London. https:\/\/doi.org\/10.1049\/cp.2017.0360","DOI":"10.1049\/cp.2017.0360"},{"issue":"9","key":"5672_CR24","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1109\/TNSRE.2016.2638830","volume":"25","author":"S Chandrakala","year":"2017","unstructured":"Chandrakala S, Rajeswari N (2017) Representation learning based speech assistive system for persons with dysarthria. IEEE Trans Neural Syst Rehabil Eng 25(9):1510\u20131517. https:\/\/doi.org\/10.1109\/TNSRE.2016.2638830","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"5","key":"5672_CR25","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1121\/1.4967208","volume":"140","author":"M Tu","year":"2016","unstructured":"Tu M, Wisler A, Berisha V, Liss JM (2016) The relationship between perceptual disturbances in dysarthric speech and automatic speech recognition performance. J Acoust Soc Am 140(5):416\u2013422. https:\/\/doi.org\/10.1121\/1.4967208","journal-title":"J Acoust Soc Am"},{"key":"5672_CR26","doi-asserted-by":"publisher","unstructured":"Espana-Bonet C, Fonollosa JAR (2016) Automatic speech recognition with deep neural networks for impaired speech. In: International conference on advances in speech and language technologies for Iberian languages, IberSPEECH 2016, vol 10077. Springer, Cham, pp 97\u2013107. https:\/\/doi.org\/10.1007\/978-3-319-49169-1_10","DOI":"10.1007\/978-3-319-49169-1_10"},{"issue":"2","key":"5672_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.14738\/tmlai.22.150","volume":"2","author":"T Nakashika","year":"2014","unstructured":"Nakashika T, Yoshioka T, Takiguchi T, Ariki Y, Duffner S, Garcia C (2014) Convolutive bottleneck network with dropout for dysarthric speech recognition. Trans Mach Learn Artif Intell 2(2):1\u201315. https:\/\/doi.org\/10.14738\/tmlai.22.150","journal-title":"Trans Mach Learn Artif Intell"},{"key":"5672_CR28","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1016\/j.csl.2019.05.002","volume":"58","author":"E Y\u0131lmaz","year":"2019","unstructured":"Y\u0131lmaz E, Mitra V, Sivaraman G, Franco H (2019) Articulatory and Bottleneck features for speaker-independent ASR of dysarthric speech. Comput Speech Lang 58:319\u2013334. https:\/\/doi.org\/10.1016\/j.csl.2019.05.002","journal-title":"Comput Speech Lang"},{"key":"5672_CR29","doi-asserted-by":"publisher","unstructured":"Tripathi A, Bhosale S, Kopparapu S K (2020) A novel approach for intelligibility assessment in dysarthric subjects. In: 2020 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6779\u20136783, Barcelona, Spain. https:\/\/doi.org\/10.1109\/ICASSP40776.2020.9053339","DOI":"10.1109\/ICASSP40776.2020.9053339"},{"key":"5672_CR30","doi-asserted-by":"crossref","unstructured":"Sak H, Senior A, Beaufays F (2014) Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: INTERSPEECH 2014, 15th annual conference of the international speech communication association, pp 338\u2013342, Singapore","DOI":"10.21437\/Interspeech.2014-80"},{"key":"5672_CR31","doi-asserted-by":"publisher","unstructured":"Zhang Y, Chen G, Yu D, Yaco K, Khudanpur S, Glass J (2016) Highway long short-term memory RNNS for distant speech recognition. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 5755\u20135759, Shanghai, China. https:\/\/doi.org\/10.1109\/ICASSP.2016.7472780","DOI":"10.1109\/ICASSP.2016.7472780"},{"key":"5672_CR32","doi-asserted-by":"publisher","unstructured":"Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition & understanding (ASRU), pp 273\u2013278, Olomouc, Czech Republic. https:\/\/doi.org\/10.1109\/ASRU.2013.6707742","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"5672_CR33","doi-asserted-by":"publisher","unstructured":"Eyben F, W\u00f6llmer M, Schuller B, Graves A (2009) From speech to letters-using a novel neural network architecture for grapheme based ASR. In: 2009 IEEE workshop on automatic speech recognition and understanding (ASRU), pp. 376\u2013380, Merano, Italy. https:\/\/doi.org\/10.1109\/ASRU.2009.5373257","DOI":"10.1109\/ASRU.2009.5373257"},{"key":"5672_CR34","doi-asserted-by":"publisher","unstructured":"Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 6645\u20136649, Vancouver, BC, Canada. https:\/\/doi.org\/10.1109\/ICASSP.2013.6638947","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"5672_CR35","doi-asserted-by":"publisher","unstructured":"Mayle A, Mou Z, Bunescu R, Mirshekarian S, Xu L, LiuC (2019) Diagnosing dysarthria with long short-term memory networks. In: INTERSPEECH 2019, pp 4514\u20134518, Graz, Austria. https:\/\/doi.org\/10.21437\/Interspeech.2019-2903","DOI":"10.21437\/Interspeech.2019-2903"},{"issue":"2","key":"5672_CR36","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/JSTSP.2020.2967652","volume":"14","author":"C Bhat","year":"2020","unstructured":"Bhat C, Strik H (2020) Automatic assessment of sentence-level dysarthria intelligibility using BLSTM. IEEE J Sel Top Signal Process 14(2):322\u2013330. https:\/\/doi.org\/10.1109\/JSTSP.2020.2967652","journal-title":"IEEE J Sel Top Signal Process"},{"key":"5672_CR37","doi-asserted-by":"publisher","unstructured":"Menendez-Pidal X, Poliko JB, Peters SM, Leonzio JE, Bunnell HT (1996) The nemours database of dysarthric speech. In: Proceeding of 4th international conference on spoken language processing (ICSLP \u201896), vol 3, pp 1962\u20131965, Philadelphia, PA, USA. https:\/\/doi.org\/10.1109\/ICSLP.1996.608020","DOI":"10.1109\/ICSLP.1996.608020"},{"issue":"2","key":"5672_CR38","first-page":"1","volume":"2","author":"TS Nimbalkar","year":"2016","unstructured":"Nimbalkar TS, Bogiri N (2016) A novel integrated fragmentation clustering allocation approach for promote web telemedicine database system. Int J Adv Electron Comput Sci (IJAECS) 2(2):1\u201311","journal-title":"Int J Adv Electron Comput Sci (IJAECS)"},{"issue":"4","key":"5672_CR39","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TASSP.1980.1163420","volume":"28","author":"SB Davis","year":"1980","unstructured":"Davis SB, Mermelstein P (1980) Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans Acoust Speech Signal Process 28(4):357\u2013366. https:\/\/doi.org\/10.1109\/TASSP.1980.1163420","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"5672_CR40","doi-asserted-by":"publisher","unstructured":"Mohamed A, Hinton G, Penn G (2012) Understanding how deep belief networks perform acoustic modelling. In: 2012 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 4273-4276, Kyoto, Japan. https:\/\/doi.org\/10.1109\/ICASSP.2012.6288863","DOI":"10.1109\/ICASSP.2012.6288863"},{"issue":"4","key":"5672_CR41","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.1121\/1.399423","volume":"87","author":"H Hermansky","year":"1990","unstructured":"Hermansky H (1990) Perceptual linear predictive (PLP) analysis of speech. J Acoust Soc Am 87(4):1738\u20131752. https:\/\/doi.org\/10.1121\/1.399423","journal-title":"J Acoust Soc Am"},{"key":"5672_CR42","doi-asserted-by":"publisher","unstructured":"Zaidi B F, Selouani S, Boudraa M, Addou D, SidiYakoub M (2020) Automatic recognition system for dysarthric speech based on MFCC\u2019s, PNCC\u2019s, JITTER and SHIMMER coefficients. In: Advances in computer vision CVC 2019. Advances in intelligent systems and computing, vol 944. Springer, Cham, pp 500\u2013510. https:\/\/doi.org\/10.1007\/978-3-030-17798-0_40","DOI":"10.1007\/978-3-030-17798-0_40"},{"key":"5672_CR43","unstructured":"Young S, Evermann G, Gales M, Hain T, Kershaw D, Liu X, Moore G, Odell J, Ollason D, Povey D et al (1995\u20132015) The HTK book. Cambridge University Engineering Department"},{"issue":"3","key":"5672_CR44","first-page":"222","volume":"20","author":"D Alu","year":"2018","unstructured":"Alu D, Zoltan E, Stoica IC (2018) Voice based emotion recognition with convolutional neural networks for companion robots. Roman J Inf Sci Technol 20(3):222\u2013241","journal-title":"Roman J Inf Sci Technol"},{"issue":"2","key":"5672_CR45","first-page":"29","volume":"3","author":"MVV Bhagatpatil","year":"2015","unstructured":"Bhagatpatil MVV, Sardar V (2015) An automatic infants cry detection using linear frequency Cepstrum coefficients (LFCC). Int J Technol Enhanc Emerg Eng Res (IJTEEER) 3(2):29\u201334","journal-title":"Int J Technol Enhanc Emerg Eng Res (IJTEEER)"},{"key":"5672_CR46","unstructured":"Nair V, Hinton G E (2010) Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th international conference on machine learning (ICML), pp 1\u20138."},{"key":"5672_CR47","unstructured":"Dengsheng C, Jun L, Kai X (2020) AReLU: attention-based rectified linear unit. arXiv:2006.13858v2"},{"key":"5672_CR48","unstructured":"Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks. In: Advances in neural information processing systems, pp 971\u2013980"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05672-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-020-05672-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-05672-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T03:05:50Z","timestamp":1670727950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-020-05672-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,9]]},"references-count":48,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["5672"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-05672-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,9]]},"assertion":[{"value":"16 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}