{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:07:52Z","timestamp":1775470072274,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T00:00:00Z","timestamp":1708387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001786","name":"University of Adelaide","doi-asserted-by":"publisher","award":["75134777"],"award-info":[{"award-number":["75134777"]}],"id":[{"id":"10.13039\/501100001786","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001786","name":"University of Adelaide","doi-asserted-by":"publisher","award":["13133234"],"award-info":[{"award-number":["13133234"]}],"id":[{"id":"10.13039\/501100001786","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001786","name":"The University of Adelaide","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001786","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity.<\/jats:p>\n                <jats:p><jats:bold>Graphical Abstract<\/jats:bold><\/jats:p>","DOI":"10.1007\/s11517-024-03047-6","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T08:02:26Z","timestamp":1708416146000},"page":"1763-1779","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5905-1572","authenticated-orcid":false,"given":"Taseef Hasan","family":"Farook","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tashreque Mohammed","family":"Haq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lameesa","family":"Ramees","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Dudley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,20]]},"reference":[{"key":"3047_CR1","doi-asserted-by":"crossref","unstructured":"Farook TH, Rashid F, Ahmed S, Dudley J (2023) Clinical machine learning in parafunctional and altered functional occlusion: A systematic review. J Prosthet Dent","DOI":"10.1016\/j.prosdent.2023.01.013"},{"key":"3047_CR2","doi-asserted-by":"crossref","unstructured":"Farook TH, Rashid F, Alam MK, Dudley J (2022) Variables influencing the device-dependent approaches in digitally analysing jaw movement\u2014a systematic review. Clin Oral Investig 1\u201316","DOI":"10.1007\/s00784-022-04835-w"},{"key":"3047_CR3","doi-asserted-by":"crossref","unstructured":"Farook TH, Dudley J (2023) Automation and deep (machine) learning in temporomandibular joint disorder radiomics. A systematic review. J Oral Rehabil","DOI":"10.1111\/joor.13440"},{"key":"3047_CR4","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.jelekin.2007.07.005","volume":"19","author":"T \u00d8stensvik","year":"2009","unstructured":"\u00d8stensvik T, Veiersted KB, Nilsen P (2009) A method to quantify frequency and duration of sustained low-level muscle activity as a risk factor for musculoskeletal discomfort. J Electromyogr Kinesiol 19:283\u2013294","journal-title":"J Electromyogr Kinesiol"},{"key":"3047_CR5","doi-asserted-by":"publisher","first-page":"e43754","DOI":"10.2196\/43754","volume":"25","author":"T Rashi","year":"2023","unstructured":"Rashi T, Yom-Tov E (2023) Ethics of medical archival internet research data. J Med Internet Res 25:e43754","journal-title":"J Med Internet Res"},{"key":"3047_CR6","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1038\/gim.2012.23","volume":"14","author":"SM Wolf","year":"2012","unstructured":"Wolf SM, Crock BN, Van Ness B, Lawrenz F, Kahn JP, Beskow LM, Cho MK, Christman MF, Green RC, Hall R (2012) Managing incidental findings and research results in genomic research involving biobanks and archived data sets. Genet Med 14:361\u2013384","journal-title":"Genet Med"},{"key":"3047_CR7","doi-asserted-by":"publisher","first-page":"e15065","DOI":"10.2196\/15065","volume":"22","author":"I Hochberg","year":"2020","unstructured":"Hochberg I, Allon R, Yom-Tov E (2020) Assessment of the frequency of online searches for symptoms before diagnosis: analysis of archival data. J Med Internet Res 22:e15065","journal-title":"J Med Internet Res"},{"key":"3047_CR8","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.medengphy.2008.03.004","volume":"31","author":"Y Wu","year":"2009","unstructured":"Wu Y, Rangayyan RM, Zhou Y, Ng S-C (2009) Filtering electrocardiographic signals using an unbiased and normalized adaptive noise reduction system. Med Eng Phys 31:17\u201326","journal-title":"Med Eng Phys"},{"key":"3047_CR9","doi-asserted-by":"publisher","first-page":"102645","DOI":"10.1016\/j.jelekin.2022.102645","volume":"63","author":"SM Barron","year":"2022","unstructured":"Barron SM, Diaz TO, Pozzi F, Vasilopoulos T, Nichols JA (2022) Linear relationship between electromyography and shear wave elastography measurements persists in deep muscles of the upper extremity. J Electromyogr Kinesiol 63:102645","journal-title":"J Electromyogr Kinesiol"},{"key":"3047_CR10","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13054-023-04779-x","volume":"28","author":"AH Jonkman","year":"2024","unstructured":"Jonkman AH, Warnaar RSP, Baccinelli W, Carbon NM, D\u2019Cruz RF, Doorduin J, van Doorn JLM, Elshof J, Estrada-Petrocelli L, Gra\u00dfhoff J (2024) Analysis and applications of respiratory surface EMG: report of a round table meeting. Crit Care 28:2","journal-title":"Crit Care"},{"key":"3047_CR11","doi-asserted-by":"publisher","first-page":"107514","DOI":"10.1016\/j.engappai.2023.107514","volume":"128","author":"S Mahmud","year":"2024","unstructured":"Mahmud S, Chowdhury MEH, Kiranyaz S, Al Emadi N, Tahir AM, Hossain MS, Khandakar A, Al-Maadeed S (2024) Restoration of motion-corrupted EEG signals using attention-guided operational CycleGAN. Eng Appl Artif Intell 128:107514","journal-title":"Eng Appl Artif Intell"},{"key":"3047_CR12","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1251\/bpo115","volume":"8","author":"MBI Reaz","year":"2006","unstructured":"Reaz MBI, Hussain MS, Mohd-Yasin F (2006) Techniques of EMG signal analysis: detection, processing, classification and applications. Biol Proced Online 8:11\u201335","journal-title":"Biol Proced Online"},{"key":"3047_CR13","doi-asserted-by":"publisher","first-page":"105681","DOI":"10.1016\/j.bspc.2023.105681","volume":"88","author":"M Chen","year":"2024","unstructured":"Chen M, Cheng Q, Feng X, Zhao K, Zhou Y, Xing B, Tang S, Wang R, Duan J, Wang J (2024) Optimized variational mode decomposition algorithm based on adaptive thresholding method and improved whale optimization algorithm for denoising magnetocardiography signal. Biomed Signal Process Control 88:105681","journal-title":"Biomed Signal Process Control"},{"key":"3047_CR14","doi-asserted-by":"crossref","unstructured":"Makowski D, Pham T, Lau ZJ, Brammer JC, Lespinasse F, Pham H, Sch\u00f6lzel C, Chen SHA (2021) NeuroKit2: A Python toolbox for neurophysiological signal processing. Behav Res Methods 1\u20138","DOI":"10.31234\/osf.io\/eyd62"},{"key":"3047_CR15","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1111\/1754-9485.13261","volume":"65","author":"P Chlap","year":"2021","unstructured":"Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A (2021) A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 65:545\u2013563","journal-title":"J Med Imaging Radiat Oncol"},{"key":"3047_CR16","first-page":"441","volume":"9","author":"D Hazra","year":"2020","unstructured":"Hazra D, Byun Y-C (2020) SynSigGAN: Generative adversarial networks for synthetic biomedical signal generation. Biology (Basel) 9:441","journal-title":"Biology (Basel)"},{"key":"3047_CR17","doi-asserted-by":"crossref","unstructured":"Nowroozilarki Z, Mortazavi BJ, Jafari R (2023) Variational autoencoders for biomedical signal morphology clustering and noise detection. IEEE J Biomed Health Inform","DOI":"10.1109\/JBHI.2023.3320585"},{"key":"3047_CR18","doi-asserted-by":"publisher","first-page":"100456","DOI":"10.1016\/j.softx.2020.100456","volume":"11","author":"M Barandas","year":"2020","unstructured":"Barandas M, Folgado D, Fernandes L, Santos S, Abreu M, Bota P, Liu H, Schultz T, Gamboa H (2020) TSFEL: time series feature extraction library. SoftwareX 11:100456","journal-title":"SoftwareX"},{"key":"3047_CR19","doi-asserted-by":"publisher","first-page":"105510","DOI":"10.1016\/j.bspc.2023.105510","volume":"87","author":"TI Khan","year":"2024","unstructured":"Khan TI, Sakib N, Hassan MM, Ide S (2024) Gaussian mixture model in clustering acoustic emission signals for characterizing osteoarthritic knees. Biomed Signal Process Control 87:105510","journal-title":"Biomed Signal Process Control"},{"key":"3047_CR20","doi-asserted-by":"crossref","unstructured":"\u00c7er\u00e7i \u00c7, Temelta\u015f H (2018) Feature extraction of EMG signals, classification with ANN and kNN algorithms, in: 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, 1\u20134","DOI":"10.1109\/SIU.2018.8404207"},{"key":"3047_CR21","first-page":"138","volume":"71","author":"W Farag","year":"2020","unstructured":"Farag W (2020) Road-objects tracking for autonomous driving using lidar and radar fusion. J Electr Eng 71:138\u2013149","journal-title":"J Electr Eng"},{"key":"3047_CR22","doi-asserted-by":"publisher","first-page":"e0290497","DOI":"10.1371\/journal.pone.0290497","volume":"18","author":"TH Farook","year":"2023","unstructured":"Farook TH, Ahmed S, Talukder MSI, Dudley J (2023) A 3D printed electronic wearable device to generate vertical, horizontal and phono-articulatory jaw movement parameters: A concept implementation. PLoS One 18:e0290497","journal-title":"PLoS One"},{"key":"3047_CR23","doi-asserted-by":"crossref","unstructured":"Paul R, Hoque ASML (2010) Clustering medical data to predict the likelihood of diseases, in: 2010 Fifth International Conference on Digital Information Management (ICDIM), IEEE, pp. 44\u201349","DOI":"10.1109\/ICDIM.2010.5664638"},{"key":"3047_CR24","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1038\/s41746-019-0158-1","volume":"2","author":"M Lehne","year":"2019","unstructured":"Lehne M, Sass J, Essenwanger A, Schepers J, Thun S (2019) Why digital medicine depends on interoperability. NPJ Digit Med 2:79","journal-title":"NPJ Digit Med"},{"key":"3047_CR25","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1147\/sj.442.0239","volume":"44","author":"A Boulanger","year":"2005","unstructured":"Boulanger A (2005) Open-source versus proprietary software: is one more reliable and secure than the other? IBM Syst J 44:239\u2013248","journal-title":"IBM Syst J"},{"key":"3047_CR26","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1038\/s41591-020-1041-y","volume":"26","author":"B Norgeot","year":"2020","unstructured":"Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, Arnaout R, Kohane IS, Saria S, Topol E (2020) Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med 26:1320\u20131324","journal-title":"Nat Med"},{"key":"3047_CR27","doi-asserted-by":"crossref","unstructured":"Patki N, Wedge R, Veeramachaneni K (2016) The synthetic data vault, in: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, pp. 399\u2013410","DOI":"10.1109\/DSAA.2016.49"},{"key":"3047_CR28","doi-asserted-by":"crossref","unstructured":"Verleysen M, Fran\u00e7ois D (2005) The curse of dimensionality in data mining and time series prediction, in: International Work-Conference on Artificial Neural Networks, Springer, pp. 758\u2013770","DOI":"10.1007\/11494669_93"},{"key":"3047_CR29","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9"},{"key":"3047_CR30","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53\u201365","journal-title":"J Comput Appl Math"},{"key":"3047_CR31","doi-asserted-by":"crossref","unstructured":"Rahmah N, Sitanggang IS (2016) Determination of optimal epsilon (eps) value on dbscan algorithm to clustering data on peatland hotspots in sumatra, in: IOP Conf Ser Earth Environ Sci, IoP Publishing, p. 012012","DOI":"10.1088\/1755-1315\/31\/1\/012012"},{"key":"3047_CR32","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1023\/A:1009745219419","volume":"2","author":"J Sander","year":"1998","unstructured":"Sander J, Ester M, Kriegel H-P, Xu X (1998) Density-based clustering in spatial databases: the algorithm gdbscan and its applications. Data Min Knowl Discov 2:169\u2013194","journal-title":"Data Min Knowl Discov"},{"key":"3047_CR33","doi-asserted-by":"publisher","first-page":"14214","DOI":"10.1038\/s41598-021-93244-2","volume":"11","author":"B Lyu","year":"2021","unstructured":"Lyu B, Wu W, Hu Z (2021) A novel bidirectional clustering algorithm based on local density. Sci Rep 11:14214","journal-title":"Sci Rep"},{"key":"3047_CR34","doi-asserted-by":"publisher","first-page":"2605","DOI":"10.3390\/s20092605","volume":"20","author":"R AnicetZanini","year":"2020","unstructured":"AnicetZanini R, Luna Colombini E (2020) Parkinson\u2019s disease EMG data augmentation and simulation with DCGANs and style transfer. Sensors 20:2605","journal-title":"Sensors"},{"key":"3047_CR35","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1109\/LCOMM.2019.2963671","volume":"24","author":"W Ozan","year":"2020","unstructured":"Ozan W, Grammenos R, Darwazeh I (2020) Zero padding or cyclic prefix: evaluation for non-orthogonal signals. IEEE Commun Lett 24:690\u2013694","journal-title":"IEEE Commun Lett"},{"key":"3047_CR36","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.ins.2016.06.048","volume":"369","author":"KP Agrawal","year":"2016","unstructured":"Agrawal KP, Garg S, Sharma S, Patel P (2016) Development and validation of OPTICS based spatio-temporal clustering technique. Inf Sci (N Y) 369:388\u2013401","journal-title":"Inf Sci (N Y)"},{"key":"3047_CR37","doi-asserted-by":"publisher","first-page":"205","DOI":"10.21105\/joss.00205","volume":"2","author":"L McInnes","year":"2017","unstructured":"McInnes L, Healy J, Astels S (2017) hdbscan: Hierarchical density based clustering. J Open Source Softw 2:205","journal-title":"J Open Source Softw"},{"key":"3047_CR38","doi-asserted-by":"publisher","first-page":"104849","DOI":"10.1016\/j.autcon.2023.104849","volume":"150","author":"Y Liu","year":"2023","unstructured":"Liu Y, Bao Y (2023) Real-time remote measurement of distance using ultra-wideband (UWB) sensors. Autom Constr 150:104849","journal-title":"Autom Constr"},{"key":"3047_CR39","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s00202-020-01061-z","volume":"103","author":"G Dobri\u0107","year":"2021","unstructured":"Dobri\u0107 G, \u017darkovi\u0107 M (2021) Fuzzy expert system for metal-oxide surge arrester condition monitoring. Electr Eng 103:91\u2013101","journal-title":"Electr Eng"},{"key":"3047_CR40","doi-asserted-by":"crossref","unstructured":"Ahmed HO (2021) 17.16 GOPS\\W sustainable FLS-based wireless sensor network for surveillance system using FPGA, in: 2021 Integrated Communications Navigation and Surveillance Conference (ICNS), IEEE, pp. 1\u201310","DOI":"10.1109\/ICNS52807.2021.9441628"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03047-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-024-03047-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-024-03047-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T23:06:32Z","timestamp":1715123192000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-024-03047-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,20]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["3047"],"URL":"https:\/\/doi.org\/10.1007\/s11517-024-03047-6","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,20]]},"assertion":[{"value":"24 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The study was approved by the University of Adelaide Human Research and Ethics Committee (HREC H-2022\u2013185).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"All participants provided written and signed consent prior to participating in the research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent of the participants"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}