{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T05:01:09Z","timestamp":1769835669124,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Narcolepsy with cataplexy is a severe lifelong disorder characterized, among others, by sudden loss of bilateral face muscle tone triggered by emotions (cataplexy). A recent approach for the diagnosis of the disease is based on a completely manual analysis of video recordings of patients undergoing emotional stimulation made on-site by medical specialists, looking for specific facial behavior motor phenomena. We present here the CAT-CAD tool for automatic detection of cataplexy symptoms, with the double aim of (1) supporting neurologists in the diagnosis\/monitoring of the disease and (2) facilitating the experience of patients, allowing them to conduct video recordings at home. CAT-CAD includes a front-end medical interface (for the playback\/inspection of patient recordings and the retrieval of videos relevant to the one currently played) and a back-end AI-based video analyzer (able to automatically detect the presence of disease symptoms in the patient recording). Analysis of patients\u2019 videos for discovering disease symptoms is based on the detection of facial landmarks, and an alternative implementation of the video analyzer, exploiting deep-learning techniques, is introduced. Performance of both approaches is experimentally evaluated using a benchmark of real patients\u2019 recordings, demonstrating the effectiveness of the proposed solutions.<\/jats:p>","DOI":"10.3390\/computers10040051","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T10:58:07Z","timestamp":1618311487000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CAT-CAD: A Computer-Aided Diagnosis Tool for Cataplexy"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8074-1129","authenticated-orcid":false,"given":"Ilaria","family":"Bartolini","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering (DISI), Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy"}]},{"given":"Andrea","family":"Di Luzio","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering (DISI), Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3480","DOI":"10.1093\/brain\/awr244","article-title":"Complex Movement Disorders at Disease Onset in Childhood Narcolepsy with Cataplexy","volume":"134","author":"Plazzi","year":"2011","journal-title":"Brain J. Neurol."},{"key":"ref_2","unstructured":"American Academy of Sleep Medicine (2014). The International Classification of Sleep Disorders, Elsevier. [3rd ed.]. (ICSD-3)."},{"key":"ref_3","first-page":"77","article-title":"Validation of a Cataplexy Questionnaire in 983 Sleep-Disorders Patients","volume":"22","author":"Guilleminault","year":"1999","journal-title":"Sleep"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"zsy026","DOI":"10.1093\/sleep\/zsy026","article-title":"The Distinguishing Motor Features of Cataplexy: A Study from Video-Recorded Attacks","volume":"41","author":"Pizza","year":"2018","journal-title":"Sleep"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lazli, L., Boukadoum, M., and Mohamed, O.A. (2020). A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. Appl. Sci., 10.","DOI":"10.3390\/app10051894"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bartolini, I., and Di Luzio, A. (2017, January 4\u20136). Towards Automatic Recognition of Narcolepsy with Cataplexy. Proceedings of the 15th ACM International Conference on Advances in Mobile Computing & Multimedia (MoMM2017), Salzburg, Austria.","DOI":"10.1145\/3151848.3151875"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1109\/TITS.2016.2582900","article-title":"Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State","volume":"18","author":"Mandal","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bakheet, S., and Al-Hamadi, A. (2021). A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Na\u00efve Bayesian Classification. Brain Sci., 11.","DOI":"10.3390\/brainsci11020240"},{"key":"ref_9","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients for Human Detection. Proceedings of the 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"127202","DOI":"10.1117\/1.3657506","article-title":"Vision-Based Method for Detecting Driver Drowsiness and Distraction in Driver Monitoring System","volume":"50","author":"Jo","year":"2011","journal-title":"Opt. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yao, K., Lin, W., Fang, C., Wang, J., Chang, S., and Chen, S. (2010, January 16\u201319). Real-Time Vision-Based Driver Drowsiness\/Fatigue Detection System. Proceedings of the 71st IEEE Vehicular Technology Conference Proceedings (VTC 2010), Taipei, Taiwan.","DOI":"10.1109\/VETECS.2010.5493972"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bergasa, L.M., Buenaposada, J.M., Nuevo, J., Jimenez, P., and Baumela, L. (2008, January 12\u201315). Analysing Driver\u2019s Attention Level Using Computer Vision. Proceedings of the 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), Beijing, China.","DOI":"10.1109\/ITSC.2008.4732544"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wang, P., and Shen, L. (2012, January 16\u201318). A Method Detecting Driver Drowsiness State Based on Multi-Features of Face. Proceedings of the 5th International Congress on Image and Signal Processing (CISP 2012), Chongqing, China.","DOI":"10.1109\/CISP.2012.6469987"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.sleep.2017.08.021","article-title":"A Standardized Test to Document Cataplexy","volume":"53","author":"Vandi","year":"2019","journal-title":"Sleep Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TMI.2016.2553401","article-title":"Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique","volume":"35","author":"Greenspan","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep Learning Applications in Medical Image Analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sriporn, K., Tsai, C.-F., Tsai, C.-E., and Wang, P. (2020). Analyzing Malaria Disease Using Effective Deep Learning Approach. Diagnostics, 10.","DOI":"10.3390\/diagnostics10100744"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, J.-W., Lin, W.-J., Lin, C.-Y., Hung, C.-L., Hou, C.-P., Cho, C.-C., Young, H.-T., and Tang, C.-Y. (2020). Automated Classification of Blood Loss from Transurethral Resection of the Prostate Surgery Videos Using Deep Learning Technique. Appl. Sci., 10.","DOI":"10.3390\/app10144908"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gil-Mart\u00edn, M., Montero, J.M., and San-Segundo, R. (2019). Parkinson\u2019s Disease Detection from Drawing Movements Using Convolutional Neural Networks. Electronics, 8.","DOI":"10.3390\/electronics8080907"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Oudah, M., Al-Naji, A., and Chahl, J. (2021). Elderly Care Based on Hand Gestures Using Kinect Sensor. Computers, 10.","DOI":"10.3390\/computers10010005"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.neunet.2007.12.031","article-title":"Training Neural Network Classifiers for Medical Decision Making: The Effects of Imbalanced Datasets on Classification Performance","volume":"21","author":"Mazurowski","year":"2008","journal-title":"Neural Netw."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ajay, J., Song, C., Wang, A., Langan, J., Li, Z., and Xu, W. (2018, January 4\u20137). A Pervasive and Sensor-Free Deep Learning System for Parkinsonian Gait Analysis. Proceedings of the 2018 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2019), Las Vegas, NV, USA.","DOI":"10.1109\/BHI.2018.8333381"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dentamaro, V., Impedovo, D., and Pirlo, G. (2019, January 9\u201313). Real-Time Neurodegenerative Disease Video Classification with Severity Prediction. Proceedings of the 20th International Conference on Image Analysis and Processing (ICIAP 2019), Trento, Italy.","DOI":"10.1007\/978-3-030-30645-8_56"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1109\/TPAMI.2012.59","article-title":"3D Convolutional Neural Networks for Human Action Recognition","volume":"35","author":"Ji","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., and Comaniciu, D. (2015, January 5\u20139). 3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data. Proceedings of the 2015 IEEE Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), Munich, Germany.","DOI":"10.1007\/978-3-319-24553-9_69"},{"key":"ref_27","unstructured":"Palazzo, S., Spampinato, C., D\u2019Oro, P., Giordano, D., and Shah, M. (2018, January 8\u201314). Generating Synthetic Video Sequences by Explicitly Modeling Object Motion. Proceedings of the 9th International Workshop on Human Behavior Understanding (HBUGEN2018), Munich, Germany."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11042-011-0948-1","article-title":"SHIATSU: Tagging and retrieving videos without worries","volume":"63","author":"Bartolini","year":"2013","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","unstructured":"Soukupov\u00e1, T., and \u010cech, J. (2016, January 3\u20135). Real-Time Eye Blink Detection Using Facial Landmarks. Proceedings of the 21st Computer Vision Winter Workshop (CVWW \u201916), Rimske Toplice, Slovenia."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sukno, F.M., Pavani, S.-K., Butakoff, C., and Frangi, A.F. (2009, January 13\u201315). Automatic Assessment of Eye Blinking Patterns through Statistical Shape Models. Proceedings of the 7th International Conference on Computer Vision Systems (ICVS 2009), Li\u00e8ge, Belgium.","DOI":"10.1007\/978-3-642-04667-4_4"},{"key":"ref_31","unstructured":"Schiffman, H.R. (2001). Sensation and Perception: An Integrated Approach, John Wiley and Sons."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.sleep.2018.07.018","article-title":"Automatic Detection of Cataplexy","volume":"52","author":"Bartolini","year":"2018","journal-title":"Sleep Med."},{"key":"ref_33","unstructured":"Burkert, P., Trier, F., Afzal, M.Z., Dengel, A., and Liwicki, M. (2015). DeXpression: Deep Convolutional Neural Network for Expression Recognition. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sokolova, M., Japkowicz, N., and Szpakowicz, S. (2006, January 4\u20138). Beyond Accuracy, F-score and ROC: A Family of Discriminant Measures for Performance Evaluation. Proceedings of the 19th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI 2006), Hobart, Australia.","DOI":"10.1007\/11941439_114"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC Analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_37","unstructured":"Provost, F. (2000, January 31). Machine Learning from Imbalanced Data Sets 101. Proceedings of the AAAI\u20192000 Workshop on Imbalanced Data Sets, 68(2000), Austin, TX, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust Real-Time Face Detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1136\/jnnp-2015-310864","article-title":"Intermittent Head Drops: The Differential Spectrum","volume":"87","author":"Antelmi","year":"2016","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bartolini, I., Patella, M., and Romani, C. (2010, January 10\u201313). SHIATSU: Semantic-Based Hierarchical Automatic Tagging of Videos by Segmentation using Cuts. Proceedings of the 3rd International Workshop on Automated Information Extraction in Media Production (AIEMPro 10), Firenze, Italy.","DOI":"10.1145\/1877850.1877866"},{"key":"ref_41","unstructured":"Bartolini, I., Patella, M., and Stromei, G. (2011, January 18\u201321). The Windsurf Library for the Efficient Retrieval of Multimedia Hierarchical Data. Proceedings of the International Conference on Signal Processing and Multimedia Applications (SIGMAP 2011), Seville, Spain."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Baltru\u0161aitis, T., Zadeh, A., Yao Chong, L., and Morency, L.-P. (2018, January 15\u201319). OpenFace 2.0: Facial Behavior Analysis Toolkit. Proceedings of the 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00019"},{"key":"ref_43","unstructured":"Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. (2014, January 21\u201326). Decaf: A Deep Convolutional Activation Feature for Generic Visual Recognition. Proceedings of the 31st International Conference on International Conference on Machine Learning (ICML\u201914), Beijing, China."},{"key":"ref_44","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201916), Savannah, GA, USA."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/4\/51\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:47:25Z","timestamp":1760161645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/10\/4\/51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,13]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["computers10040051"],"URL":"https:\/\/doi.org\/10.3390\/computers10040051","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,13]]}}}