Gotowe bibliografie tematyczne / Hypomimia

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Gotowa bibliografia na temat „Hypomimia”

Autor: Grafiati

Data publikacji: 12 kwietnia 2025

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Artykuły w czasopismach na temat "Hypomimia"

Khomchenkova, A. A., i S. V. Prokopenko. "Hypomimia and Methods of Its Diagnostics in Patients with Parkinson’s Disease". Doctor.Ru 20, nr 5 (2021): 39–42. http://dx.doi.org/10.31550/1727-2378-2021-20-5-39-42.

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Objective of the Review: To present information of hypomimia manifestations and methods of its diagnostics in patients with Parkinson’s disease (PD) used in scientific trials and clinical practice. Key Points. One of the most significant manifestations of PD is hypomimia. Most commonly, this syndrome is evaluated using the unified PD rating scale which allows analysing patient’s hypomimia subjectively. There are studies which are based on objective methods for computeraided hypomimia diagnostics. Conclusion. To the date, there is insufficient information regarding objective characteristics of hypomimia. The issue of interrelation between hypomimia and generalised hypokinesia and possible cross-correction of these syndromes remains unclear. It is necessary to develop an objective, affordable and simple method for facial expression assessment to be used in routine neurological practice. Keywords: Parkinson’s disease, hypokinesia, mimics, hypomimia, videoanalysis.

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Su, Ge, Bo Lin, Wei Luo, Jianwei Yin, Shuiguang Deng, Honghao Gao i Renjun Xu. "Hypomimia Recognition in Parkinson’s Disease With Semantic Features". ACM Transactions on Multimedia Computing, Communications, and Applications 17, nr 3s (31.10.2021): 1–20. http://dx.doi.org/10.1145/3476778.

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Parkinson’s disease is the second most common neurodegenerative disorder, commonly affecting elderly people over the age of 65. As the cardinal manifestation, hypomimia, referred to as impairments in normal facial expressions, stays covert. Even some experienced doctors may miss these subtle changes, especially in a mild stage of this disease. The existing methods for hypomimia recognition are mainly dominated by statistical variable-based methods with the help of traditional machine learning algorithms. Despite the success of recognizing hypomimia, they show a limited accuracy and lack the capability of performing semantic analysis. Therefore, developing a computer-aided diagnostic method for semantically recognizing hypomimia is appealing. In this article, we propose a Semantic Feature based Hypomimia Recognition network , named SFHR-NET , to recognize hypomimia based on facial videos. First, a Semantic Feature Classifier (SF-C) is proposed to adaptively adjust feature maps salient to hypomimia, which leads the encoder and classifier to focus more on areas of hypomimia-interest. In SF-C, the progressive confidence strategy (PCS) ensures more reliable semantic features. Then, a two-stream framework is introduced to fuse the spatial data stream and temporal optical stream, which allows the encoder to semantically and progressively characterize the rigid process of hypomimia. Finally, to improve the interpretability of the model, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to generate attention maps that cast our engineered features into hypomimia-interest regions. These highlighted regions provide visual explanations for decisions of our network. Experimental results based on real-world data demonstrate the effectiveness of our method in detecting hypomimia.

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Khomchenkova, Aleksandra A., Semyon V. Prokopenko i Saikal B. Ismailova. "Clinical aspects of hypomimia in Parkinson’s disease". Neurology Bulletin LIV, nr 1 (11.04.2022): 45–53. http://dx.doi.org/10.17816/nb89531.

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BACKGROUND. Hypomimia is a distinct symptom of Parkinsons disease manifested by reduced facial expression and slowness of movement in mimic muscles. Clinical aspects of this symptom remain underinvestigated to the date. AIM. Based on correlation analysis, to determine the association between hypomimia, general manifestations of hypokinesia and the quality of life in patients with Parkinsons disease as well as to evaluate the influence of therapy with L-DOPA pharmaceuticals on hypomimia. MATERIAL AND METHODS. Clinical aspects of hypomimia were studied in 30 patients with stage 1.53.0 PD according to Hoehn and Yahr Rating Scale. The mean age of the patients was 69. Analysis of hypomimia was carried out using a proprietary method of computer video-analysis of facial muscle movement in performance of 6 tests: smiling, drawing of the letter O, winking, eyebrow raising, frowning and text reading. The gait function was assessed using the laser analyser of kinematic parameters of gait. Mean step length, step time and gait velocity were evaluated. Manifestations of general hypokinesia were analysed via the Unified Parkinsons Disease Rating Scale licensed by the International Parkinson and Movement Disorder Society. Statistical processing of the data was performed using the IBM SPSS Statistics 23 software package. The Wilcoxon test and the Spearmans correlation coefficient were used. The statistical significance level was set at p 0.05. RESULTS. The correlation between the degree of hypomimia and the severity of general hypokinesia has been confirmed, which determines the prospect in objective analysis of mimics as a possible screening test for general hypokinesia. We have obtained data confirming the association between hypomimia and general hypokinesia: in particular, step time, step length and gait velocity. CONCLUSION. While using the proprietary method of objective hypomimia diagnosis, we have revealed the presence of correlation between manifestations of hypomimia and general hypokinesia. Additionally, manifestations of hypomimia have been found to decrease against the background of dopaminergic therapy.

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Prokopenko, S. V., A. A. Khomchenkova, V. A. Gurevich, N. A. Butenko, V. A. Kontorin i A. V. Spirin. "An Objective Method for Assessment of Facial Expression in Patients with Parkinson’s Disease and Healthy Population". Medical University 3, nr 4 (1.12.2020): 151–54. http://dx.doi.org/10.2478/medu-2020-0018.

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Abstract Hypomimia is a common symptom of Parkinson’s disease. At the present time, issues related to existence of interrelations between hypomimia and manifestations of common hypokinesia remain under-investigated in aspects of both clinical manifestations and rehabilitation efficacy. An objective method for facial muscle movement diagnostics is necessary to achieve this goal. The article presents novel experience in application of a proprietary method using objective facial expression assessment video analysis on the example of a healthy female subject and a female patient with Parkinson’s disease. The Parkinson’s female patient had objective symptoms of hypomimia: a decrease in velocity and amplitude of eyebrow and mouth movement, slow winking. Therefore, application of this method creates prerequisites for more in-depth study of theoretical and clinical aspects in facial expression of Parkinson’s disease patients.

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Bianchini, Edoardo, Domiziana Rinaldi, Marika Alborghetti, Marta Simonelli, Flavia D’Audino, Camilla Onelli, Elena Pegolo i Francesco E. Pontieri. "The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson’s Disease". Brain Sciences 14, nr 1 (22.01.2024): 109. http://dx.doi.org/10.3390/brainsci14010109.

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Facial movements are crucial for social and emotional interaction and well-being. Reduced facial expressions (i.e., hypomimia) is a common feature in patients with Parkinson’s disease (PD) and previous studies linked this manifestation to both motor symptoms of the disease and altered emotion recognition and processing. Nevertheless, research on facial motor impairment in PD has been rather scarce and only a limited number of clinical evaluation tools are available, often suffering from poor validation processes and high inter- and intra-rater variability. In recent years, the availability of technology-enhanced quantification methods of facial movements, such as automated video analysis and machine learning application, led to increasing interest in studying hypomimia in PD. In this narrative review, we summarize the current knowledge on pathophysiological hypotheses at the basis of hypomimia in PD, with particular focus on the association between reduced facial expressions and emotional processing and analyze the current evaluation tools and management strategies for this symptom, as well as future research perspectives.

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Khomchenkova, A. A., S. V. Prokopenko, V. A. Gurevich i P. V. Peresunko. "Diagnosis of hypomimia in Parkinson’s disease". Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova 122, nr 11 (2022): 24. http://dx.doi.org/10.17116/jnevro202212211224.

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Khomchenkova, A. A., S. V. Prokopenko, S. B. Ismailova, Yu N. Ashikhmina i E. S. Denisova. "Correction of Hypomimia Through Activation of Gait Function in Patients with Parkinson`s Disease". Doctor.Ru 22, nr 6 (2023): 78–82. http://dx.doi.org/10.31550/1727-2378-2023-22-6-78-82.

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Aim: To assess the influence of gait function activation on manifestations of hypomimia in patients with Parkinson's disease (PD). Design: A comparative clinical study. Materials and methods. The study enrolled 18 patients with stage of 2–3 PD according to Hoehn — Yahr Rating Scale aged 70 [65; 76] years. Analysis of facial expression in PD patients was analysed by using a proprietary method of computer video-analysis of facial muscle movement. Activation of the gait function used another proprietary method: the apparatus for functional foot therapy that mechanically activates the rear push of the foot at the moment of the lift of the foot from the supporting surface. Results. Activation of the gait function has statistically significantly improved the mimic function of the face, in particular the blink frequency (p = 0.022), eyebrow movement amplitude along the Y axis in the «brow furrow» test (p = 0.048) and the amplitude of mouth movement while reading a text along the axis X (p = 0.012). Conclusion. The confirmation of the decrease of hypomimia manifestations through affecting the condition of general hypokinesia creates the prerequisites for appearance of rehabilitation methods making it possible to perform simultaneously correction of these two symptoms. Keywords: Parkinson’s disease, hypomimia, hypokinesia, gait function activation.

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Pegolo, Elena, Daniele Volpe, Alberto Cucca, Lucia Ricciardi i Zimi Sawacha. "Quantitative Evaluation of Hypomimia in Parkinson’s Disease: A Face Tracking Approach". Sensors 22, nr 4 (10.02.2022): 1358. http://dx.doi.org/10.3390/s22041358.

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Parkinson’s disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients’ quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric.

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Ricciardi, L., A. De Angelis, L. Marsili, I. Faiman, P. Pradhan, E. A. Pereira, M. J. Edwards, F. Morgante i M. Bologna. "Hypomimia in Parkinson’s disease: an axial sign responsive to levodopa". European Journal of Neurology 27, nr 12 (20.08.2020): 2422–29. http://dx.doi.org/10.1111/ene.14452.

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Dumer, Aleksey I., Harriet Oster, David McCabe, Laura A. Rabin, Jennifer L. Spielman, Lorraine O. Ramig i Joan C. Borod. "Effects of the Lee Silverman Voice Treatment (LSVT® LOUD) on Hypomimia in Parkinson's Disease". Journal of the International Neuropsychological Society 20, nr 3 (13.02.2014): 302–12. http://dx.doi.org/10.1017/s1355617714000046.

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AbstractGiven associations between facial movement and voice, the potential of the Lee Silverman Voice Treatment (LSVT) to alleviate decreased facial expressivity, termed hypomimia, in Parkinson's disease (PD) was examined. Fifty-six participants—16 PD participants who underwent LSVT, 12 PD participants who underwent articulation treatment (ARTIC), 17 untreated PD participants, and 11 controls without PD—produced monologues about happy emotional experiences at pre- and post-treatment timepoints (“T1” and “T2,” respectively), 1 month apart. The groups of LSVT, ARTIC, and untreated PD participants were matched on demographic and health status variables. The frequency and variability of facial expressions (Frequency and Variability) observable on 1-min monologue videorecordings were measured using the Facial Action Coding System (FACS). At T1, the Frequency and Variability of participants with PD were significantly lower than those of controls. Frequency and Variability increases of LSVT participants from T1 to T2 were significantly greater than those of ARTIC or untreated participants. Whereas the Frequency and Variability of ARTIC participants at T2 were significantly lower than those of controls, LSVT participants did not significantly differ from controls on these variables at T2. The implications of these findings, which suggest that LSVT reduces parkinsonian hypomimia, for PD-related psychosocial problems are considered. (JINS, 2014, 20, 1–11)

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Rozprawy doktorskie na temat "Hypomimia"

Filali, razzouki Anas. "Deep learning-based video face-based digital markers for early detection and analysis of Parkinson disease". Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAS002.

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Cette thèse vise à développer des biomarqueurs numériques robustes pour la détection précoce de la maladie de Parkinson (MP) en analysant des vidéos faciales afin d'identifier les changements associés à l'hypomimie. Dans ce contexte, nous introduisons de nouvelles contributions à l'état de l'art : l'une fondée sur l'apprentissage automatique superficiel et l'autre fondée sur l'apprentissage profond. La première méthode utilise des modèles d'apprentissage automatique qui exploitent des caractéristiques faciales extraites manuellement, en particulier les dérivés des unités d'action faciale (AUs). Ces modèles intègrent des mécanismes d'interprétabilité qui permettent d'expliquer leur processus de décision auprès des parties prenantes, mettant en évidence les caractéristiques faciales les plus distinctives pour la MP. Nous examinons l'influence du sexe biologique sur ces biomarqueurs numériques, les comparons aux données de neuroimagerie et aux scores cliniques, et les utilisons pour prédire la gravité de la MP. La deuxième méthode exploite l'apprentissage profond pour extraire automatiquement des caractéristiques à partir de vidéos faciales brutes et des données de flux optique en utilisant des modèles fondamentaux basés sur les Vision Transformers pour vidéos. Pour pallier le manque de données d'entraînement, nous proposons des techniques avancées d'apprentissage par transfert adaptatif, en utilisant des modèles fondamentaux entraînés sur de grands ensembles de données pour la classification de vidéos. De plus, nous intégrons des mécanismes d'interprétabilité pour établir la relation entre les caractéristiques extraites automatiquement et les AUs faciales extraites manuellement, améliorant ainsi la clarté des décisions des modèles. Enfin, nos caractéristiques faciales générées proviennent à la fois de données transversales et longitudinales, ce qui offre un avantage significatif par rapport aux travaux existants. Nous utilisons ces enregistrements pour analyser la progression de l'hypomimie au fil du temps avec ces marqueurs numériques, et sa corrélation avec la progression des scores cliniques. La combinaison des deux approches proposées permet d'obtenir une AUC (Area Under the Curve) de classification de plus de 90%, démontrant l'efficacité des modèles d'apprentissage automatique et d'apprentissage profond dans la détection de l'hypomimie chez les patients atteints de MP à un stade précoce via des vidéos faciales. Cette recherche pourrait permettre une surveillance continue de l'hypomimie en dehors des environnements hospitaliers via la télémédecine
This thesis aims to develop robust digital biomarkers for early detection of Parkinson's disease (PD) by analyzing facial videos to identify changes associated with hypomimia. In this context, we introduce new contributions to the state of the art: one based on shallow machine learning and the other on deep learning.The first method employs machine learning models that use manually extracted facial features, particularly derivatives of facial action units (AUs). These models incorporate interpretability mechanisms that explain their decision-making process for stakeholders, highlighting the most distinctive facial features for PD. We examine the influence of biological sex on these digital biomarkers, compare them against neuroimaging data and clinical scores, and use them to predict PD severity.The second method leverages deep learning to automatically extract features from raw facial videos and optical flow using foundational models based on Video Vision Transformers. To address the limited training data, we propose advanced adaptive transfer learning techniques, utilizing foundational models trained on large-scale video classification datasets. Additionally, we integrate interpretability mechanisms to clarify the relationship between automatically extracted features and manually extracted facial AUs, enhancing the comprehensibility of the model's decisions.Finally, our generated facial features are derived from both cross-sectional and longitudinal data, which provides a significant advantage over existing work. We use these recordings to analyze the progression of hypomimia over time with these digital markers, and its correlation with the progression of clinical scores.Combining these two approaches allows for a classification AUC (Area Under the Curve) of over 90%, demonstrating the efficacy of machine learning and deep learning models in detecting hypomimia in early-stage PD patients through facial videos. This research could enable continuous monitoring of hypomimia outside hospital settings via telemedicine

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Części książek na temat "Hypomimia"

Vinokurov, Nomi, David Arkadir, Eduard Linetsky, Hagai Bergman i Daphna Weinshall. "Quantifying Hypomimia in Parkinson Patients Using a Depth Camera". W Communications in Computer and Information Science, 63–71. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32270-4_7.

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Mehta, Gautam, i Bilal Iqbal. "Central Nervous System". W Clinical Medicine for the MRCP PACES. Oxford University Press, 2010. http://dx.doi.org/10.1093/oso/9780199542550.003.0011.

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As with all neurological patients, you will be more likely to pick up the diagnosis if you take a step back and look at the whole patient. Take some time to assess their facial expressions, speech, tremor, and posture. A common instruction at this station, with the patient seated on a chair is ‘Look at this patient, and examine as appropriate’. Candidates are often baffled, when given this instruction. Often the patients with Parkinson’s disease are given specific instructions to interlock the fingers of both hands, or place hands flat on their lap to mask the tremor. Picking up an expressionless face and low volume monotonous speech from the outset will provide useful clues to the diagnosis. If you are not sure at this stage, proceed to examining the gait. Once you are certain, that this is Parkinson’s disease, you may proceed to demonstrate the other features. 1. Patients with Parkinson’s disease have characteristic expressionless facies (hypomimia), often described as ‘mask-like’. This is a manifestation of bradykinesia. There is a reduced blink rate. The glabellar tap (Myerson’s sign) is an unreliable sign and is not recommended in the examination. This involves tapping the patient’s forehead repeatedly. Normal subjects will stop blinking, but in Parkinson’s disease, the patient will continue to blink. The patient may be drooling saliva (resulting from dysphagia and sialorrhoea-due to autonomic dysfunction) 2. Patients may have soft speech (hypophonia). This is also a manifestation of bradykinesia, and characteristically, the speech is low-volume, monotonous and tremulous (appears slurred). 3. Blepharoclonus is tremor of the eyelids. This will only be demonstrated if the eyes are gently closed, as opposed to tightly closing the eyes. 4. The classic tremor is present at rest and asymmetrical (more marked on one side). It is classically described as being 4–6Hz and is the initial symptom in 60% of cases, although 20% of patients never have a tremor. The tremor may appear as a ‘pill-rolling’ motion of the hand or a simple oscillation of the hand or arm. It is easier to spot a tremor if you ask the patient to rest their arms in their lap in the semi-prone position.

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Streszczenia konferencji na temat "Hypomimia"

Grammatikopoulou, Athina, Nikos Grammalidis, Sevasti Bostantjopoulou i Zoe Katsarou. "Detecting hypomimia symptoms by selfie photo analysis". W PETRA '19: The 12th PErvasive Technologies Related to Assistive Environments Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3316782.3322756.

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Xu, Zhouxiang, Dongxu Lv, Haoyu Li, Hong Li i Hebei Gao. "Application of ResLSTM in Hypomimia Video Detection for Parkinson's Disease". W 2023 International Conference on New Trends in Computational Intelligence (NTCI). IEEE, 2023. http://dx.doi.org/10.1109/ntci60157.2023.10403741.

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Rajnoha, Martin, Jiri Mekyska, Radim Burget, Ilona Eliasova, Milena Kostalova i Irena Rektorova. "Towards Identification of Hypomimia in Parkinson's Disease Based on Face Recognition Methods". W 2018 10th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT). IEEE, 2018. http://dx.doi.org/10.1109/icumt.2018.8631249.

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Athayde, Natália Merten, Wladimir Bocca Vieira de Rezende Pinto, Paulo Victor Sgobbi de Souza, Acary Souza Bulle Oliveira i Alzira Alves de Siqueira Carvalho. "Expansion of the phenotype in ALS19". W XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.455.

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Context: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects the upper and lower motor neurons. Most cases are sporadic, being 5-10% familial. Currently, more than 20 genes are described as causing familial ALS, with C9ORF72 and SOD1 the most common. Case report: Woman, 42 years old, with progressive weakness in her left foot for 3 years evolving with difficulty walking. No family history. Neurological exam(NE): asymmetric stepagge gait. Asymmetric proximal, distal and axial muscle weakness and distal atrophy. Hyperactive reflexes. EMG: pre-ganglionic lesion in lower limbs with active denervation. Brain MRI: high signal intensity on left corticospinal tract. ALS genetic panel: c.3878G> A, heterozygois in ErbB4(OMIM* 600543). CASE 2: Female, 55 years old, with parkinsonism for 2 years, evolving with muscle weakness, myalgia, dysphonia and dysphagia. After 7 months, respiratory failure and death. Family history: ALS and atypical parkinsonism. NE: Global amyotrophy, facial hypomimia, dropped head, fasciculations on the tongue. Bradykinesia. Plastic hypertonia in the 4 limbs. Proximal and distal weakness. Babinski sign on the right. Oculomotor apraxia. Dysarthrophonia. EMG: pre-ganglionic lesion in the 4 limbs. Brain MRI: global cortical atrophy with temporal predominance. Exome: pathogenic variant, in heterozygosis c. 2428G>A in ErbB4. Conclusion: There are only 3 cases reported in the literature associated to pathogenic variants in this gene. We suggest an expansion of the clinical phenotype for ALS19.

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Valenzuela, Brayan, Jhon Arevalo, William Contreras i Fabio Martinez. "A Spatio-Temporal Hypomimic Deep Descriptor to Discriminate Parkinsonian Patients". W 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2022. http://dx.doi.org/10.1109/embc48229.2022.9871753.

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