Facial Paralysis Grading Based on Dynamic and Static Features

Peripheral facial nerve palsy, also known as facial paralysis (FP), is a common clinical disease, which requires subjective judgment and scoring based on the FP scale. There exists some automatic facial paralysis grading methods, but the current methods mostly only consider either static or dynamic features, resulting in a low accuracy rate of FP grading. This thesis proposes an automatic facial paralysis assessment method including both static and dynamic characteristics. The first step of the method performs preprocessing on the collected facial expression videos of the subjects, including rough video interception, video stabilization, keyframe extraction, image geometric normalization and gray-scale normalization. Next, the method selects as keyframes no facial expression state and maximum facial expression state in the image data to build the the research data set. Data preprocessing reduces errors, noise, redundancy and even errors in the original data. The basis for extracting static and dynamic features of an image is to use Ensemble of Regression Trees algorithm to determine 68 facial landmarks. Based on landmark points, image regions of image are formed. According to the Horn-Schunck optical flow method, the optical flow information of parts of the face are extracted, and the dynamic characteristics of the optical flow difference between the left and right parts are calculated. Finally, the results of dynamic and static feature classification are weighted and analyzed to obtain FP ratings of subjects. A 32-dimensional static feature is fed into the support vector machine for classification. A 60-dimensional feature vector of dynamical aspects is fed into a long and short-term memory network for classification. Videos of 30 subjects are used to extract 1419 keyframes to test the algorithm. The accuracy, precision, recall and f1 of the best classifier reach 93.33%, 94.29%, 91.33% and 91.87%, respectively.Perifeerinen kasvojen hermohalvaus, joka tunnetaan myös nimellä kasvojen halvaus (FP), on yleinen kliininen sairaus, joka vaatii subjektiivista arviointia ja FP -asteikon pisteytystä. Joitakin automaattisia kasvohalvauksen luokittelumenetelmiä on olemassa, mutta yleensä ottaen ne punnitsevat vain joko staattisia tai dynaamisia piirteitä. Tässä tutkielmassa ehdotetaan automaattista kasvojen halvaantumisen arviointimenetelmää, joka kattaa sekä staattiset että dynaamiset ominaisuudet. Menetelmän ensimmäinen vaihe suorittaa ensin esikäsittelyn kohteiden kerätyille kasvojen ilmevideoille, mukaan lukien karkea videon sieppaus, videon vakautus, avainruudun poiminta, kuvan geometrinen normalisointi ja harmaasävyjen normalisointi. Seuraavaksi menetelmä valitsee avainruuduiksi ilmeettömän tilan ja kasvojen ilmeiden maksimitilan kuvadatasta kerryttäen tutkimuksen data-aineiston. Tietojen esikäsittely vähentää virheitä, kohinaa, redundanssia ja jopa virheitä alkuperäisestä datasta. Kuvan staattisten ja dynaamisten piirteiden poimimisen perusta on käyttää Ensemble of Regression Trees -algoritmia 68 kasvojen merkkipisteiden määrittämiseen. Merkkipisteiden perusteella määritellään kuvan kiinnostavat alueet. Horn-Schunckin optisen virtausmenetelmän mukaisesti poimitaan optisen virtauksen tiedot joistakin kasvojen osista, ja dynaaminen luonnehdinta lasketaan vasempien ja oikeiden osien välille. Lopuksi dynaamisen ja staattisen piirteiden luokittelun tulokset painotetaan ja analysoidaan kattavasti koehenkilöiden FP-luokitusten saamiseksi. 32- ulotteinen staattisten piirteiden vektori syötetään tukivektorikoneeseen luokittelua varten. 60-ulotteinen dynaamisten piirteiden ominaisuusvektori syötetään pitkän ja lyhyen aikavälin muistiverkkoon luokittelua varten. Parhaan luokittelijan tarkkuus, täsmällisyys, palautustaso ja f1 saavuttavat arvot 93,33%, 94,29%, 91,33% ja 91,87%

Remote Outcome Tracking after Facial Reanimation Surgery

Introduction Facial reanimation surgery encompasses several different surgical approaches to facial palsy (FP). Goals of surgery are dependent on patient priorities and disease related loss of function. A major frustration in this field is the lack of a universal outcome measure that can aid clinicians in gaining a clear understanding of the true benefits of surgery. Advancements in computer vision technology may provide clinicians with an accurate and automated outcome assessment tool. Aims This thesis has two main goals. Firstly, to describe current and proposed future methods of facial palsy assessment, focusing on newly implemented automated methods. The second goal is to detail and validate the development of a novel outcome measurement tool designed to provide an automated and objective assessment of smile in FP patients. Methods The first chapter of this thesis will involve a narrative review detailing the current literature available for outcome measurement in facial palsy. The second chapter validates a newly developed mobile phone app ‘SmileCheck’ by comparing app-based smile analysis with clinician observation in FP patients. A pilot trial was undertaken prior to the second study to validate quality assurance within the application by determining optimal usage environment

Bionic Lid Implant for Natural Closure (BLINC)

Facial nerve palsy (FNP) leads to an inability to blink. The exposed eye is at risk of developing corneal keratopathy and currently there is a lack of solution to active eye closure that is immediate and reliable. Bionic Lid Implant for Natural Closure (BLINC) proposes the use of an implantable actuator combined with the effects of an eyelid sling for dynamic eye closure. The aims of this thesis are to 1) explore the clinical need for BLINC, 2) describe the BLINC technology, and 3) present the results of its application in cadaveric and live models. Methods The aims of this project are addressed in three parts. In part one, the current therapies addressing key clinical end points in FNP from an ocular perspective and the setting where BLINC may first be used are explored. In part two the science behind BLINC is outlined. Finally in part three application of BLINC in cadaveric and live models are studied followed by a discussion on future steps preceding a pilot study in humans. Results Patients with FNP consistently identify issues related to the eye a primary concern. Current reanimation strategies offer the possibility of dynamic eye closure but the results are delayed and often unpredictable. BLINC reliably achieves active eye closure in cadaveric models by means of a wireless-powered, implantable electromagnetic actuator in conjunction with an eyelid sling. BLINC closes the eye in a similar fashion to natural closure for a symmetrical blink in FNP. Successful application of an inactive device in its complete form is achieved in a live animal without significant morbidity. Conclusion BLINC offers the possibility of restoring active eye closure with use of an implantable actuator. The concept has been successfully demonstrated in cadaveric models with successful device implantation in a live model. Future live trials are needed to address the remaining biocompatibility issues in preparation for human application

Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments

Ocular Parasitic Infections – An Overview

Eyes are said to be the windows of body, by which this beautiful world is visualized. Human eye has a unique structure and is vulnerable to numerous infections. Whenever anatomical structures are breached, host defenses come into play, but if infection is severe and not treated timely, it could lead to visual impairment or blindness. Parasitic infections are considered, the significant causes of ophthalmic diseases worldwide. In this chapter, an overview of ocular parasitic infections (OPI) is detailed out, with an initial brief introduction followed by description of anatomy of the human eye and various defense mechanisms to provide better understanding of the parasitic infections affecting different parts of human eye. The last part includes individual details of various human ocular parasitic infections

Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference