Аnalysis and anthropometric study of port placement in robotic rectal cancer surgery

Robotic surgery is an innovative, minimally invasive technique, which has already proved its advantages in the operative-technical field by providing ergonomics, three dimensional (3D) visualization of the operative field, more precise dissection in narrow spaces, etc. The additional time needed for docking of the console and collision (internal and external) between the robotic hands is a part of the specific difficulties related to this type of surgery. The aim of this study was to analyze the position of ports and their efficiency in robotic rectal surgery based on our initial experience with this type of surgery in the University Hospital of Pleven- Bulgaria as well as to seek for additional anatomical landmarks to improve the work process. The initial experience with robotic rectal resections for rectal cancer revealed that the recommended distance of 8 cm between the ports doesn`t provide sufficient efficacy. Additional topography anatomical landmarks are required for personalized preoperative planning of port positions and enhanced effectiveness of the robotic system in rectal cancer treatment. Further studies in this field are necessary

Triggered optical coherence tomography for capturing rapid periodic motion

Quantitative cross-sectional imaging of vocal folds during phonation is potentially useful for diagnosis and treatments of laryngeal disorders. Optical coherence tomography (OCT) is a powerful technique, but its relatively low frame rates makes it challenging to visualize rapidly vibrating tissues. Here, we demonstrate a novel method based on triggered laser scanning to capture 4-dimensional (4D) images of samples in motu at audio frequencies over 100 Hz. As proof-of-concept experiments, we applied this technique to imaging the oscillations of biopolymer gels on acoustic vibrators and aerodynamically driven vibrations of the vocal fold in an ex vivo calf larynx model. Our results suggest that triggered 4D OCT may be useful in understanding and assessing the function of vocal folds and developing novel treatments in research and clinical settings

Assessment of vocal cord nodules: A case study in speech processing by using Hilbert-Huang Transform

Vocal cord nodules represent a pathological condition for which the growth of unnatural masses on vocal folds affects the patients. Among other effects, changes in the vocal cords' overall mass and stiffness alter their vibratory behaviour, thus changing the vocal emission generated by them. This causes dysphonia, i.e. abnormalities in the patients' voice, which can be analysed and inspected via audio signals. However, the evaluation of voice condition through speech processing is not a trivial task, as standard methods based on the Fourier Transform, fail to fit the non-stationary nature of vocal signals. In this study, four audio tracks, provided by a volunteer patient, whose vocal fold nodules have been surgically removed, were analysed using a relatively new technique: the Hilbert-Huang Transform (HHT) via Empirical Mode Decomposition (EMD); specifically, by using the CEEMDAN (Complete Ensemble EMD with Adaptive Noise) algorithm. This method has been applied here to speech signals, which were recorded before removal surgery and during convalescence, to investigate specific trends. Possibilities offered by the HHT are exposed, but also some limitations of decomposing the signals into so-called intrinsic mode functions (IMFs) are highlighted. The results of these preliminary studies are intended to be a basis for the development of new viable alternatives to the softwares currently used for the analysis and evaluation of pathological voice

Detection of severe obstructive sleep apnea through voice analysis

tThis paper deals with the potential and limitations of using voice and speech processing to detect Obstruc-tive Sleep Apnea (OSA). An extensive body of voice features has been extracted from patients whopresent various degrees of OSA as well as healthy controls. We analyse the utility of a reduced set offeatures for detecting OSA. We apply various feature selection and reduction schemes (statistical rank-ing, Genetic Algorithms, PCA, LDA) and compare various classifiers (Bayesian Classifiers, kNN, SupportVector Machines, neural networks, Adaboost). S-fold crossvalidation performed on 248 subjects showsthat in the extreme cases (that is, 127 controls and 121 patients with severe OSA) voice alone is able todiscriminate quite well between the presence and absence of OSA. However, this is not the case withmild OSA and healthy snoring patients where voice seems to play a secondary role. We found that thebest classification schemes are achieved using a Genetic Algorithm for feature selection/reduction

Fundamental frequency estimation of low-quality electroglottographic signals

Fundamental frequency (fo) is often estimated based on electroglottographic (EGG) signals. Due to the nature of the method, the quality of EGG signals may be impaired by certain features like amplitude or baseline drifts, mains hum or noise. The potential adverse effects of these factors on fo estimation has to date not been investigated. Here, the performance of thirteen algorithms for estimating fo was tested, based on 147 synthesized EGG signals with varying degrees of signal quality deterioration. Algorithm performance was assessed through the standard deviation σfo of the difference between known and estimated fo data, expressed in octaves. With very few exceptions, simulated mains hum, and amplitude and baseline drifts did not influence fo results, even though some algorithms consistently outperformed others. When increasing either cycle-to-cycle fo variation or the degree of subharmonics, the SIGMA algorithm had the best performance (max. σfo = 0.04). That algorithm was however more easily disturbed by typical EGG equipment noise, whereas the NDF and Praat's auto-correlation algorithms performed best in this category (σfo = 0.01). These results suggest that the algorithm for fo estimation of EGG signals needs to be selected specifically for each particular data set. Overall, estimated fo data should be interpreted with care

Objective diagnosis of laryngeal pathology using the Wigner-Ville distribution

Cette conférence présente une nouvelle possibilité d'application de la distribution de Wigner-Ville (WVD), comme un moyen de traitement numérique du signal vocal. La méthode décrite est assignée de donner des évaluations quantitatives de certains paramètres, utilisés dans la pratique médicale. Elle comprend deux niveaux de traitement principaux: analyse en domaine temporel et analyse WVD temporelle-fréquencielle. L'analyse en domaine temporel représente une suite adaptive de mesurages de la fréquence principale momentanée Fo(i), précédée d'une estimation autocorrélative des limites de la fréquence principale de la voix et de sa régularité. Les paramètres temporels suivants sont déterminés: degré de nonvocalisation, degré de subharmoniques, degré d'interruptions et degré de perturbations de la fréquence principale et de l'amplitude des impulsions du signal. La version de la WVD appliquée (OTSWVD) est optimisée par un polissage temporel, synchronie aux variations temporelles de F(i)o. La valeur moyenne du degré de raucité (DHvv) du spectre d'OTSWVD est évalue. Elle diffère de celle du degré de raucité (DHsp), extracté de la spectrogramme conventionelle, par une sensibilité augmentée en pathologie, grace aux interférences nonpolissées en cas de nonstationarité de la voix. Finalement, des résultats comparatifs des DHsp et DHvv sont présentés

Visual-based system for tracking of mobile robots with support of infra-red sensor

International audienceThe platooning technique for means of transport uses different kind of sensors for longitudinal and lateral control. This paper describes an approach for tracking of transport means in a platooning system which calculates the distance between preceding and following vehicles using only a single digital camera with support of infra-red range finder. The key features defining the distance between two vehicles are obtained by image processing. The calculated distances by the two sensor channels are synchronized in time and fused with a weighted average algorithm. The results show that the relation between key features and distance is nonlinear and the data from different sources are reliably fused in a short computation time. This fused value is suitable for real time longitudinal control of slow speed industrial mobile robots