Valoración de la vía aérea por endoscopía respiratoria en el paciente pediátrico intubado

Estenosis postintubación: Se trata de una lesión traumática por el tubo endotraqueal. Globalmente; el 3 a 7% de los pacientes internados en Unidad de Cuidados Intensivos Pediátricos puede desarrollar estenosis subglótica. En los pacientes intubados, la acción simultánea de distintos factores puede generar lesión laríngea. Estos factores son: - Propios del paciente: tamaño de la laringe, edad, peso, lesiones previas, infecciones, hipotensión arterial, reflujo gastroesofágico, movilidad. - Propios de la Internación: tamaño y calidad del tubo endotraqueal, tiempo de intubación, maniobras reiteradas de intubación, traumatismos, episodios de reintubaciones durante la estadía en UTIP

Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses

Abstract Background For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses.</p

Using High Density EMG to Proportionally Control 3D Model of Human Hand

Control of human hand using surface electromyography (EMG) is already established in various mechanisms, but proportionally controlling magnitudes degrees of freedom (DOF) of humanoid hand model is still highly developed in recent years. This paper proposes another method to achieve a proportional estimation and control of human’s hand multiple DOFs. Gestures in the form of American Sign Language (ABCDFIKLOW) were chosen as the targets, of which ten alphabetical gestures were specifically used following their clarity on its 3D model. Then the dataset of the movements gestures was simultaneously recorded using High-density electromyography (HD-EMG) and motion capture system. Sensor placements were on intrinsic - extrinsic muscles for HD-EMG and finger joints for the motion capture system. To derive the proportional control in time series between both datasets (HD-EMG and kinematics data), neural network (NN) and k-Nearest Neighbour were used. The models produced around 70-95 % (R index) accuracy for the eleven DOFs in four healthy subjects’ hand. kNN’s performance was better than NN, even if the input features were reduced either using manual selections or principal component analysis (PCA). The time series controls could also identify most sign language gestures (9 of 10), with difficulty was given on O gesture. The false interpretation was because of nearly identical muscle’s EMG and kinematics data between O and C. This paper intends to extend its conference version [1] by adding more in-depth Results and Discussion along making other sections more comprehensive

Miniaturized magnetic sensors for implantable magnetomyography

Magnetism‐based systems are widely utilized for sensing and imaging biological phenomena, for example, the activity of the brain and the heart. Magnetomyography (MMG) is the study of muscle function through the inquiry of the magnetic signal that a muscle generates when contracted. Within the last few decades, extensive effort has been invested to identify, characterize and quantify the magnetomyogram signals. However, it is still far from a miniaturized, sensitive, inexpensive and low‐power MMG sensor. Herein, the state‐of‐the‐art magnetic sensing technologies that have the potential to realize a low‐profile implantable MMG sensor are described. The technical challenges associated with the detection of the MMG signals, including the magnetic field of the Earth and movement artifacts are also discussed. Then, the development of efficient magnetic technologies, which enable sensing pico‐Tesla signals, is advocated to revitalize the MMG technique. To conclude, spintronic‐based magnetoresistive sensing can be an appropriate technology for miniaturized wearable and implantable MMG systems

Common synaptic input, synergies and size principle: Control of spinal motor neurons for movement generation

Understanding how movement is controlled by the CNS remains a major challenge, with ongoing debate about basic features underlying this control. In current established views, the concepts of motor neuron recruitment order, common synaptic input to motor neurons and muscle synergies are usually addressed separately and therefore seen as independent features of motor control. In this review, we analyse the body of literature in a broader perspective and we identify a unified approach to explain apparently divergent observations at different scales of motor control. Specifically, we propose a new conceptual framework of the neural control of movement, which merges the concept of common input to motor neurons and modular control, together with the constraints imposed by recruitment order. This framework is based on the following assumptions: (1) motor neurons are grouped into functional groups (clusters) based on the common inputs they receive; (2) clusters may significantly differ from the classical definition of motor neuron pools, such that they may span across muscles and/or involve only a portion of a muscle; (3) clusters represent functional modules used by the CNS to reduce the dimensionality of the control; and (4) selective volitional control of single motor neurons within a cluster receiving common inputs cannot be achieved. Here, we discuss this framework and its underlying theoretical and experimental evidence

Estimation of Phantom Arm Mechanics About Four Degrees of Freedom After Targeted Muscle Reinnervation

The intuitive control of bionic arms requires estimation of amputee's phantom arm movements from residual muscle bio-electric signals. The functional use of myoelectric arms relies on the ability of controlling large sets of degrees of freedom (>3 DOFs) spanning elbow, forearm, and wrist joints. This would assure optimal hand orientation in any environment. As part of this paper we recorded high-density electromyograms with >190 electrodes from the residual stump of a trans-humeral amputee who underwent targeted muscle reinnervation. We employed clustering to determine eight spatially separated sub-sets of channels sampling electromyograms associated to the actuation of four phantom arm DOFs. We created a large-scale musculoskeletal model of the phantom arm encompassing 33 musculo-tendon units. For the first time, this enabled the accurate electromyography-driven simulation of complex phantom joint rotations about elbow flexion-extension, forearm pronation-supination, wrist flexion-extension, and radial-ulnar deviation. These results support the potential for a new class of bionic limbs that are controlled as natural extensions of the body, an important step toward next-generation prosthetics that mimic human biological functionality and robustness