Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.

Federated Learning used for predicting outcomes in SARS-COV-2 patients.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.

Motor Unit Potential Jitter: A New Measure of Neuromuscular Transmission Instability.

A new measure of neuromuscular transmission instability, motor unit potential (MUP) jitter, is introduced. MUP jitter can be estimated quickly using MUP trains (MUPTs) extracted from electromyographic (EMG) signals acquired using conventional clinical equipment and needle EMG electrodiagnostic protocols. The primary motivation for developing MUP jitter is to avoid the technical demands associated with estimating jitter using conventional single fiber EMG techniques.

Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition.

This paper proposes a system for hand movement recognition using multichannel electromyographic (EMG) signals obtained from the forearm surface. This system can be used to control prostheses or to provide inputs for a wide range of human computer interface systems. In this work, the hand movement recognition problem is formulated as a multi-class distance based classification of multi-dimensional sequences.

Multifunction myoelectric control using multi-dimensional dynamic time warping.

Myoelectric control can be used for a variety of applications including powered protheses and different human computer interface systems. The aim of this study is to investigate the formulation of myoelectric control as a multi-class distance-based classification of multidimensional sequences. More specifically, we investigate (1) estimation of multi-muscle activation sequences from multi-channel electromyographic signals in an online manner, and (2) classification using a distance metric based on multi-dimensional dynamic time warping.

Detecting neuropathy using measures of motor unit activation extracted from standard concentric needle electromyographic signals.

Objective: Motor unit loss associated with neuropathic disorders affects motor unit activation. Quantitative electromyographic (EMG) features of motor unit activation estimated from the sequences of motor unit potentials (MUPs) created by concurrently active motor units can support the detection of neuropathic disorders. Interpretation of most motor unit activation feature values are, however, confounded by uncertainty regarding the level of muscle activation during EMG signal detection.

Transparent muscle characterization using quantitative electromyography: different binarization mappings.

Evaluation of patients with suspected neuromuscular disorders is typically based on qualitative visual and auditory assessment of needle detected eletromyographic (EMG) signals; the resulting muscle characterization is subjective and highly dependent on the skill and experience of the examiner. Quantitative electromyography (QEMG) techniques were developed to extract motor unit potential trains (MUPTs) from needle detected EMG signals, and estimate features capturing motor unit potential (MUP) morphology and quantifying morphological consistency across MUPs belonging to the same MUPT. The aim of this study is to improve available methods for obtaining transparent muscle characterizations from features obtained using QEMG techniques.

Feature selection for motor unit potential train characterization.

Introduction: Ten new features of motor unit potential (MUP) morphology and stability are proposed. These new features, along with 8 traditional features, are grouped into 5 aspects: size, shape, global complexity, local complexity, and stability.

Methods: We used sequential forward and backward search strategies to select subsets of these 18 features to discriminate accurately between muscles whose MUPs are predominantly neurogenic, myopathic, or normal.