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Found 6 papers, 1 papers with code
Automated Classification of General Movements in Infants Using a Two-stream Spatiotemporal Fusion Network
In this study, we propose an automated GMs classification method, which consists of preprocessing networks that remove unnecessary background information from GMs videos and adjust the infant's body position, and a subsequent motion classification network based on a two-stream structure.
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection
The covariance matrix of the Gaussian distribution is weighted with a latent scale parameter, which is also a random variable, resulting in the stochastic fluctuations of covariances.
EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models
These results indicated the validity of the proposed method and its applicability to EMG-based control systems.
Non-Gaussianity Detection of EEG Signals Based on a Multivariate Scale Mixture Model for Diagnosis of Epileptic Seizures
Experiments using simulated and real EEG data demonstrated the validity of the model and its applicability to epileptic seizure detection.
A Neural Network Based on the Johnson $S_\mathrm{U}$ Translation System and Related Application to Electromyogram Classification
In this study, a discriminative model based on the multivariate Johnson $S_\mathrm{U}$ translation system is transformed into a linear combination of coefficients and input vectors using log-linearization.
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis
This paper proposes a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns.
