Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap to some extent. In the first and second...

In this paper we present a novel concept for simultaneous shape estimation and motion analysis based on a feed-forward TDNN architecture with adaptable spatio-temporal receptive fields. On synthetic image sequences displaying elliptic spots of different orientation moving horizontally across the scene at several speeds, this network simultaneously manages to classify the shapes correctly as wel...

This paper proposes an empirical modeling of the system formed by the riserplatform connection, in deep water. This connection has the objective of minimizing the acting bending moment, possesses high complexity and highcriticity due to economic and environmental consequences from its fault. The main element in the joint is made of elastomeric material, which reveals nonlinear hysteresis. In ad...

In this paper, we present the use of different mathematical models to forecast service requests in support centers (SCs). A successful prediction of service request can help in the efficient management of both human and technological resources that are used to solve these eventualities. A nonlinear analysis of the time series indicates the convenience of nonlinear modeling. Neural models based ...

The aim of this paper is to investigate the performance of time delay neural networks (TDNNs) and the probabilistic neural networks (PNNs) trained with nonlinear features (Lyapunov exponents and Entropy) on electroencephalogram signals (EEG) in a specific pathological state. For this purpose, two types of EEG signals (normal and partial epilepsy) are analyzed. To evaluate the performance of the...

Linear and nonlinear (TDNN) models have been shown to estimate hand position using populations of action potentials collected in the pre-motor and motor cortical areas of a primate’s brain. One of the applications of this discovery is to restore movement in patients suffering from paralysis. For real-time implementation of this technology, reliable and accurate signal processing models that pro...

We present a number of Time-Delay Neural Network (TDNN) based architectures for multi-speaker phoneme recognition (/b,d,g/ task). We use speech of two females and four males to compare the performance of the various architectures against a baseline recognition rate of 95.9% for a single IDNN on the six-speaker /b,d,g/ task. This series of modular designs leads to a highly modular multi-network ...

Speaker adaptation aims to estimate a speaker specific acoustic model from a speaker independent one to minimize the mismatch between the training and testing conditions arisen from speaker variabilities. A variety of neural network adaptation methods have been proposed since deep learning models have become the main stream. But there still lacks an experimental comparison between different met...

We define a Gamma multi-layer perceptron (MLP) as an MLP with the usual synaptic weights replaced by gamma filters (as proposed by de Vries and Principe (de Vries and Principe, 1992)) and associated gain terms throughout all layers. We derive gradient descent update equations and apply the model to the recognition of speech phonemes. We find that both the inclusion of gamma filters in all layer...

This research focuses on the development of a machine learning technique based on Time-Delay Neural Networks (TDNN) and Independent Component Analysis (ICA), to analyze EEG signal dynamics related to the initiation and propagation of epileptic seizures. We aim at designing a generative model to simulate EEG time-series after alteration of specific localized channels (electrodes) in order to exp...