Search results for "Signal classification"
showing 4 items of 4 documents
Performance of the Shape of Partial Discharge Signal Wireless Probes
2013
This paper focuses on the evaluation of the performances of three different antenna sensors suitable of Partial Discharge (PD) measurements. Monopole, triangular and spherical antennas were simulated by means of the surface moment method. The transmitting system is modeled by a power electronic device with a fault current between two metal plate. The shape of the simulated transmitted and received signals have been compared to verify the sensor the provides the best fidelity among the three. The auto-correlation function and the Pearson correlation index are adopted here for the comparison. A discussion on the dynamic characteristic of the different antenna probes and their use in different…
Ventricular Fibrillation and Tachycardia Detection Using Features Derived from Topological Data Analysis
2022
A rapid and accurate detection of ventricular arrhythmias is essential to take appropriate therapeutic actions when cardiac arrhythmias occur. Furthermore, the accurate discrimination between arrhythmias is also important, provided that the required shocking therapy would not be the same. In this work, the main novelty is the use of the mathematical method known as Topological Data Analysis (TDA) to generate new types of features which can contribute to the improvement of the detection and classification performance of cardiac arrhythmias such as Ventricular Fibrillation (VF) and Ventricular Tachycardia (VT). The electrocardiographic (ECG) signals used for this evaluation were obtained from…
Signal-to-noise ratio in reproducing kernel Hilbert spaces
2018
This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed inde…
Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
2020
The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitori…