Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics

[EN] This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.Cuesta Frau, D.; Miró Martínez, P.; Jordán Núñez, J.; Oltra Crespo, S.; Molina Picó, A. (2017). Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Computers in Biology and Medicine. 87:141-151. doi:10.1016/j.compbiomed.2017.05.028S1411518

Seismic signal segmentation procedure using time-frequency decomposition and statistical modelling

In the paper a novel automatic seismic signal segmentation procedure is proposed. This procedure is motivated by analysis of real seismic vibration signals acquired in an underground mine. During regular mining activities in the underground mine one can expect some seismic events which appear just after the mining activity, e.g. blasting procedures, provoked relaxation of rock and some events that are unexpected, like natural rock burst. It often happens that, during one signal realization, several shocks (events) appear. Apart from two main sources of events (i.e. rock burst and blasting), other activities in the mine might also initiate seismic signal recording procedure (for example machine moving nearby the sensor). Obviously, significance of each type of recorded signal is very different, its shape in time domain, energy and frequency structure (i.e. spectrum of the signal) are different. In order to recognize these events automatically, recorded observation should be pre-processed in order to isolate a single event. The problem of signal segmentation is investigated in literature, several application domains might be found. Although, there are just a few works on seismic signal segmentation. In this paper we propose to use a time-frequency decomposition of the signal and model each sub-signal at every frequency bin using statistical methods. Narrowband components are much easier to search for so called structural breakpoint, i.e. time instance when properties of signal significantly change. It is obvious that simple energy-based methods applied to raw signal fail when one event begins before the previous one relaxed. In order to find beginning and end of a single event we propose to use measures based on empirical quantiles estimated for each sub-signal and, finally, aggregate 2D array into 1D probability vector which indicates location where statistical features has switched from one regime to another one. The proposed procedure can be applied in order to improve time domain isolation of single event for the case, when duration of signal acquisition is longer than duration of the event or to isolate single event from sequence of events (recorded for example during blasting)

Hidden Markov Models

Hidden Markov Models (HMMs), although known for decades, have made a big career nowadays and are still in state of development. This book presents theoretical issues and a variety of HMMs applications in speech recognition and synthesis, medicine, neurosciences, computational biology, bioinformatics, seismology, environment protection and engineering. I hope that the reader will find this book useful and helpful for their own research