Antonio Quintero-Rincón

Antonio Quintero-Rincón, Jorge Prendes, Valeria Muro et al.

Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t-location-scale statistical representation to detect spike-and-waves. The proposed approach is demonstrated on a real dataset containing both spike-and-wave events and normal brain function signals, where our performance is evaluated in terms of classification accuracy, sensitivity, and specificity.

Nicolas Masino, Antonio Quintero-Rincon

Breast cancer detection is still an open research field, despite a tremendous effort devoted to work in this area. Effect size is a statistical concept that measures the strength of the relationship between two variables on a numeric scale. Feature selection is widely used to reduce the dimensionality of data by selecting only a subset of predictor variables to improve a learning model. In this work, an algorithm and experimental results demonstrate the feasibility of developing a statistical feature-selector-based learning tool capable of reducing the data dimensionality using parametric effect size measures from features extracted from cell nuclei images. The SVM classifier with a linear kernel as a learning tool achieved an accuracy of over 90%. These excellent results suggest that the effect size is within the standards of the feature-selector methods

Antonio Quintero Rincon, Nicolas Masino, Veronica Marsico et al.

Spike-and-wave discharges (SWDs) are the electroencephalographic hallmark of absence epilepsy, yet their manual identification in multi-day recordings remains labour-intensive and error-prone. We present a lightweight hybrid pipeline that couples analytical features with a shallow artificial neural network (ANN) for accurate, patient-specific SWD detection in long-term, monopolar EEG. A two-sided moving-average (MA) filter first suppresses the high-frequency components of normal background activity. The residual signal is then summarised by the mean and the standard deviation of its normally distributed samples, yielding a compact, two-dimensional feature vector for every 20s window. These features are fed to a single-hidden-layer ANN trained via back-propagation to classify each window as SWD or non-SWD. The method was evaluated on 780 channels sampled at 256 Hz from 12 patients, comprising 392 annotated SWD events. It correctly detected 384 events (sensitivity: 98%) while achieving a specificity of 96.2 % and an overall accuracy of 97.2%. Because feature extraction is analytic, and the classifier is small, the pipeline runs in real-time and requires no manual threshold tuning. These results indicate that normal-distribution descriptors combined with a modest ANN provide an effective and computationally inexpensive solution for automated SWD screening in extended EEG recordings.

Antonio Quintero-Rincon, Carlos D'Giano, Marcelo Risk

To predict an epileptic event means the ability to determine in advance the time of the seizure with the highest possible accuracy. A correct prediction benchmark for epilepsy events in clinical applications is a typical problem in biomedical signal processing that helps to an appropriate diagnosis and treatment of this disease. In this work, we use Pearson's product-moment correlation coefficient from generalized Gaussian distribution parameters coupled with a linear-based classifier to predict between seizure and non-seizure events in epileptic EEG signals. The performance in 36 epileptic events from 9 patients showing good performance with 100% of effectiveness for sensitivity and specificity greater than 83% for seizures events in all brain rhythms. Pearson's test suggests that all brain rhythms are highly correlated in non-seizure events but no during the seizure events. This suggests that our model can be scaled with the Pearson's product-moment correlation coefficient for the detection of epileptic seizures.

Antonio Quintero-Rincón, Valeria Muro, Carlos D'Giano et al.

Spike-and-wave discharge (SWD) pattern classification in electroencephalography (EEG) signals is a key problem in signal processing. It is particularly important to develop a SWD automatic detection method in long-term EEG recordings since the task of marking the patters manually is time consuming, difficult and error-prone. This paper presents a new detection method with a low computational complexity that can be easily trained if standard medical protocols are respected. The detection procedure is as follows: First, each EEG signal is divided into several time segments and for each time segment, the Morlet 1-D decomposition is applied. Then three parameters are extracted from the wavelet coefficients of each segment: scale (using a generalized Gaussian statistical model), variance and median. This is followed by a k-nearest neighbors (k-NN) classifier to detect the spike-and-wave pattern in each EEG channel from these three parameters. A total of 106 spike-and-wave and 106 non-spike-and-wave were used for training, while 69 new annotated EEG segments from six subjects were used for classification. In these circumstances, the proposed methodology achieved 100% accuracy. These results generate new research opportunities for the underlying causes of the so-called absence epilepsy in long-term EEG recordings.

Antonio Quintero-Rincón, Catalina Carenzo, Joaquín Ems et al.

Epilepsy is an important public health issue. An appropriate epileptiform discharge pattern detection of this neurological disease is a typical problem in biomedical engineering. In this paper, a new method is proposed for spike-and-wave discharge pattern detection based on Kendall's Tau-b coefficient. The proposed approach is demonstrated on a real dataset containing spike-and-wave discharge signals, where our performance is evaluated in terms of high Specificity, rule in (SpPIn) with 94% for patient-specific spike-and-wave discharge detection and 83% for a general spike-and-wave discharge detection.