Specific Language Impairment (SLI) is a disorder that affects communication and can affect both comprehension and expression. This study focuses on effectively detecting SLI in children using transcripts of spontaneous narratives from 1063 interviews. A three-stage cascading pipeline was proposed f. In the first stage, feature extraction and dimensionality reduction of the data are performed using the Random Forest (RF) and Spearman correlation methods. In the second stage, the most predictive variables from the first stage are estimated using logistic regression, which is used in the last stage to detect SLI in children from transcripts of spontaneous narratives using a nearest neighbor classifier. The results revealed an accuracy of 97.13% in identifying SLI, highlighting aspects such as the length of the responses, the quality of their utterances, and the complexity of the language. This new approach, framed in natural language processing, offers significant benefits to the field of SLI detection by avoiding complex subjective variables and focusing on quantitative metrics directly related to the child's performance.

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.

Electroencephalograms (EEG) are a noninvasive longstanding medical modality that measures the brain's activity by recording the electromagnetic field at the scalp. Since its creation, EEG has played a fundamental role in understanding several major neurological disorders, by analyzing their manifestation into brain rhythms. For example, the study of deceases such as depression, age-related cognitive deterioration, epilepsy, anxiety disorders and subnormal brain development in children have benefited from this technology. The typical brain rhythms are distinguished by their different frequency ranges, called delta (δ) within the range 0.5 to 4Hz, theta (θ) within the range 4 to 7.5Hz, alpha (α) within the range 8 to 13Hz, beta (β) within the range 14 to 30Hz, and gamma (γ) within the range 30 to 64Hz. In this study, we focus on the brain rhythm called mu (µ) within the range 7.5 to 11.5Hz. Mu-waves are considered to emerge naturally and may convey information about what the functioning of brain hierarchies [1]. According to [2], there exist three historical theoretical hypotheses to explaining the mu-brain rhythm: i) the neuronal hyperexcitability related to the rolandic cortex; ii) the superficial cortical inhibition explaining its suppression with motor activity; and iii) the somatosensory cortical idling, related to the afference-dependent phenomenon.

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.