Examinando Bioingeniería por Materia "ALGORITMOS"
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Artículo de Publicación PeriódicaAutomated detection and quantification of reverse triggering efort under mechanical ventilation(2021-02-15) Pham, Tài; Montanya, Jaume; Telias, Irene; Piraino, Thomas; Magrans, Rudys; Coudroy, Rémi; Damiani, L. Felipe; Mellado Artigas, Ricard; Madorno, Matías; Blanch, Lluis; Brochard, Laurent"Reverse triggering (RT) is a dyssynchrony defned by a respiratory muscle contraction following a passive mechanical insufation. It is potentially harmful for the lung and the diaphragm, but its detection is challenging. Magnitude of efort generated by RT is currently unknown. Our objective was to validate supervised methods for automatic detection of RT using only airway pressure (Paw) and fow. A secondary objective was to describe the magnitude of the eforts generated during RT." Artículo de Publicación PeriódicaFast statistical model-based classification of epileptic EEG signals(2018-01) Quintero-Rincón, Antonio; Pereyra, Marcelo; D'Giano, Carlos; Risk, Marcelo; Batatia, Hadj"This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using awavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature." Artículo de Publicación PeriódicaReal-time detection of imminent ventricular fibrillation using mean and standard deviation of beat-to-beat HRV(2018) Mosquera, Candelaria; Racca, Dora María; Quintero-Rincón, Antonio"It is estimated that 50% of all cardiovascular deaths are caused by a sudden cardiac arrest (SCA), which represents 15% of global mortality, and its main cause is ventricular fibrillation (VF). Therefore, it is of interest to design new methods capable to detect changes in heart rate (HR or RR interval) that could announce the beginning of an imminent fibrillation. In this work, an effective novel indicator, based on mean and standard deviation of Heart Rate Variability (HRV), was studied and used to develop an algorithm that predicts imminent VF with 100% sensitivity and 100% specificity. The study was based on 65 RR intervals signals. The algorithm’s simplicity provides a quick-to-use implementation in a micro controller unit (MCU) for real-time VF detection, allowing its application in a variety of medical devices with electrocardiogram (ECG) modules."