In recent years, electroencephalographic (EEG) signals have been intensively used in the area of emotion recognition, partcularly in distress identification due to its negative impact on physical and mental health. Traditionally, brain activity has been studied from a frequency perspective by computing the power spectral density of the EEG recordings and extracting features from different frequency sub-bands. However, these features are often individually extracted from single EEG channels, such that each brain region is separately evaluated, even when it has been corroborated that mental processes are based on the coordination of different brain areas working simultaneously. To take advantage of the brain’s behaviour as a synchronized network, in the present work, 2-D and 3-D spectral images constructed from common 32 channel EEG signals are evaluated for the first time to discern between emotional states of calm and distress using a well-known deep-learning algorithm, such as AlexNet. The obtained results revealed a significant improvement in the classification performance regarding previous works, reaching an accuracy about 84%. Moreover, no significant differences between the results provided by the diverse approaches considered to reconstruct 2-D and 3-D spectral maps from the original location of the EEG channels over the scalp were noticed, thus suggesting that these kinds of images preserve original spatial brain information.
en el ciclo productivo 2019. Se utilizó un diseño en bloques completos al azar (DBCA) dispuesto en parcelas subdivididas. La parcela principal correspondió a las variedades de frejol (negro y carioca) y la parcela secundaria a los biofertilizantes (sin biofertilizante, biofertilizante 1 y biofertilizante 2) con cuatro repeticiones. En todos los tratamientos, excepto el testigo, se aplicó el biofertilizante (500 ml) disuelto en 20 litros de agua en tres oportunidades (20 días después de la emergencia al estado de plántula, antes de la floración y en formación de vainas). Las determinaciones estudiadas fueron altura de plantas a los 30, 50 y 70 DDE, número de vainas por planta, número de granos por vaina, peso de 1000 granos y rendimiento del cultivo. Los datos fueron sometidos al análisis de varianza (ANAVA) y las medias de las variables se compararon por el test de Tukey al 5% de probabilidad de error. Los resultados arrojados por el experimento indican la eficacia de los biofertilizantes en la producción de variedades de frejol de forma significativa en todas las determinaciones evaluadas. Se observaron interacciones significativas de los factores en la altura a los 70 DDE, número de vainas y rendimiento.
The electrocardiogram (ECG) is a physiological signal highly sensitive to disturbances during its acquisition. To palliate this issue, many works have described preprocessing algorithms operating in 12-lead, short-term ECG recordings. However, only a few methods have been introduced to detect noisy segments in single-lead, long-term ECG signals, this being a pending challenge to be resolved. Hence, this work proposes a novel technique to automatically detect low-quality segments in single-lead, long-term ECG recordings. The method is based on the high learning capability of a convolutional neural network (CNN), which has been trained with 2D images obtained when turning ECG recordings into scalograms using a continuous Wavelet transform (CWT). To validate the method, a publicly available dataset containing single-lead, long-term ECG intervals from patients with different cardiac rhythms has been used. These signals have been annotated by experts, who identified noisy intervals and those with sufficient quality to be clinically interpreted. The results have shown that the proposed method discriminates correctly between low and high-quality ECG segments with an accuracy greater than 90%, and with sensitivity slightly larger than specificity.
Quality assessment of ECG signals acquired with wearable devices is essential to avoid misdiagnosis of some cardiac disorders. For that purpose, novel deep learning algorithms have been recently proposed. However, training of these methods require large amount of data and public databases with annotated ECG samples are limited. Hence, the present work aims at validating the usefulness of a well-known data augmentation approach in this context of ECG quality assessment. Precisely, classification between high-and low-quality ECG excerpts achieved by a common convolutional neural network (CNN) trained on two databases has been compared. On the one hand, 2,000 5 second-length ECG excerpts were initially selected from a freely available database. Half of the segments were extracted from noisy ECG recordings and the other half from high-quality signals. On the other hand, using a data augmentation approach based on time-scale modification, noise addition, and pitch shifting of the original noisy ECG experts, 1,000 additional low-quality intervals were generated. These surrogate noisy signals and the original highquality ones formed the second dataset. The results for both cases were compared using a McNemar test and no statistically significant differences were noticed, thus suggesting that the synthesized noisy signals could be used for reliable training of CNN-based ECG quality indices.
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