Over the last few decades, electroencephalogram (EEG) has become one of the most vital tools used by physicians to diagnose several neurological disorders of the human brain and, in particular, to detect seizures. Because of its peculiar nature, the consequent impact of epileptic seizures on the quality of life of patients made the precise diagnosis of epilepsy extremely essential. Therefore, this article proposes a novel deep-learning approach for detecting seizures in pediatric patients based on the classification of raw multichannel EEG signal recordings that are minimally pre-processed. The new approach takes advantage of the automatic feature learning capabilities of a two-dimensional deep convolution autoencoder (2D-DCAE) linked to a neural network-based classifier to form a unified system that is trained in a supervised way to achieve the best classification accuracy between the ictal and interictal brain state signals. For testing and evaluating our approach, two models were designed and assessed using three different EEG data segment lengths and a 10-fold cross-validation scheme. Based on five evaluation metrics, the best performing model was a supervised deep convolutional autoencoder (SDCAE) model that uses a bidirectional long short-term memory (Bi-LSTM) – based classifier, and EEG segment length of 4 s. Using the public dataset collected from the Children’s Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), this model has obtained 98.79 ± 0.53% accuracy, 98.72 ± 0.77% sensitivity, 98.86 ± 0.53% specificity, 98.86 ± 0.53% precision, and an F1-score of 98.79 ± 0.53%, respectively. Based on these results, our new approach was able to present one of the most effective seizure detection methods compared to other existing state-of-the-art methods applied to the same dataset.
ObjectiveTo evaluate the outcome of using semi-rigid ureteroscopy with or without intracorporeal pneumatic lithotripsy vs. temporary ureteric JJ stenting in the management of obstructing ureteric calculi in pregnant women.Patients and methodsThis prospective comparative study comprised 43 pregnant women with obstructing ureteric calculi. The diagnosis was based on the acute flank pain as the main symptom, microscopic haematuria, and unilateral hydronephrosis on abdominal ultrasonography (US). The patients were randomly divided into two groups; those in group 1 (22 patients) were treated by temporary ureteric JJ stenting until after delivery, and those in group 2 (21) were treated definitively by ureteroscopic stone extraction with intracorporeal pneumatic lithotripsy. Postoperative complications and the degree of patient satisfaction were reported.ResultsAn obstructing ureteric stone was identified by US in 68% and 76% of groups 1 and 2, respectively. In group 1, nine patients had mid-ureteric stones and 13 had stones in the lower ureter. In group 2, seven patients had mid-ureteric stones, whilst the stones were in the distal ureter in 14. No perioperative foetal complications were detected in any group and all patients completed the full term of pregnancy. In group 1, four patients had a postoperative urinary tract infection (UTI), and the JJ stent was exchanged in seven. Two patients in group 2 had a postoperative UTI.ConclusionsDefinitive ureteroscopy, even with intracorporeal pneumatic lithotripsy, is an effective and safe treatment for pregnant women with obstructing ureteric calculi. It has a better outcome and is more satisfactory for the patients than a temporary JJ stent.
Soil moisture (SM) plays a significant role in determining the probability of flooding in a given area. Currently, SM is most commonly modeled using physically-based numerical hydrologic models. Modeling the natural processes that take place in the soil is difficult and requires assumptions. Besides, hydrologic model runtime is highly impacted by the extent and resolution of the study domain. In this study, we propose a data-driven modeling approach using Deep Learning (DL) models. There are different types of DL algorithms that serve different purposes. For example, the Convolutional Neural Network (CNN) algorithm is well suited for capturing and learning spatial patterns, while the Long Short-Term Memory (LSTM) algorithm is designed to utilize time-series information and to learn from past observations. A DL algorithm that combines the capabilities of CNN and LSTM called ConvLSTM was recently developed. In this study, we investigate the applicability of the ConvLSTM algorithm in predicting SM in a study area located in south Louisiana in the United States. This study reveals that ConvLSTM significantly outperformed CNN in predicting SM. We tested the performance of ConvLSTM based models by using a combination of different sets of predictors and different LSTM sequence lengths. The study results show that ConvLSTM models can predict SM with a mean areal Root Mean Squared Error (RMSE) of 2.5% and mean areal correlation coefficients of 0.9 for our study area. ConvLSTM models can also provide predictions between discrete SM observations, making them potentially useful for applications such as filling observational gaps between satellite overpasses.
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