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- Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEGPublication . Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Freitas, Diogo; Dias, Fernando Morgado; Ravelo-García, Antonio G.The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalo gram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels’ feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2–F4, C4–A1, F4–C4), which is in line with the CAP protocol to ensure multiple channels’ arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.
- Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern DetectionPublication . Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Freitas, Diogo; Dias, Fernando Morgado; Ravelo-García, Antonio G.Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.
- An oximetry based wireless device for sleep apnea detectionPublication . Mendonça, Fabio; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Ravelo-García, Antonio G.Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-intensive, and expensive. A non-invasive and easy to self-assemble home monitoring device was developed to address these issues. The device can perform the OSA diagnosis at the patient’s home and a specialized technician is not required to supervise the process. An automatic scoring algorithm was developed to examine the blood oxygen saturation signal for a minute-by-minute OSA assessment. It was performed by analyzing statistical and frequency-based features that were fed to a classifier. Afterward, the ratio of the number of minutes classified as OSA to the time in bed in minutes was compared with a threshold for the global (subject-based) OSA diagnosis. The average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve for the minute-by-minute assessment were, respectively, 88%, 80%, 91%, and 0.86. The subject-based accuracy was 95%. The performance is in the same range as the best state of the art methods for the models based only on the blood oxygen saturation analysis. Therefore, the developed model has the potential to be employed in clinical analysis.
- A Review of Approaches for Sleep Quality AnalysisPublication . Mendonça, Fabio; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Ravelo-García, Antonio G.; Penzel, ThomasSleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term ‘‘sleep quality’’ still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been proposed to address the estimation of sleep quality, and devices were developed to conduct the examination in the subject’s home. The objective of this paper is to analyze the methods and the devices presented in the literature, assessing the development of objective markers that could lead to an improvement of the subjective sleep experience understanding, leading to developments in the treatment of sleep quality deficits. A systematic review was conducted, selecting research articles published from 2000 to 2018, and two research questions were formulated, specifically, ‘‘what methods for sleep quality assessment have been developed’’ and ‘‘what kind of measures are employed by the devices that have been developed to estimate sleep quality.’’ The research trend for the assessment of sleep quality is based on the sleep macrostructure, and it was verified that despite the convenience and considerable popularity among the consumers of home health monitoring of devices, such as actigraphs, the validity of these tools regarding the estimation of sleep quality still needs to be systematically examined. A detailed resume of the key findings and the identified challenges are presented, ascertaining the main gaps in the current state of the art.
- Noncontact Automatic Water-Level Assessment and Prediction in an Urban Water Stream Channel of a Volcanic Island Using Deep LearningPublication . Mendonça, Fabio; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Azevedo, Joaquim Amândio; Ravelo-García, Antonio G.; Navarro-Mesa, Juan L.Traditional methods for water-level measurement usually employ permanent structures, such as a scale built into the water system, which is costly and laborious and can wash away with water. This research proposes a low-cost, automatic water-level estimator that can appraise the level without disturbing water flow or affecting the environment. The estimator was developed for urban areas of a volcanic island water channel, using machine learning to evaluate images captured by a low-cost remote monitoring system. For this purpose, images from over one year were collected. For better performance, captured images were processed by converting them to a proposed color space, named HLE, composed of hue, lightness, and edge. Multiple residual neural network architectures were examined. The best-performing model was ResNeXt, which achieved a mean absolute error of 1.14 cm using squeeze and excitation and data augmentation. An explainability analysis was carried out for transparency and a visual explanation. In addition, models were developed to predict water levels. Three models successfully forecasted the subsequent water levels for 10, 60, and 120 min, with mean absolute errors of 1.76 cm, 2.09 cm, and 2.34 cm, respectively. The models could follow slow and fast transitions, leading to a potential flooding risk-assessment mechanism.
- Multi-objective hyperparameter optimization of convolutional neural network for obstructive sleep apnea detectionPublication . Mostafa, Sheikh Shanawaz; Mendonça, Fábio; Ravelo-García, Antonio G.; Dias, Fernando MorgadoObstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea detection, such as airflow and electrocardiogram. However, the reduction of airflow normally leads to a decrease in the blood oxygen saturation level (SpO2). This signal is usually measured by a pulse oximeter, a sensor that is cheap, portable, and easy to assemble. Therefore, the SpO2 was chosen as the reference signal. Feature based classifiers with shallow neural networks have been developed to provide apnea detection using SpO2. However, two main issues arise, the need for feature creation and the selection of the more relevant features. Deep neural networks can solve these issues by employing featureless methods. Among multiple deep classifiers that have been developed, convolution neural networks (CNN) are gaining popularity. However, the selection of the CNN structure and hyperparameters are typically done by experts, where prior knowledge is essential. With these problems in mind, an algorithm for automatic structure selection and hyper parameterization of a one dimension CNN was developed to detect OSA events using only the SpO2 signal. Three different input sizes and databases were tested, and the best model achieved an average accuracy, sensitivity, and specificity of 94%, 92%, and 96%, respectively.
- Visual Explanations of Deep Learning Architectures in Predicting Cyclic Alternating Patterns Using Wavelet TransformsPublication . Gupta, Ankit; Mendonça, Fabio; Mostafa, Sheikh Shanawaz; Ravelo-García, Antonio G.; Dias, Fernando Morgado: Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the ampli tude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black box approach of the machine learning approach, the proposed systems remain untrusted by the physician. This study contributes to accurately estimating CAP in a Frequency-Time domain by A-phase and its subtypes prediction by transforming the monopolar deviated electroencephalogram signals into corresponding scalograms. Subsequently, various computer vision classifiers were tested for the A-phase using scalogram images. It was found that MobileNetV2 outperformed all other tested classifiers by achieving the average accuracy, sensitivity, and specificity values of 0.80, 0.75, and 0.81, respectively. The MobileNetV2 trained model was further fine-tuned for A-phase subtypes prediction. To further verify the visual ability of the trained models, Gradcam++ was employed to identify the targeted regions by the trained network. It was verified that the areas identified by the model match the regions focused on by the sleep experts for A-phase predictions, thereby proving its clinical viability and robustness. This motivates the development of novel deep learning based methods for CAP patterns predictions.
- Automatic Detection of a Phases for CAP ClassificationPublication . Mendonça, Fábio; Fred, Ana; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Ravelo-García, Antonio G.The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then applying a finite state machine to implement the final classification. A public database was used to test the algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select the most relevant features and a post processing procedure was used for further improvement of the classification. The classification of the A phases was produced using linear discriminant analysis and the average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM periods, contrary to the method that is used in the majority of the state of the art publications which leads to an increase in the overall performance. However, the approach of this work is more suitable for automatic system implementation since no alteration of the EEG data is needed.
- A Systematic Review of Detecting Sleep Apnea Using Deep LearningPublication . Mostafa, Sheikh Shanawaz; Mendonça, Fábio; Ravelo-García, Antonio G.; Dias, Fernando MorgadoSleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic classification algorithms. Deep learning is gaining higher interest due to database availability, newly developed techniques, the possibility of producing machine created features and higher computing power that allows the algorithms to achieve better performance than the shallow classifiers. Therefore, the sleep apnea research has currently gained significant interest in deep learning. The goal of this work is to analyze the published research in the last decade, providing an answer to the research questions such as how to implement the different deep networks, what kind of pre-processing or feature extraction is needed, and the advantages and disadvantages of different kinds of networks. The employed signals, sensors, databases and implementation challenges were also considered. A systematic search was conducted on five indexing services from 2008–2018. A total of 255 papers were found and 21 were selected by considering the inclusion and exclusion criteria, using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach.
- On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature CreationPublication . Mendonça, Fábio; Mostafa, Sheikh Shanawaz; Dias, Fernando Morgado; Ravelo-García, Antonio G.This study presents a novel approach for kernel selection based on Kullback–Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model’s parameters. Each latent variable is sampled from the distribution associated with a single kernel of the last encoder’s convolutional layer, resulting in an individual distribution for each kernel. Relevant features are selected from the sampled latent variables to perform kernel selection, which filters out uninformative features and, consequently, unnecessary kernels. Both the proposed filter method and the sequential feature selection (standard wrapper method) were examined for feature selection. Particularly, the filter method evaluates the Kullback–Leibler divergence between all kernels’ distributions and hypothesizes that similar kernels can be discarded as they do not convey relevant information. This hypothesis was confirmed through the experiments performed on four standard datasets, where it was observed that the number of kernels can be reduced without meaningfully affecting the performance. This analysis was based on the accuracy of the model when the selected kernels fed a probabilistic classifier and the feature-based similarity index to appraise the quality of the reconstructed images when the variational autoencoder only uses the selected kernels. Therefore, the proposed methodology guides the reduction of the number of parameters of the model, making it suitable for developing applications for resource-constrained devices.