Evaluation of sleep stage classification using feature importance of EEG signal for big data healthcare

Sleep analysis is widely and experimentally considered due to its importance to body health care. Since its sufficiency is essential for a healthy life, people often spend almost a third of their lives sleeping. In this case, a similar sleep pattern is not practiced by every individual, regarding pure healthiness or disorders such as insomnia, apnea, bruxism, epilepsy, and narcolepsy. Therefore, this study aims to determine the classification patterns of sleep stages, using big data for health care. This used a high-dimensional FFT extraction algorithm, as well as a feature importance and tuning classifier, to develop accurate classification. The results showed that the proposed method led to more accurate classification than previous techniques. This was because the previous experiments had been conducted with the feature selection model, with accuracy implemented as a performance evaluation. Meanwhile, the EEG Sleep Stages classification model in this present report was composed of the feature selection and importance of the extraction stage. The previous and present experiments also reached the highest values of accuracy, with the Random Forest and SVM models using 2000 and 3000 features (87.19% and 89.19%, respectively. In this article, we proposed an analysis that the feature importance subsequently influenced the model's accuracy. This was because the proposed method was easily fine-tuned and optimized for each subject to improve sensitivity and reduce false negative occurrences

Feature Importance Explanations for Temporal Black-Box Models

Models in the supervised learning framework may capture rich and complex representations over the features that are hard for humans to interpret. Existing methods to explain such models are often specific to architectures and data where the features do not have a time-varying component. In this work, we propose TIME, a method to explain models that are inherently temporal in nature. Our approach (i) uses a model-agnostic permutation-based approach to analyze global feature importance, (ii) identifies the importance of salient features with respect to their temporal ordering as well as localized windows of influence, and (iii) uses hypothesis testing to provide statistical rigor

Development of Artificial Intelligence Algorithms for Early Diagnosis of Sepsis

Sepsis is a prevalent syndrome that manifests itself through an uncontrolled response from the body to an infection, that may lead to organ dysfunction. Its diagnosis is urgent since early treatment can reduce the patients’ chances of having long-term consequences. Yet, there are many obstacles to achieving this early detection. Some stem from the syndrome’s pathogenesis, which lacks a characteristic biomarker. The available clinical detection tools are either too complex or lack sensitivity, in both cases delaying the diagnosis. Another obstacle relates to modern technology, that when paired with the many clinical parameters that are monitored to detect sepsis, result in extremely heterogenous and complex medical records, which constitute a big obstacle for the responsible clinicians, that are forced to analyse them to diagnose the syndrome. To help achieve this early diagnosis, as well as understand which parameters are most relevant to obtain it, an approach based on the use of Artificial Intelligence algorithms is proposed in this work, with the model being implemented in the alert system of a sepsis monitoring platform. This platform uses a Random Forest algorithm, based on supervised machine learning classification, that is capable of detecting the syndrome in two different scenarios. The earliest detection can happen if there are only five vital sign parameters available for measurement, namely heart rate, systolic and diastolic blood pressures, blood oxygen saturation level, and body temperature, in which case, the model has a score of 83% precision and 62% sensitivity. If besides the mentioned variables, laboratory analysis measurements of bilirubin, creatinine, hemoglobin, leukocytes, platelet count, and Creactive protein levels are available, the platform’s sensitivity increases to 77%. With this, it has also been found that the blood oxygen saturation level is one of the most important variables to take into account for the task, in both cases. Once the platform is tested in real clinical situations, together with an increase in the available clinical data, it is believed that the platform’s performance will be even better.A sépsis é uma síndrome com elevada incidência a nível global, que se manifesta através de uma resposta desregulada por parte do organismo a uma infeção, podendo resultar em disfunções orgânicas generalizadas. O diagnóstico da mesma é urgente, uma vez que um tratamento precoce pode reduzir as hipóteses de consequências a longo prazo para os doentes. Apesar desta necessidade, existem vários obstáculos. Alguns deles advêm da patogenia da síndrome, que carece de um biomarcador específico. As ferramentas de deteção clínica são demasiado complexas, ou pouco sensíveis, em ambos os casos atrasando o diagnóstico. Outro obstáculo relaciona-se com os avanços da tecnologia, que, com os vários parâmetros clínicos que são monitorizados, resulta em registos médicos heterogéneos e complexos, o que constitui um grande obstáculo para os profissionais de saúde, que se vêm forçados a analisá-los para diagnosticar a síndrome. Para atingir este diagnóstico precoce, bem como compreender quais os parâmetros mais relevantes para o alcançar, é proposta neste trabalho uma abordagem baseada num algoritmo de Inteligência Artificial, sendo o modelo implementado no sistema de alerta de uma plataforma de monitorização de sépsis. Esta plataforma utiliza um classificador Random Forest baseado em aprendizagem automática supervisionada, capaz de diagnosticar a síndrome de duas formas. Uma deteção mais precoce pode ocorrer através de cinco parâmetros vitais, nomeadamente frequência cardíaca, pressão arterial sistólica e diastólica, nível de saturação de oxigénio no sangue e temperatura corporal, caso em que o modelo atinge valores de 83% de precisão e 62% de sensibilidade. Se, para além das variáveis mencionadas, estiverem disponíveis análises laboratoriais de bilirrubina, creatinina, hemoglobina, leucócitos, contagem de plaquetas e níveis de proteína C-reativa, a sensibilidade da plataforma sobre para 77%. Concluiu-se que o nível de saturação de oxigénio no sangue é uma das variáveis mais importantes a ter em conta para o diagnóstico, em ambos os casos. A partir do momento que a plataforma venha a ser utilizada em situações clínicas reais, com o consequente aumento dos dados disponíveis, crê-se que o desempenho venha a ser ainda melhor