Fouzi Harrou

Time Series Modelling and Forecasting of Emergency Department Overcrowding

Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

Improved principal component analysis for anomaly detection

Developed PCA-based MCUSUM anomaly detection (AD) method.Extended the AD advantages of the MCUSUM to enhance the conventional PCA.The proposed algorithm is applied to monitor an emergency department.The detection results show effectiveness of the proposed method. Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotellings T 2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

Seasonal ARMA-based SPC charts for anomaly detection

Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

Anomaly detection/detectability for a linear model with a bounded nuisance parameter

Abstract Anomaly detection is addressed within a statistical framework. Often the statistical model is composed of two types of parameters: the informative parameters and the nuisance ones. The nuisance parameters are of no interest for detection but they are necessary to complete the model. In the case of unknown, non-random and non-bounded nuisance parameters, their elimination is unavoidable. Some approaches based on the assumption that the nuisance parameters belonging to a subspace interfere with the informative ones in a linear manner, use the theory of invariance to reject the nuisance. Unfortunately, this can lead to a serious degradation of the detector capacity because some anomalies are masked by nuisance parameters. Nevertheless, in many cases the physical nature of nuisance parameters is (partially) known, and this a priori knowledge permits to define lower and upper bounds for the nuisance parameters. The goal of this paper is to study the statistical performances of the constrained generalized likelihood ratio test used to detect an additive anomaly in the case of bounded nuisance parameters. An example of the integrity monitoring of GNSS train positioning illustrates the relevance of the proposed method.

Monitoring linear antenna arrays using an exponentially weighted moving average-based fault detection scheme

The evolution of modern wireless communications systems has dramatically increased the demand for antenna arrays. An antenna array with certain radiation characteristics is often desired. However, the actual radiation pattern of an antenna array changes when faults are introduced in the array. In this paper a statistical fault detection methodology based on the exponentially weighted moving average (EWMA) control scheme is proposed to detect possible faulty radiation patterns in linear antenna arrays. The proposed method detects the faults based on deviation in the radiation pattern from the desired ones. The difference between synthesized radiation pattern obtained using the Minimax algorithm and the measured pattern can be used as an indicator about the existence or absence of faults. To assess the fault detection abilities of the EWMA control scheme, three case studies are considered, one involving a complete failure in one element in the array, one involving partial failure in two elements, and one involving degradation caused by random noise due to interference and other factors. The simulation results for all cases show the effectiveness of the proposed EWMA fault detection method.

Accelerometer and Camera-Based Strategy for Improved Human Fall Detection

In this paper, we address the problem of detecting human falls using anomaly detection. Detection and classification of falls are based on accelerometric data and variations in human silhouette shape. First, we use the exponentially weighted moving average (EWMA) monitoring scheme to detect a potential fall in the accelerometric data. We used an EWMA to identify features that correspond with a particular type of fall allowing us to classify falls. Only features corresponding with detected falls were used in the classification phase. A benefit of using a subset of the original data to design classification models minimizes training time and simplifies models. Based on features corresponding to detected falls, we used the support vector machine (SVM) algorithm to distinguish between true falls and fall-like events. We apply this strategy to the publicly available fall detection databases from the university of Rzeszow’s. Results indicated that our strategy accurately detected and classified fall events, suggesting its potential application to early alert mechanisms in the event of fall situations and its capability for classification of detected falls. Comparison of the classification results using the EWMA-based SVM classifier method with those achieved using three commonly used machine learning classifiers, neural network, K-nearest neighbor and naïve Bayes, proved our model superior.

A statistical fault detection strategy using PCA based EWMA control schemes

In data-based method for fault detection, principal component analysis (PCA) has been used successfully for fault detection in system with highly correlated variables. The aim of this paper is to combine the exponentially weighted moving average (EWMA) control scheme with PCA model in order to improve fault detection performance. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. Because of the ability of EWMA control scheme for detecting small changes, this technique is appropriate to improve the detection of a small fault in PCA model. The performance of the PCA-based EWMA fault detection algorithm is illustrated and compared to conventional fault detection methods using simulated continuously stirred tank reactor (CSTR) data. The results show the effectiveness of the developed algorithm.

Univariate process monitoring using multiscale Shewhart charts.

Monitoring charts play an important role in statistical quality control. Shewhart charts are among the most commonly used charts in process monitoring, and have seen many extensions for improved performance. Unfortunately, measured practical data are usually contaminated with noise, which degrade the detection abilities of the conventional Shewhart chart by increasing the rate of false alarms. Therefore, the effect of noise needs to be suppressed for enhanced process monitoring. Wavelet-based multiscale representation of data, which is a powerful feature extraction tool, has shown good abilities to efficiently separate deterministic and stochastic features. In this paper, the advantages of multiscale representation are exploited to enhance the fault detection performance of the conventional Shewhart chart by developing an integrated multiscale Shewhart algorithm. The performance of the developed algorithm is illustrated using two examples, one using synthetic data, and the other using simulated distillation column data. The simulation results clearly show the effectiveness of the proposed method over the conventional Shewhart chart and the conventional Shewhart chart applied on multiscale pre-filtered data.

Enhanced monitoring using PCA-based GLR fault detection and multiscale filtering

One of the most popular multivariate statistical methods used for data-based process monitoring is Principal Component Analysis (PCA). In the absence of a process model, PCA has been successfully used as a data-based FD technique for highly correlated process variables. Some of the PCA detection indices include the T2 or Q statistics, which have their advantages and disadvantages. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abili ties. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The PCA-based GLR fault detection algorithm provides optimal properties by maximizing the detection probability of faults for a given false alarm rate. However, the presence of measurement noise and modeling errors increase the rate of false alarms. Therefore, to further improve the quality of fault detection, multiscale filtering is utilized to filter the residuals obtained from the PCA model, which helps suppress the effect on errors, and thus decrease the false alarm rate. The proposed fault detection methodology is demonstrated through its application to monitor the ozone level in the Upper Normandy region, France, and it is shown to effectively reduce the rate of false alarms whilst retaining the capability of detecting process faults.

Detecting abnormal ozone levels using PCA-based GLR hypothesis testing

Ozone is one of the lost serious air pollution problems. Monitoring abnormal changes in the concentration of ozone in the troposphere is of great interest because of its negative influence on human health, vegetation, and materials. Modeling ozone is very challenging because of the complexity of the ozone formation mechanisms in the troposphere and the uncertainty about the meteorological conditions in urban areas. In the absence of a process model, principal component analysis (PCA), which is a multivariate statistical technique, has been successfully used as a data-based fault detection (FD) method for highly correlated process variables. When a process model is available, however, the generalized likelihood ratio (GLR) test, which is a statistical hypothesis testing method, has shown good fault detection abilities. In this work, a PCA-based GLR fault detection algorithm is developed to exploit the advantages of the GLR test in the absence of a process model. In fact, PCA is used to provide a modeling framework for the develop fault detection algorithm. The developed PCA-based GLR FD algorithm is utilized to enhance monitoring the ozone concentrations in Upper Normandy, France. The performances of PCA and PCA-based GLR test are compared through two practical case studies, one involving a sensor fault and the other involving tropospheric ozone pollution in multiple measuring stations. The results show that the PCA-based GLR test can detect abnormal ozone levels with a smaller number of false alarms than the conventional PCA method.