Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

Design and performance assessment of correlation filters for the detection of objects in high clutter thermal imagery

The research reported in this thesis has examined means of enhancing the performance of the Optimal Trade-off Maximum Average Correlation Height (OT-MACH) filter for target detection in Forward Looking Infra-Red (FLIR) imagery acquired from a helicopter and border security FLIR camera in northern Kuwait. The data acquired with these FLIR sensors allows real-world evaluation of the comparative performance of the various filters that have been developed in the thesis. The results obtained have been quantified using well known performance measures such as Peak to Side-lobe Ratio (PSR) and Total Detection Error (TDE). The initial focus was to study the effect of modifying the OT-MACH parameters on the correlation metrics. A new optimisation technique has been presented, which computes statistically the filter alpha parameter associated with controlling the response of the filter to clutter noise. A further modification of the OT-MACH filter performance using the Difference of Gaussian bandpass filter (named the D-MACH filter) as a pre-processing stage has been described. The D-MACH has been applied to several test images containing single and multiple targets in the scene. Enhanced performance of the modified filter is demonstrated with improved metrics being obtained with less false side peaks in the correlation plane, especially when multiple targets are present in the test images. A further pre-processing technique was investigated using the Rayleigh distribution as a pre-processing filter (named the R-MACH filter). The R-MACH filter has been applied to multiple target types with tests conducted across various image data sets. The filter demonstrated an improvement over the Difference of Gaussian filter in terms of 6 reducing the number of parameters needing to be tuned whilst producing further enhanced correlation plane metrics. Finally, recommendations for future work has been made to improve the use of the OT-MACH filter in target detection and identification. A novel training image representation is proposed for further investigation, which will minimise the computational intensity of using the MACH filter for unconstrained object recognition

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Frequency domain filtering strategies for hybrid optical information processing

Due to the rapid development of spatial light modulators, optical materials and filter design techniques; real time pattern recognition exploiting hybrid optical correlation is increasingly attractive. The spatial light modulator (SLM) enables signal or image patterns to be encoded as amplitude and/or phase modulation patterns across a directed coherent optical beam. It is the vast computational potential of optical information processing that provides the motivation for the design of spatial filters suitable for implementation on currently available SLMs. A good correlation filter should produce a sharp localised correlation peak in the output plane, and be able to achieve this in the presence of noise in the input plane. Thus optimisation is of great importance in optical correlator systems. The lower frequency components produce a broad correlation peak, whereas the higher frequency-band produces a sharp correlation peak that is sensitive to noise. This suggests that a filter with a band-pass characteristic can be made tolerant to noise and also give good localisation of the correlation peak. Here, spatial frequency band tuning and adaptive filtering are developed for this purpose. Spatial frequency selectivity is found to be very important for the design of a spatial filter, a compromise between correlation peak sharpness and noise robustness is sought. Thus, the tuneable photorefractive filter is assessed and difference of Gaussian function filter is developed. For different noise characteristics the spatial filter parameters must be tuned to give optimised performance, this optimisation process depends greatly on the noise and target object spectral characteristics. Adaptive filter design is developed which integrates the phase only filter with the classical matched filter, where a variable amplitude threshold value is set so that, at a particular spatial pixel location, if the amplitude value is greater than the pre-set threshold, only phase information is recorded; otherwise, both the phase and amplitude information are encoded. The development of the synthetic discriminant function filters as distortion tolerant filters was motivated by the sensitivity of the spatial matched filters to distortions in the input image such as in-plane rotations, out-of-plane rotations and scale variations. In applications it is very important that a spatial filter detects the target object from the input scene regardless of its orientation. The design of synthetic discriminant function filters suitable for implementation on commercially available SLM's is an extremely important feature of current research in the area. Therefore, based on the filter synthetic discriminant function (fSDF), a modified filter synthetic discriminant function filter is developed. Via the filter modulation operator Ml the modified fSDF permits advantageous preprocessing of individual training set images that are used in a linear combination to construct the fSDF, which applies a modulation operator M to the synthetic discriminant function. A relaxation algorithm is used to satisfy the equal correlation peaks rule in the correlator output plane. As the filter modulation operators M and M can be given any functional form, the MfSDF design proposed herein is sufficiently general to be described as a unified filter modulation SDF design. By considering the implementation of the modified fSDF on currently available SLM's, the binary phase-only encoded and the multilevel phase and amplitude encoded modified fSDF, which are suitable for the binary mode SLM and the liquid crystal television respectively, are investigated and evaluated. The evaluation is performed to better understand the image distortion range that can be encoded using the modified fSDF filters. The Wiener filter, which has been used extensively for the image restoration and signal processing, is developed for robust optical pattern recognition and classification. The Wiener filter is formulated to incorporate the in-class image (to be detected) and the out-of-class noise image (to be rejected) into a single step filter construction. A Wiener filter-SDF is thus developed and investigated by applying it to vehicle recognition and laser cutting process control. The joint transform correlator (JTC) provides a popular alternative to the Van- derLugt architecture. To improve its performance, a modified fringe-adjusted filter based JTC is introduced and with a multi-object input shown to ameliorate the noise sensitivity of the fringe-adjusted filter based JTC; this provides a solution that overcomes the difficulties encountered with binary JTC techniques. In order to permit the JTC to accommodate a high degree of image distortion, a SDF based modified fringe-adjusted JTC is developed and investigated to illustrate its ability to deal with noisy multi-class, multi-object inputs

Bio-inspired log-polar based color image pattern analysis in multiple frequency channels

The main topic addressed in this thesis is to implement color image pattern recognition based on the lateral inhibition subtraction phenomenon combined with a complex log-polar mapping in multiple spatial frequency channels. It is shown that the individual red, green and blue channels have different recognition performances when put in the context of former work done by Dragan Vidacic. It is observed that the green channel performs better than the other two channels, with the blue channel having the poorest performance. Following the application of a contrast stretching function the object recognition performance is improved in all channels. Multiple spatial frequency filters were designed to simulate the filtering channels that occur in the human visual system. Following these preprocessing steps Dragan Vidacic\u27s methodology is followed in order to determine the benefits that are obtained from the preprocessing steps being investigated. It is shown that performance gains are realized by using such preprocessing steps

Design of coupled mace filters for optical pattern recognition using practical spatial light modulators

Spatial light modulators (SLMs) are being used in correlation-based optical pattern recognition systems to implement the Fourier domain filters. Currently available SLMs have certain limitations with respect to the realizability of these filters. Therefore, it is necessary to incorporate the SLM constraints in the design of the filters. The design of a SLM-constrained minimum average correlation energy (SLM-MACE) filter using the simulated annealing-based optimization technique was investigated. The SLM-MACE filter was synthesized for three different types of constraints. The performance of the filter was evaluated in terms of its recognition (discrimination) capabilities using computer simulations. The correlation plane characteristics of the SLM-MACE filter were found to be reasonably good. The SLM-MACE filter yielded far better results than the analytical MACE filter implemented on practical SLMs using the constrained magnitude technique. Further, the filter performance was evaluated in the presence of noise in the input test images. This work demonstrated the need to include the SLM constraints in the filter design. Finally, a method is suggested to reduce the computation time required for the synthesis of the SLM-MACE filter

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing