ECG Classification with an Adaptive Neuro-Fuzzy Inference System

Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases can be made through an Adaptive Neuro-Fuzzy Inference System (ANFIS) preprocessed by subtractive clustering. Six types of heartbeats are classified: normal sinus rhythm, premature ventricular contraction (PVC), atrial premature contraction (APC), left bundle branch block (LBBB), right bundle branch block (RBBB), and paced beats. The goal is to detect important characteristics of an ECG signal to determine if the patient’s heartbeat is normal or irregular. The results from three trials indicate an average accuracy of 98.10%, average sensitivity of 94.99%, and average specificity of 98.87%. These results are comparable to two artificial neural network (ANN) algorithms: gradient descent and Levenberg Marquardt, as well as the ANFIS preprocessed by grid partitioning

Parameterization of point-cloud freeform surfaces using adaptive sequential learning RBFnetworks

We propose a self-organizing Radial Basis Function (RBF) neural network method for parameterization of freeform surfaces from larger, noisy and unoriented point clouds. In particular, an adaptive sequential learning algorithm is presented for network construction from a single instance of point set. The adaptive learning allows neurons to be dynamically inserted and fully adjusted (e.g. their locations, widths and weights), according to mapping residuals and data point novelty associated to underlying geometry. Pseudo-neurons, exhibiting very limited contributions, can be removed through a pruning procedure. Additionally, a neighborhood extended Kalman filter (NEKF) was developed to significantly accelerate parameterization. Experimental results show that this adaptive learning enables effective capture of global low-frequency variations while preserving sharp local details, ultimately leading to accurate and compact parameterization, as characterized by a small number of neurons. Parameterization using the proposed RBF network provides simple, low cost and low storage solutions to many problems such as surface construction, re-sampling, hole filling, multiple level-of-detail meshing and data compression from unstructured and incomplete range data. Performance results are also presented for comparison

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Developmental Flight Test of a Powered Approach Stability Augmentation System on the U.S. Navy\u27s E- 2C Hawkeye 2000 Aircraft

The E-2C aircraft is a Navy carrier based high-wing, twin engine turboprop powered aircraft used for the Airborne Early Warning (AEW) mission. In the power approach configuration, the aircraft displays strong adverse yaw, weak directional stability, and excessive rudder control power. These antagonistic characteristics, when coupled together, result in an extremely high workload for the pilot during both carrier and field landings. Although the aircraft has a yaw axis stability augmentation system, it is currently only applicable to cruise conditions. Engaging the stability augmentation in the power approach configuration results in a 1 Hz directional oscillation due to the system’s high gain schedule. Additionally, another attribute of the existing system design results in extremely high rudder pedal forces while maintaining sideslip in crosswind conditions. Northrop Grumman developed Flight Control Computer (FCC) software patches designed to improve the handling qualities on landing approaches. These patches are designed to change the rudder control gain schedule to allow the use of stability augmentation in the power approach configuration and suppress the divergent Phugoid characteristic throughout the flight envelope. The system is a directional axis controller only and termed the Powered Approach Stability Augmentation System (PASAS). Initial flight tests on a developmental system provided the design parameters for the production system, which was eventually installed in the Navy’s newest E-2C variant, termed Hawkeye 2000. The ensuing flight test program consisted of land based test flights during the summer of 2001, and culminated in a ship trial consisting of multiple landings on the USS Truman in March of 2002

Comprehensible credit scoring models using rule extraction from support vector machines.

In recent years, Support Vector Machines (SVMs) were successfully applied to a wide range of applications. Their good performance is achieved by an implicit non-linear transformation of the original problem to a high-dimensional (possibly infinite) feature space in which a linear decision hyperplane is constructed that yields a nonlinear classifier in the input space. However, since the classifier is described as a complex mathematical function, it is rather incomprehensible for humans. This opacity property prevents them from being used in many real- life applications where both accuracy and comprehensibility are required, such as medical diagnosis and credit risk evaluation. To overcome this limitation, rules can be extracted from the trained SVM that are interpretable by humans and keep as much of the accuracy of the SVM as possible. In this paper, we will provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX. The described techniques are compared using publicly avail- able datasets, such as Ripley's synthetic dataset and the multi-class iris dataset. We will also look at medical diagnosis and credit scoring where comprehensibility is a key requirement and even a regulatory recommendation. Our experiments show that the SVM rule extraction techniques lose only a small percentage in performance compared to SVMs and therefore rank at the top of comprehensible classification techniques.Credit; Credit scoring; Models; Model; Applications; Performance; Space; Decision; Yield; Real life; Risk; Evaluation; Rules; Neural networks; Networks; Classification; Research;

Dynamic non-linear system modelling using wavelet-based soft computing techniques

The enormous number of complex systems results in the necessity of high-level and cost-efficient modelling structures for the operators and system designers. Model-based approaches offer a very challenging way to integrate a priori knowledge into the procedure. Soft computing based models in particular, can successfully be applied in cases of highly nonlinear problems. A further reason for dealing with so called soft computational model based techniques is that in real-world cases, many times only partial, uncertain and/or inaccurate data is available. Wavelet-Based soft computing techniques are considered, as one of the latest trends in system identification/modelling. This thesis provides a comprehensive synopsis of the main wavelet-based approaches to model the non-linear dynamical systems in real world problems in conjunction with possible twists and novelties aiming for more accurate and less complex modelling structure. Initially, an on-line structure and parameter design has been considered in an adaptive Neuro- Fuzzy (NF) scheme. The problem of redundant membership functions and consequently fuzzy rules is circumvented by applying an adaptive structure. The growth of a special type of Fungus (Monascus ruber van Tieghem) is examined against several other approaches for further justification of the proposed methodology. By extending the line of research, two Morlet Wavelet Neural Network (WNN) structures have been introduced. Increasing the accuracy and decreasing the computational cost are both the primary targets of proposed novelties. Modifying the synoptic weights by replacing them with Linear Combination Weights (LCW) and also imposing a Hybrid Learning Algorithm (HLA) comprising of Gradient Descent (GD) and Recursive Least Square (RLS), are the tools utilised for the above challenges. These two models differ from the point of view of structure while they share the same HLA scheme. The second approach contains an additional Multiplication layer, plus its hidden layer contains several sub-WNNs for each input dimension. The practical superiority of these extensions is demonstrated by simulation and experimental results on real non-linear dynamic system; Listeria Monocytogenes survival curves in Ultra-High Temperature (UHT) whole milk, and consolidated with comprehensive comparison with other suggested schemes. At the next stage, the extended clustering-based fuzzy version of the proposed WNN schemes, is presented as the ultimate structure in this thesis. The proposed Fuzzy Wavelet Neural network (FWNN) benefitted from Gaussian Mixture Models (GMMs) clustering feature, updated by a modified Expectation-Maximization (EM) algorithm. One of the main aims of this thesis is to illustrate how the GMM-EM scheme could be used not only for detecting useful knowledge from the data by building accurate regression, but also for the identification of complex systems. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient hybrid learning approach is used to adjust the parameters of dilation, translation, weights, and membership. Extended Kalman Filter (EKF) is employed for wavelet parameters adjustment together with Weighted Least Square (WLS) which is dedicated for the Linear Combination Weights fine-tuning. The results of a real-world application of Short Time Load Forecasting (STLF) further re-enforced the plausibility of the above technique