Automated Risk Identification of Myocardial Infarction Using Relative Frequency Band Coefficient (RFBC) Features from ECG

Various structural and functional changes associated with ischemic (myocardial infarcted) heart cause amplitude and spectral changes in signals obtained at different leads of ECG. In order to capture these changes, Relative Frequency Band Coefficient (RFBC) features from 12-lead ECG have been proposed and used for automated identification of myocardial infarction risk. RFBC features reduces the effect of subject variabilty in body composition on the amplitude dependent features. The proposed method is evaluated on ECG data from PTB diagnostic database using support vector machine as classifier. The promising result suggests that the proposed RFBC features may be used in the screening and clinical decision support system for myocardial infarction

VPNet: Variable Projection Networks

In this paper, we introduce VPNet, a novel model-driven neural network architecture based on variable projections (VP). The application of VP operators in neural networks implies learnable features, interpretable parameters, and compact network structures. This paper discusses the motivation and mathematical background of VPNet as well as experiments. The concept was evaluated in the context of signal processing. We performed classification tasks on a synthetic dataset, and real electrocardiogram (ECG) signals. Compared to fully-connected and 1D convolutional networks, VPNet features fast learning ability and good accuracy at a low computational cost in both of the training and inference. Based on the promising results and mentioned advantages, we expect broader impact in signal processing, including classification, regression, and even clustering problems

Application of artificial intelligence techniques for automated detection of myocardial infarction: A review

Myocardial infarction (MI) results in heart muscle injury due to receiving insufficient blood flow. MI is the most common cause of mortality in middle-aged and elderly individuals around the world. To diagnose MI, clinicians need to interpret electrocardiography (ECG) signals, which requires expertise and is subject to observer bias. Artificial intelligence-based methods can be utilized to screen for or diagnose MI automatically using ECG signals. In this work, we conducted a comprehensive assessment of artificial intelligence-based approaches for MI detection based on ECG as well as other biophysical signals, including machine learning (ML) and deep learning (DL) models. The performance of traditional ML methods relies on handcrafted features and manual selection of ECG signals, whereas DL models can automate these tasks. The review observed that deep convolutional neural networks (DCNNs) yielded excellent classification performance for MI diagnosis, which explains why they have become prevalent in recent years. To our knowledge, this is the first comprehensive survey of artificial intelligence techniques employed for MI diagnosis using ECG and other biophysical signals.Comment: 16 pages, 8 figure

Models for calculating confidence intervals for neural networks

This research focused on coding and analyzing existing models to calculate confidence intervals on the results of neural networks. The three techniques for determining confidence intervals determination were the non-linear regression, the bootstrapping estimation, and the maximum likelihood estimation. Confidence intervals for non-linear regression, bootstrap estimation, and maximum likelihood were coded in Visual Basic. The neural network used the backpropagation algorithm with an input layer, one hidden layer and an output layer with one unit. The hidden layer had a logistic or binary sigmoidal activation function and the output layer had a linear activation function. These techniques were tested on various data sets with and without additional noise. Out of eight cases studied, non-linear regression and bootstrapping each had the four lowest values for the average coverage probability minus the nominal probability. For the average coverage probabilities minus the nominal probabilities of all data sets, the bootstrapping estimation obtained the lowest values. The ranges and standard deviations of the coverage probabilities over 15 simulations for the three techniques were computed, and it was observed that the non-linear regression obtained consistent results with the least range and standard deviation, and bootstrapping had the largest ranges and standard deviations. The bootstrapping estimation technique gave a slightly better average coverage probability (CP) minus nominal values than the non-linear regression method, but it had considerably more variation in individual simulations. The maximum likelihood estimation had the poorest results with respect to the average CP minus nominal values

Diseño e implementación de software de análisis para establecer los efectos de la telefonía celular sobre parámetros electrocardiográficos

En el presente trabajo de grado se diseñó e implementó un software con Matlab (MATrix LABoratory: programa de cálculo técnico y científico que permite realizar cálculos numéricos con vectores y matrices), para caracterizar automáticamente la señal electrocardiográfica mediante wavelets y obtener los valores promedios de las duraciones (tiempos) y amplitudes de los diferentes segmentos, intervalos y ondas inherentes a la señal ECG. Esta herramienta le facilitará al grupo de Electrofisiología de la universidad Tecnológica de Pereira (U.T.P.) investigar y analizar los posibles efectos de la telefonía celular sobre parámetros electrocardiográficos, con base en su proyecto denominado: "Telefonía celular, medio ambiente y salud pública". Para verificar el correcto funcionamiento de la aplicación, se tomaron registros ECG con un electrocardiógrafo profesional a personas aparentemente sanas sin ninguna patología cardiaca, inicialmente, sin exponerlas directamente a las radiaciones procedentes de los celulares y posteriormente, ubicando estos dispositivos electrónicos cerca al corazón de estos voluntarios, en diferentes estados o fases: repicando, con la llamada establecida y finalizada la conversación; este procedimiento se llevó a cabo con base en el protocolo de investigación que realizará el grupo de Electrofisiología de la U.T.P. Estos registros previamente se almacenaron y con base en un software adicional se convirtieron a archivos *.mat, para que la aplicación los interpretara y realizara la respectiva caracterización, proporcionando las diversas medidas derivadas de la señal electrocardiográfica. Mediante una interfaz gráfica (GUI), se le facilitará al usuario el manejo de los registros y la visualización de los resultados con base en ventanas que se despliegan individualmente, dependiendo de las opciones que se seleccionen y ejecuten

IMAGE UNDERSTANDING OF MOLAR PREGNANCY BASED ON ANOMALIES DETECTION

Cancer occurs when normal cells grow and multiply without normal control. As the cells multiply, they form an area of abnormal cells, known as a tumour. Many tumours exhibit abnormal chromosomal segregation at cell division. These anomalies play an important role in detecting molar pregnancy cancer. Molar pregnancy, also known as hydatidiform mole, can be categorised into partial (PHM) and complete (CHM) mole, persistent gestational trophoblastic and choriocarcinoma. Hydatidiform moles are most commonly found in women under the age of 17 or over the age of 35. Hydatidiform moles can be detected by morphological and histopathological examination. Even experienced pathologists cannot easily classify between complete and partial hydatidiform moles. However, the distinction between complete and partial hydatidiform moles is important in order to recommend the appropriate treatment method. Therefore, research into molar pregnancy image analysis and understanding is critical. The hypothesis of this research project is that an anomaly detection approach to analyse molar pregnancy images can improve image analysis and classification of normal PHM and CHM villi. The primary aim of this research project is to develop a novel method, based on anomaly detection, to identify and classify anomalous villi in molar pregnancy stained images. The novel method is developed to simulate expert pathologists’ approach in diagnosis of anomalous villi. The knowledge and heuristics elicited from two expert pathologists are combined with the morphological domain knowledge of molar pregnancy, to develop a heuristic multi-neural network architecture designed to classify the villi into their appropriated anomalous types. This study confirmed that a single feature cannot give enough discriminative power for villi classification. Whereas expert pathologists consider the size and shape before textural features, this thesis demonstrated that the textural feature has a higher discriminative power than size and shape. The first heuristic-based multi-neural network, which was based on 15 elicited features, achieved an improved average accuracy of 81.2%, compared to the traditional multi-layer perceptron (80.5%); however, the recall of CHM villi class was still low (64.3%). Two further textural features, which were elicited and added to the second heuristic-based multi-neural network, have improved the average accuracy from 81.2% to 86.1% and the recall of CHM villi class from 64.3% to 73.5%. The precision of the multi-neural network II has also increased from 82.7% to 89.5% for normal villi class, from 81.3% to 84.7% for PHM villi class and from 80.8% to 86% for CHM villi class. To support pathologists to visualise the results of the segmentation, a software tool, Hydatidiform Mole Analysis Tool (HYMAT), was developed compiling the morphological and pathological data for each villus analysis

What is the added value of using non-linear models to explore complex healthcare datasets?

Health care is a complex system and it is therefore expected to behave in a non-linear manner. It is important for the delivery of health interventions to patients that the best possible analysis of available data is undertaken. Many of the conventional models used for health care data are linear. This research compares the performance of linear models with non-linear models for two health care data sets of complex interventions. Logistic regression, latent class analysis and a classification artificial neural network were each used to model outcomes for patients using data from a randomised controlled trial of a cognitive behavioural complex intervention for non-specific low back pain. A Cox proportional hazards model and an artificial neural network were used to model survival and the hazards for different sub-groups of patients using an observational study of a cardiovascular rehabilitation complex intervention. The artificial neural network and an ordinary logistic regression were more accurate in classifying patient recovery from back pain than a logistic regression on latent class membership. The most sensitive models were the artificial neural network and the latent class logistic regression. The best overall performance was the artificial neural network, providing both sensitivity and accuracy. Survival was modelled equally well by the Cox model and the artificial neural network, when compared to the empirical Kaplan-Meier survival curve. Long term survival for the cardiovascular patients was strongly associated with secondary prevention medications, and fitness was also important. Moreover, improvement in fitness during the rehabilitation period to a fairly modest 'high fitness' category was as advantageous for long-term survival as having achieved that same level of fitness by the beginning of the rehabilitation period. Having adjusted for fitness, BMI was not a predictor of long term survival after a cardiac event or procedure. The Cox proportional hazards model was constrained by its assumptions to produce hazard trajectories proportional to the baseline hazard. The artificial neural network model produced hazard trajectories that vary, giving rise to hypotheses about how the predictors of survival interact in their influence on the hazard. The artificial neural network, an exemplar non-linear model, has been shown to match or exceed the capability of conventional models in the analysis of complex health care data sets