ECG beat classification using a cost sensitive classifier

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems

Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%)

Optimal Multi-Stage Arrhythmia Classification Approach

Arrhythmia constitutes a problem with the rate or rhythm of the heartbeat, and an early diagnosis is essential for the timely inception of successful treatment. We have jointly optimized the entire multi-stage arrhythmia classification scheme based on 12-lead surface ECGs that attains the accuracy performance level of professional cardiologists. The new approach is comprised of a three-step noise reduction stage, a novel feature extraction method and an optimal classification model with finely tuned hyperparameters. We carried out an exhaustive study comparing thousands of competing classification algorithms that were trained on our proprietary, large and expertly labeled dataset consisting of 12-lead ECGs from 40,258 patients with four arrhythmia classes: atrial fibrillation, general supraventricular tachycardia, sinus bradycardia and sinus rhythm including sinus irregularity rhythm. Our results show that the optimal approach consisted of Low Band Pass filter, Robust LOESS, Non Local Means smoothing, a proprietary feature extraction method based on percentiles of the empirical distribution of ratios of interval lengths and magnitudes of peaks and valleys, and Extreme Gradient Boosting Tree classifier, achieved an F1-Score of 0.988 on patients without additional cardiac conditions. The same noise reduction and feature extraction methods combined with Gradient Boosting Tree classifier achieved an F1-Score of 0.97 on patients with additional cardiac conditions. Our method achieved the highest classification accuracy (average 10-fold cross-validation F1-Score of 0.992) using an external validation data, MIT-BIH arrhythmia database. The proposed optimal multi-stage arrhythmia classification approach can dramatically benefit automatic ECG data analysis by providing cardiologist level accuracy and robust compatibility with various ECG data sources

Selective de-identification of ECGs, The

Includes bibliographical references.2022 Fall.Biometrics are often used for immigration control, business applications, civil identity, and healthcare. Biometrics can also be used for authentication, monitoring (e.g., subtle changes in biometrics may have health implications), and personalized medical concerns. Increased use of biometrics creates identity vulnerability through the exposure of personal identifiable information (PII). Hence an increasing need to not only validate but secure a patient's biometric data and identity. The latter is achieved by anonymization, or de-identification, of the PII. Using Python in collaboration with the PTB-XL ECG database from Physionet, the goal of this thesis is to create "selective de-identification." When dealing with data and de-identification, clusters, or groupings, of data with similarity of content and location in feature space are created. Classes are groupings of data with content matching that of a class definition within a given tolerance and are assigned metadata. Clusters start without derived information, i.e., metadata, that is created by intelligent algorithms, and are thus considered unstructured. Clusters are then assigned to pre-defined classes based on the features they exhibit. The goal is to focus on features that identify pathology without compromising PII. Methods to classify different pathologies are explored, and the effect on PII classification is measured. The classification scheme with the highest "gain," or (improvement in pathology classification)/ (improvement in PII classification), is deemed the preferred approach. Importantly, the process outlined can be used in many other systems involving patient recordings and diagnostic-relevant data collection

Classification of Diabetes and Cardiac Arrhythmia using Deep Learning

Master's thesis Information- and communication technology IKT591 - University of Agder 2018Deep Learning (DL) is a research area that has ourished signi cantly in the recent years and has shown remarkable potential for arti cial intelligence in the eld of medical applications. The reasons for success are the ability of DL algorithms to model high-level abstractions in the data by using automatic feature extraction property as well as signi cant amount of medical data that is available for training these algorithms. DL algorithms can learn features from a large volume of healthcare data, and then use the procured insights to assist clinical practice. We have implement DL algorithm for the classi cation of two diseases in the medical domain: Diabetes and Cardiac Arrhythmia. Diabetes is often considered as one of the world's major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in the increase in serious complications such as heart attacks and deaths. This thesis presents a Multi-Layer Feed Forward Neural Networks (MLFNN) for the classi cation of diabetes on publicly available Pima Indian Diabetes (PID) dataset. A series of experiments are conducted on this dataset with variation in learning algorithms, activation units, techniques to handle missing data and their impact on classi cation accuracy have been discussed. Finally, the results are compared with other machine learning algorithms like Na ve Bayes, Random Forest, and Logistic Regression. The achieved classi cation accuracy by MLFNN (82.5%) is the best of all the other classi ers. The term arrhythmia refers to any variation in the usual sequence of the heartbeat. There are many types of cardiac arrhythmia ranging in severity, including Premature Atrial Contractions (PACs), Atrial Fibrillation, and Premature Ventricular Contractions (PVCs). This thesis focuses on the use of DL algorithms: Convolutional Neural Network (CNN) and Long Short- Term Memory (LSTM) to classify arrhythmia with minimum possible data pre-processing on MIT-BIH Arrhythmia Database (MIT dataset). Furthermore, we study the in uence of di erent hyperparameters like L2 regularization and number of epochs on the classi cation accuracy of LSTM. We achieved a classi cation accuracy of 99.19% and 98.40% with CNN and LSTM models respectively. From our research, we believe that CNN model can assist the doctors in the classi cation of arrhythmia

Classificação automática de batidas cardíacas utilizando parâmetros de hjorth.

Este trabalho apresenta métodos para processamento de sinais de eletrocardiograma (ECG), visando realizar a classificação automática de batidas cardíacas com bom desempenho e baixo custo computacional. Em especial, uma nova abordagem para a extração de características é apresentada, na qual o sinal de ECG é caracterizado por valores de intervalos entre batidas (intervalos R-R), dados de amplitude do sinal e, principalmente, parâmetros de Hjorth. Os parâmetros de Hjorth foram utilizados anteriormente em uma variedade de áreas de pesquisa, especialmente para caracterização de sinais cerebrais, mas sua aplicação no processamento de sinal de ECG é ainda pouco explorada. Além disso, este trabalho introduz uma nova estratégia para a solução do problema de segmentação de batidas cardíacas, que evita que informações de batidas adjacentes à batida de interesse sejam levadas em consideração, aumentando o desempenho de classificação. Para o teste das técnicas propostas, utilizou-se o banco de dados norte-americano MIT-BIH de arritmias e classificadores do tipo máquina de vetor de suporte (SVM). Recomendações da Associação para o Avanço da Instrumentação Médica (AAMI) foram seguidas, de modo que o trabalho pudesse ser comparado a outros trabalhos importantes recentes. O modelo proposto apresenta índices de desempenho compatíveis ou superiores a cinco outros trabalhos de metodologia semelhante utilizados para comparação, que compõem o estado da arte nesse campo. Os resultados obtidos nos testes indicam que as técnicas propostas neste trabalho podem ser aplicadas com sucesso ao problema da classificação automática do batimento cardíaco. Além disso, esta nova abordagem tem baixo custo computacional, o que permite sua posterior implementação em dispositivos de hardware com recursos limitados, como FPGA, sistemas embarcados e circuitos integrados

Analysis of experimental ECG

Tato práce se zabývá analýzou experimentálních elektrogramů získaných z izolovaných králičích srdcí. V teoretické části práce jsou popsány principy elektrokardiografie, projevy patologické srdeční činnosti v EKG záznamu, často používané metody automatické klasifikace EKG cyklů a také informace o experimentálním výzkumu. První část praktické práce se zabývá manuální klasifikací elektrogramů a jednotlivých patologických událostí v nich zaznamenaných. Výsledky klasifikace budou použity ve veřejně dostupné databázi experimentálních elektrogramů, která nyní vzniká na UBMI VUT v Brně. Klasifikace záznamů byla konzultována s odborníky. Dále je popsán výskyt patologií v průběhu fází experimentů a dle toho zhodnocen vliv opakované ischemie na jejich vznik. Nakonec je realizována automatická klasifikace čtyř typů patologických cyklů čtyřmi klasifikačními metodami (diskriminační analýza, naivní Bayesův klasifikátor, metoda podpůrných vektorů a metoda k-nejbližších sousedů). Pro reprezentaci cyklů při klasifikaci jsou použity morfologické parametry. Celkem je z každého cyklu odvozeno 71 morfologických parametrů. Z nich jsou za pomoci testů Kruskal-Wallis a Tukey-Kramer a také analýzy hlavních komponent určeny ty, které dokáží cykly reprezentovat nejlépe.This diploma thesis deals with the analysis of experimental electrograms (EG) recorded from isolated rabbit hearts. The theoretical part is focused on the basic principles of electrocardiography, pathological events in ECGs, automatic classification of ECG and experimental cardiological research. The practical part deals with manual classification of individual pathological events – these results will be presented in the database of EG records, which is under developing at the Department of Biomedical Engineering at BUT nowadays. Manual scoring of data was discussed with experts. After that, the presence of pathological events within particular experimental periods was described and influence of ischemia on heart electrical activity was reviewed. In the last part, morphological parameters calculated from EG beats were statistically analised with Kruskal-Wallis and Tukey-Kramer tests and also principal component analysis (PCA) and used as classification features to classify automatically four types of the beats. Classification was realized with four approaches such as discriminant function analysis, k-Nearest Neighbours, support vector machines, and naive Bayes classifier.

ECG beat classification using a cost sensitive classifier

International audienceIn this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost

Wearable Wireless Devices

No abstract available