351 research outputs found

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

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    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%)

    Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

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    This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

    Presenting a New Strategy to Extract Data Clustering Heartbeat Samples by Using Discrete Wavelet Transform

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    This paper presents the improvement of detection system that normal and arrhythmia electrocardiogram classification. This classification is done to aid the ANFIS (Adaptive Neuro Fuzzy Inference System). The data used in this paper obtained from MIT-BIH normal sinus ECG database signal and MIT-BIH arrhythmia database signal. The main goal of our approach is to create an interpretable classifier that provides an acceptable accuracy. In this model, the feature extraction using DWT (Discrete Wavelet Transform) is obtained. The last stage of this extraction is introduced as the input of ANFIS model. In this paper, the ANFIS model has been trained with Quantum Behaved Particle Swarm Optimization (QPSO). In this study, for training of proposed model, four sample data have been used which result in acceleration of training data. On the test set, we achieved an outstanding sensitivity and accuracy 100%. Experimental results show that the proposed approach is very fast and accurate in improving classification. Using the proposed methodology and telemedicine technology can manage patient of heart disease

    Selective de-identification of ECGs, The

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    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

    Cardiomyopathy Detection from Electrocardiogram Features

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    Cardiomyopathy means heart (cardio) muscle (myo) disease (pathy) . Currently, cardiomyopathies are defined as myocardial disorders in which the heart muscle is structurally and/or functionally abnormal in the absence of a coronary artery disease, hypertension, valvular heart disease or congenital heart disease sufficient to cause the observed myocardial abnormalities. This book provides a comprehensive, state-of-the-art review of the current knowledge of cardiomyopathies. Instead of following the classic interdisciplinary division, the entire cardiovascular system is presented as a functional unity, and the contributors explore pathophysiological mechanisms from different perspectives, including genetics, molecular biology, electrophysiology, invasive and non-invasive cardiology, imaging methods and surgery. In order to provide a balanced medical view, this book was edited by a clinical cardiologist

    Diagnosis of Arrhythmia Using Neural Networks

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    This dissertation presents an intelligent framework for classification of heart arrhythmias. It is a framework of cascaded discrete wavelet transform and the Fourier transform as preprocessing stages for the neural network. This work exploits the information about heart activity contained in the ECG signal; the power of the wavelet and Fourier transforms in characterizing the signal and the power learningpower of neural networks. Firstly, the ECG signals are four-level discrete wavelet decomposed using a filter-bank and mother wavelet 'db2'. Then all the detailed coefficients were discarded, while retaining only the approximation coefficients at the fourth level. The retained approximation coefficients are Fourier transformed using a 16-point FFT. The FFT is symmetrical, therefore only the first 8-points are sufficient to characterize the spectrum. The last 8-points resulting from theFFTare discarded during feature selection. The 8-point feature vector is then used to train a feedforward neural network with one hidden layer of 20-units and three outputs. The neural network is trained by using the Scaled Conjugate Gradient Backpropagation algorithm (SCG). This was implemented in a MATLAB environment using the MATLAB GUINeural networktoolbox.. This approach yields an accuracy of 94.66% over three arrhythmia classes, namely the Ventricular Flutter (VFL), the Ventricular Tachycardia (VT) and the Supraventricular Tachyarrhythmia (SVTA). We conclude that for the amount of information retained and the number features used the performance is fairly competitive

    Smart-phone based electrocardiogram wavelet decomposition and neural network classification

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    This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs

    Theoretical Interpretations and Applications of Radial Basis Function Networks

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    Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains

    Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

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    Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network
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