5,205 research outputs found
Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar
To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms
Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest
The automatic detection of pulse during out-of-hospital cardiac arrest (OHCA) is necessary for the early recognition of the arrest and the detection of return of spontaneous circulation (end of the arrest). The only signal available in every single defibrillator and valid for the detection of pulse is the electrocardiogram (ECG). In this study we propose two deep neural network (DNN) architectures to detect pulse using short ECG segments (5 s), i.e., to classify the rhythm into pulseless electrical activity (PEA) or pulse-generating rhythm (PR). A total of 3914 5-s ECG segments, 2372 PR and 1542 PEA, were extracted from 279 OHCA episodes. Data were partitioned patient-wise into training (80%) and test (20%) sets. The first DNN architecture was a fully convolutional neural network, and the second architecture added a recurrent layer to learn temporal dependencies. Both DNN architectures were tuned using Bayesian optimization, and the results for the test set were compared to state-of-the art PR/PEA discrimination algorithms based on machine learning and hand crafted features. The PR/PEA classifiers were evaluated in terms of sensitivity (Se) for PR, specificity (Sp) for PEA, and the balanced accuracy (BAC), the average of Se and Sp. The Se/Sp/BAC of the DNN architectures were 94.1%/92.9%/93.5% for the first one, and 95.5%/91.6%/93.5% for the second one. Both architectures improved the performance of state of the art methods by more than 1.5 points in BAC.This work was supported by: The Spanish Ministerio de Economía y Competitividad, TEC2015-64678-R,
jointly with the Fondo Europeo de Desarrollo Regional (FEDER), UPV/EHU via GIU17/031 and the Basque
Government through the grant PRE_2018_2_0260
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
The electrocardiogram (ECG) is one of the most extensively employed signals
used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG
signals can capture the heart's rhythmic irregularities, commonly known as
arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of
patients' acute and chronic heart conditions. In this study, we propose a
two-dimensional (2-D) convolutional neural network (CNN) model for the
classification of ECG signals into eight classes; namely, normal beat,
premature ventricular contraction beat, paced beat, right bundle branch block
beat, left bundle branch block beat, atrial premature contraction beat,
ventricular flutter wave beat, and ventricular escape beat. The one-dimensional
ECG time series signals are transformed into 2-D spectrograms through
short-time Fourier transform. The 2-D CNN model consisting of four
convolutional layers and four pooling layers is designed for extracting robust
features from the input spectrograms. Our proposed methodology is evaluated on
a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art
average classification accuracy of 99.11\%, which is better than those of
recently reported results in classifying similar types of arrhythmias. The
performance is significant in other indices as well, including sensitivity and
specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote
Sensing MDPI Journa
Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks
The effectiveness of biosignal generation and data augmentation with
biosignal generative models based on generative adversarial networks (GANs),
which are a type of deep learning technique, was demonstrated in our previous
paper. GAN-based generative models only learn the projection between a random
distribution as input data and the distribution of training data.Therefore, the
relationship between input and generated data is unclear, and the
characteristics of the data generated from this model cannot be controlled.
This study proposes a method for generating time-series data based on GANs and
explores their ability to generate biosignals with certain classes and
characteristics. Moreover, in the proposed method, latent variables are
analyzed using canonical correlation analysis (CCA) to represent the
relationship between input and generated data as canonical loadings. Using
these loadings, we can control the characteristics of the data generated by the
proposed method. The influence of class labels on generated data is analyzed by
feeding the data interpolated between two class labels into the generator of
the proposed GANs. The CCA of the latent variables is shown to be an effective
method of controlling the generated data characteristics. We are able to model
the distribution of the time-series data without requiring domain-dependent
knowledge using the proposed method. Furthermore, it is possible to control the
characteristics of these data by analyzing the model trained using the proposed
method. To the best of our knowledge, this work is the first to generate
biosignals using GANs while controlling the characteristics of the generated
data
ECGadv: Generating Adversarial Electrocardiogram to Misguide Arrhythmia Classification System
Deep neural networks (DNNs)-powered Electrocardiogram (ECG) diagnosis systems
recently achieve promising progress to take over tedious examinations by
cardiologists. However, their vulnerability to adversarial attacks still lack
comprehensive investigation. The existing attacks in image domain could not be
directly applicable due to the distinct properties of ECGs in visualization and
dynamic properties. Thus, this paper takes a step to thoroughly explore
adversarial attacks on the DNN-powered ECG diagnosis system. We analyze the
properties of ECGs to design effective attacks schemes under two attacks models
respectively. Our results demonstrate the blind spots of DNN-powered diagnosis
systems under adversarial attacks, which calls attention to adequate
countermeasures.Comment: Accepted by AAAI 202
Deep Generative Models: The winning key for large and easily accessible ECG datasets?
Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored
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