Compressive Sensing of Multichannel Electrocardiogram Signals in Wireless Telehealth System

2016 ◽  
Vol 25 (09) ◽  
pp. 1650103 ◽  
Author(s):  
Jing Hua ◽  
Hua Zhang ◽  
Jizhong Liu ◽  
Junlong Zhou
Keyword(s):  
Compressive Sensing ◽  
Single Channel ◽  
Temporal Correlation ◽  
Reconstruction Algorithm ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Reconstruction Performance ◽  
Low Energy Consumption ◽  
Spatio Temporal ◽  
Electrocardiogram Ecg

Due to the capacity of compressing and recovering signal with low energy consumption, compressive sensing (CS) has drawn considerable attention in wireless telemonitoring of electrocardiogram (ECG) signals. However, most existing CS methods are designed for reconstructing single channel signal, and hence difficult to reconstruct multichannel ECG signals. In this paper, a spatio-temporal sparse model-based algorithm is proposed for the reconstruction of multichannel ECG signals by not only exploiting the temporal correlation in each individual channel signal, but also the spatial correlation among signals from different channels. In addition, a dictionary learning (DL) approach is developed to enhance the performance of the proposed reconstruction algorithm by using the sparsity of ECG signals in some transformed domain. The approach determines a dictionary by learning local dictionaries for each channel and merging them to form a global dictionary. Extensive simulations were performed to validate the proposed algorithms. Simulation results show that the proposed reconstruction algorithm has a better performance in recovering multichannel ECG signals as compared to the benchmarking methods. Moreover, the reconstruction performance of the algorithm can be further improved by using a dictionary matrix, which is obtained from the proposed DL algorithm.

scholarly journals Capturing Electrocardiogram Signals from Chairs by Multiple Capacitively Coupled Unipolar Electrodes

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2835 ◽  
Author(s):  
Zhongjie Hou ◽  
Jinxi Xiang ◽  
Yonggui Dong ◽  
Xiaohui Xue ◽  
Hao Xiong ◽  
...  
Keyword(s):  
Printed Circuit Board ◽  
Experimental Tests ◽  
Circuit Board ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Capacitively Coupled ◽  
Ecg Signals ◽  
Printed Circuit ◽  
Electrocardiogram Signals ◽  
Electrocardiogram Ecg

A prototype of an electrocardiogram (ECG) signal acquisition system with multiple unipolar capacitively coupled electrodes is designed and experimentally tested. Capacitively coupled electrodes made of a standard printed circuit board (PCB) are used as the sensing electrodes. Different from the conventional measurement schematics, where one single lead ECG signal is acquired from a pair of sensing electrodes, the sensing electrodes in our approaches operate in a unipolar mode, i.e., the biopotential signals picked up by each sensing electrodes are amplified and sampled separately. Four unipolar electrodes are mounted on the backrest of a regular chair and therefore four channel of signals containing ECG information are sampled and processed. It is found that the qualities of ECG signal contained in the four channel are different from each other. In order to pick up the ECG signal, an index for quality evaluation, as well as for aggregation of multiple signals, is proposed based on phase space reconstruction. Experimental tests are carried out while subjects sitting on the chair and clothed. The results indicate that the ECG signals can be reliably obtained in such a unipolar way.

scholarly journals Intelligent Deep Models Based on Scalograms of Electrocardiogram Signals for Biometrics

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 935 ◽  
Author(s):  
Yeong-Hyeon Byeon ◽  
Sung-Bum Pan ◽  
Keun-Chang Kwak
Keyword(s):  
Frequency Domain ◽  
Training Transfer ◽  
Training Data ◽  
Deep Convolutional Neural Networks ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Noisy Signals ◽  
The Time Domain ◽  
Electrocardiogram Ecg

This paper conducts a comparative analysis of deep models in biometrics using scalogram of electrocardiogram (ECG). A scalogram is the absolute value of the continuous wavelet transform coefficients of a signal. Since biometrics using ECG signals are sensitive to noise, studies have been conducted by transforming signals into a frequency domain that is efficient for analyzing noisy signals. By transforming the signal from the time domain to the frequency domain using the wavelet, the 1-D signal becomes a 2-D matrix, and it could be analyzed at multiresolution. However, this process makes signal analysis morphologically complex. This means that existing simple classifiers could perform poorly. We investigate the possibility of using the scalogram of ECG as input to deep convolutional neural networks of deep learning, which exhibit optimal performance for the classification of morphological imagery. When training data is small or hardware is insufficient for training, transfer learning can be used with pretrained deep models to reduce learning time, and classify it well enough. In this paper, AlexNet, GoogLeNet, and ResNet are considered as deep models of convolutional neural network. The experiments are performed on two databases for performance evaluation. Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, while Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The ResNet was 0.73%—0.27% higher than AlexNet or GoogLeNet on PTB-ECG—and the ResNet was 0.94%—0.12% higher than AlexNet or GoogLeNet on CU-ECG.

scholarly journals Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

Information ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 63
Author(s):  
Fatima Sajid Butt ◽  
Luigi La Blunda ◽  
Matthias F. Wagner ◽  
Jörg Schäfer ◽  
Inmaculada Medina-Bulo ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Fall Detection ◽  
Daily Activities ◽  
Continuous Wavelet ◽  
Learning Approaches ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Novel Approach ◽  
The Neural Network ◽  
Electrocardiogram Ecg

Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

scholarly journals Method of Extracting Fetal QRS for Single-Lead Abdominal ECG Signal Based On Combined Compressive Sensing and Clustering Analysis

2021 ◽  
Author(s):  
Yapeng Dong ◽  
Wenbo Ju ◽  
Jiancheng Hu ◽  
Lijuan Hu ◽  
Dayu Ding ◽  
...  
Keyword(s):  
Low Power ◽  
Compressive Sensing ◽  
Clustering Analysis ◽  
Single Channel ◽  
Reducing Power ◽  
Motion Artifacts ◽  
Data Sets ◽  
Non Invasive ◽  
Fetal Ecg ◽  
Electrocardiogram Ecg

Abstract Objective: The fetal electrocardiogram (ECG) is an objective index that reflects a fetus’s health status. Non-invasive abdominal ECG (aECG) was obtained by placing silicone electrodes on pregnant women’s abdominal wall. However, fetal QRS (fQRS) extraction is very challenging due to maternal ECG interference, motion artifacts, and other noise. Approach: This paper introduces a new single-lead non-invasive fQRS extraction method based on compressive sensing and clustering analysis. This method can be applied to portable, low-power remote fetal ECG (fECG) acquisition equipment based on the Internet of Things (IoT). It is mainly divided into the following steps: (1) optimal component extraction of single-channel signal based on compressive sensing theory; (2) location of maternal QRS (mQRS) using the clustering method based on extreme value; (3) maternal ECG (mECG) elimination; (4) The preliminary location of fQRS based on double clustering and the correction of fQRS based on fetal RR interval. Main results: The new algorithm proposed in this paper is verified on two publicly available data sets. The averages of these indicators are Se=98.53%, PPV=98.28%, ACC=96.95%, F1=98.43% for the Silesia datasets and Se=97.59%, PPV=97.63%, ACC=95.44%, F1=97.62% for the Challenge datasets A. Significance: The results show that it is feasible and reliable to locate fQRS from a single-channel aECG signal under the condition of reducing power consumption. It lays a foundation for implementing the low-power wireless transmission of fECG signal and remote fetal heart rate (FHR) monitoring based on the IoT.

scholarly journals Real-Time Concept Drift Detection and Its Application to ECG Data

2021 ◽  
Vol 17 (10) ◽  
pp. 160
Author(s):  
Ketan Sanjay Desale ◽  
Swati Shinde
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Model Performance ◽  
Adaptive Mechanism ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Concept Drift Detection ◽  
Electrocardiogram Ecg

Prediction of cardiac disease is one the most crucial topics in the sector of medical info evaluation. The stochastic nature and the variation concerning time in electrocardiogram (ECG) signals make it burdensome to investigate its characteristics. Being evolving in nature, it requires a dynamic predictive model. With the presence of concept drift, the model performance will get worse. Thus learning algorithms require an apt adaptive mechanism to accurately handle the drifting data streams. This paper proposes an inceptive approach, Corazon Concept Drift Detection Method (Corazon CDDM), to detect drifts and adapt to them in real-time in electrocardiogram signals. The proposed methodology results in achieving competitive results compared to the methods proposed in the literature for all types of datasets like synthetic, real-world & time-series datasets.

scholarly journals A Competitive Structure of Convolutional Autoencoder Networks for Electrocardiogram Signals Classification

2018 ◽  
Author(s):  
Alexandre Farias Baia ◽  
Adriana Rosa Garcez Castro
Keyword(s):  
Classification System ◽  
Parallel Structure ◽  
System Testing ◽  
Ecg Signals ◽  
Electrocardiogram Signals ◽  
Convolutional Autoencoder ◽  
Electrocardiogram Ecg ◽  
Competitive Structure

This paper presents the proposal of an electrocardiogram (ECG) signals classification system through a competitive structure of Convolutional Autoencoders (CAE). Two Convolutional Autoencoders were trained to reconstruct ECG signals for the cases of patients with arrhythmia and patients with signals considered normals. After the training, the two networks were arranged in a competitive parallel structure to classify these signals. For the development and testing of the system, the MIT-BIH Arrhythmia Database of ECG signals was used. An accuracy of 88,9% was achieved considering the database used for system testing.

scholarly journals The validity of a person’s authentication using an electrocardiogram with a limited number of channels

2020 ◽  
Vol 2 ◽  
pp. 43-50
Author(s):  
V.V. Vyshnevskyi ◽  
◽  
T.N. Romanenko ◽  
Yu.O. Lugovskyi ◽  
◽  
...  
Keyword(s):  
Phase Space ◽  
Single Channel ◽  
Three Dimensional ◽  
Qrs Complex ◽  
Ecg Signals ◽  
Dimensional Phase Space ◽  
Number Of Patients ◽  
Ecg Leads ◽  
Electrocardiogram Ecg ◽  
Synthetic Phase

The concept of mobile and home telemedicine for screening and early diagnostics of cardiovascular dis-eases is being expanded due to the emergence of mobile diagnostic devices and smartphones. In the course of such telemedicine consultations, the doctor must be sure that the digital electrocardiogram (ECG) be-longs to the patient who was registered. Both multi-channel and single-channel ECG-recording devices are available on the telemedicine consulting market now. Single-channel electrocardiographs are more eco-nomic feasible for home use. Previously, the authors have developed and experimentally tested the algo-rithms for patient authentication by his/her multi-channel ECG. These algorithms are based on the analysis of the shape of QRS complex in three-dimensional phase space of coordinates. Therefore, it is reasonable to adapt these algorithms to single-channel ECG. In case of multi-channel ECG, we can construct a three-dimensional phase space of coordinates by obtaining all the necessary data from the ECG leads. In a case of the single-channel ECG it is necessary to create two additional signals artificially and then it will be possible to form a synthetic phase space. In general, the question of the validity of biometric person au-thentication algorithms by his/her ECG with a limited number of channels is discussed in this paper. Be-sides the algorithms for solving the problem of authentication, the comparison of sensitivity and specificity indicators, calculated on the results of experiments for multi-channel and single-channel ECG, are also given in this paper. The results of experiments with multi-channel and single-channel ECG of a larger number of patients are given in comparison to the previous experiments. The results of the experiments for the case of recording ECG signals by different devices are given as well.

A Fourier-Bessel Expansion-Based Method for Automated Detection of Atrial Fibrillation From Electrocardiogram Signals

2019 ◽  
pp. 248-277
Author(s):  
Ashish Sharma ◽  
Shivnarayan Patidar
Keyword(s):  
Atrial Fibrillation ◽  
Ecg Signal ◽  
Clinical Environment ◽  
Test Dataset ◽  
Ecg Signals ◽  
Rr Interval ◽  
Electrocardiogram Signals ◽  
Detection And Identification ◽  
Bagged Decision Trees ◽  
Electrocardiogram Ecg

This chapter presents a new methodology for detection and identification of cardiovascular diseases from a single-lead electrocardiogram (ECG) signal of short duration. More specifically, this method deals with the detection of the most common cardiac arrhythmia called atrial fibrillation (AF) in noisy and non-clinical environment. The method begins with appropriate pre-processing of ECG signals in order to get the RR-interval and heart rate (HR) signals from them. A set of indirect features are computed from the original and the transformed versions of RR-interval and HR signals along with a set of direct features that are obtained from ECG signals themselves. In all, 47 features are computed and subsequently they are fed to an ensemble system of bagged decision trees for classifying the ECG recordings into four different classes. The proposed method has been evaluated with 2017 PhysioNet/CinC challenge hidden test dataset (phase II subset) and the final F1 score of 0.81 is obtained.

scholarly journals GPS Sparse Multipath Signal Estimation Based on Compressive Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Huiping Qin ◽  
Xiaojuan Xie
Keyword(s):  
Compressive Sensing ◽  
Estimation Error ◽  
Estimation Method ◽  
Reconstruction Algorithm ◽  
Estimation Methods ◽  
Signal Estimation ◽  
Reconstruction Algorithms ◽  
Modified Newton Method ◽  
Reconstruction Performance ◽  
Multipath Signal

A GPS sparse multipath signal estimation method based on compressive sensing is proposed. A new 0 norm approximation function is designed, and the parameter of the approximate function is gradually reduced to realize the approximation of 0 norm. The sparse signal is reconstructed by a modified Newton method. The reconstruction performance of the proposed algorithm is better than several commonly reconstruction algorithms at different sparse numbers and noise intensities. The GPS sparse multipath signal model is established, and the sparse multipath signal is estimated by the proposed reconstruction algorithm in this paper. Compared with several commonly used estimation methods, the estimation error of the proposed method is lower.

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