scholarly journals Electrocardio Panorama: Synthesizing New ECG views with Self-supervision

2021 ◽  
Author(s):  
Jintai Chen ◽  
Xiangshang Zheng ◽  
Hongyun Yu ◽  
Danny Z. Chen ◽  
Jian Wu
Keyword(s):  
Clinical Information ◽  
Field Representation ◽  
Ecg Signals ◽  
Field Information ◽  
View Transformation ◽  
Electrocardiogram Ecg ◽  
Sparse Set ◽  
First Time ◽  
Field Network ◽  
Lead Positioning

Multi-lead electrocardiogram (ECG) provides clinical information of heartbeats from several fixed viewpoints determined by the lead positioning. However, it is often not satisfactory to visualize ECG signals in these fixed and limited views, as some clinically useful information is represented only from a few specific ECG viewpoints. For the first time, we propose a new concept, Electrocardio Panorama, which allows visualizing ECG signals from any queried viewpoints. To build Electrocardio Panorama, we assume that an underlying electrocardio field exists, representing locations, magnitudes, and directions of ECG signals. We present a Neural electrocardio field Network (Nef-Net), which first predicts the electrocardio field representation by using a sparse set of one or few input ECG views and then synthesizes Electrocardio Panorama based on the predicted representations. Specially, to better disentangle electrocardio field information from viewpoint biases, a new Angular Encoding is proposed to process viewpoint angles. Also, we propose a self-supervised learning approach called Standin Learning, which helps model the electrocardio field without direct supervision. Further, with very few modifications, Nef-Net can synthesize ECG signals from scratch. Experiments verify that our Nef-Net performs well on Electrocardio Panorama synthesis, and outperforms the previous work on the auxiliary tasks (ECG view transformation and ECG synthesis from scratch). The codes and the division labels of cardiac cycles and ECG deflections on Tianchi ECG and PTB datasets are available at https://github.com/WhatAShot/Electrocardio-Panorama.

Information-based analysis of the coupling between brain and heart reactions to olfactory stimulation

2021 ◽  
pp. 1-11
Author(s):  
Najmeh Pakniyat ◽  
Mohammad Hossein Babini ◽  
Vladimir V. Kulish ◽  
Hamidreza Namazi
Keyword(s):  
Time Series ◽  
Biomedical Science ◽  
Strongly Correlated ◽  
Eeg Signals ◽  
Ecg Signals ◽  
Olfactory Stimuli ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Electroencephalogram Eeg ◽  
The Brain

BACKGROUND: Analysis of the heart activity is one of the important areas of research in biomedical science and engineering. For this purpose, scientists analyze the activity of the heart in various conditions. Since the brain controls the heart’s activity, a relationship should exist among their activities. OBJECTIVE: In this research, for the first time the coupling between heart and brain activities was analyzed by information-based analysis. METHODS: Considering Shannon entropy as the indicator of the information of a system, we recorded electroencephalogram (EEG) and electrocardiogram (ECG) signals of 13 participants (7 M, 6 F, 18–22 years old) in different external stimulations (using pineapple, banana, vanilla, and lemon flavors as olfactory stimuli) and evaluated how the information of EEG signals and R-R time series (as heart rate variability (HRV)) are linked. RESULTS: The results indicate that the changes in the information of the R-R time series and EEG signals are strongly correlated (ρ=-0.9566). CONCLUSION: We conclude that heart and brain activities are related.

scholarly journals Selection of Optimal Parameters for ECG Signal Smoothing and Baseline Drift Removal

2014 ◽  
Vol 7 (4) ◽  
pp. 99 ◽  
Author(s):  
Dejan Stantic ◽  
Jun Jo
Keyword(s):  
Clinical Information ◽  
Ecg Signal ◽  
Decomposition Level ◽  
Baseline Drift ◽  
Ecg Signals ◽  
Optimal Value ◽  
Ecg Signal Processing ◽  
Baseline Wander ◽  
Electrocardiogram Ecg ◽  
Selection Of

Electrocardiogram (ECG) contains crucial clinical information about the cardiac activities of the heart, however, such signal a part of being in large volume is often characterised by a low quality due to the noise and other artifacts. In order to correctly extract the important features from the ECG signal, first it needs to be preprocessed, denoised and normilised. Significant attention in the literature has been directed toward the ECG preprocessing, though there are ambiguity to which wavelet performs the best for ECG signal processing as well as which decomposition level should be used and how the baseline wander can be removed. Parameters of wavelets have been investigated but the lack of evidence for recommendations is not found. This research conducts a comprehensive study to identify some characteristics of optimal decomposition level and to identify the span that should be used. We have taken into consideration all available wavelets within the Matlab environment and tested it on a number of randomly chosen ECG signals. Results indicate that the decomposition level of 4 should be used and that the Biorthogonal wavelet bior3.9 performs the best for smoothing and baseline drift removal. Also, we concluded that the optimal value for span is 100, which guarantees the best baseline wander removal. 

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

Antiticiombin III Deficiency In A Large Family with 41 Affected Members

1981 ◽  
Author(s):  
J Stibbe ◽  
S Adhin ◽  
G L Ong ◽  
R S Panday ◽  
S H Peters ◽  
...  
Keyword(s):  
Autosomal Dominant ◽  
Large Family ◽  
Clinical Information ◽  
Impedance Plethysmography ◽  
Age Group ◽  
Oral Contraceptive Pills ◽  
Contraceptive Pills ◽  
At Iii ◽  
L 51 ◽  
First Time

Hereditary Antithrombin III (AT III) deficiency was found in a large Hindustani family, living partly in the Netherlands, partly in Suriname. Of 201 members investigated 35 were found to affected: AT III activity (chromogenic substrate) and AT III antigen (immuno-electrophoresis according to Laurell) were about 45 %. Analysis of this fanily clearly demonstrated the autosomal dominant inheritance of the condition. Six non-investigated members (1 living, 5 non-living) were diagnosed as being affected on the basis of affected offspring.Seventeen affected members had no signs of thrombo-embolic(TE) processes (age group 0-10 years old, n=2; 11-20, n=5; 21-30, n=4; 31-40, n=4; 41-50, n=2). Thirteen showed clinical or proven signs of TE processes (first time in age group 0-10 years old, n=0; 11-20, n=l; 21-30, n=G; 31-40, n=4; 41-50,n=l; 51-60, n=0; 61-70, n=l). No clinical information is yet available on the remaining affected members. Deep venous thrombosis (DVT) occurred in 9 patients (age group 21-30, n=5; 31-40, n=3; 61-70, n=l). Triggering factors were none 4, surgery 1, oral contraceptives and preg- nancy4. Pulmonary embolism occurred in 6 patients (2 clinical, 4 proven) and was fatal in 4; ages were 19, 21, 26, 37, 48 and 68 years old. Pregnancy was uncomplicated in 3 women (total of 4 pregnancies), one of these women was treated prophylactically with anticoagulants during pregnancy (1 pregnancy). Two women (9 pregnancies) had a thrombotic episode (1st and 3rd pregnancy respectively) and 1 woman died suddenly 7 days after her 7th childbirth. DVT occurred in 2 of 4 women who used oral contraceptive pills.In some symptomless patients (age 22, 26, 32, 33, 40 years old) impedance plethysmography (n=5), 125I-fibrinogen leg scanning (n=3), 125I-fibrinogen T½(n=3) and 51C-platelet survival (n=l) were normal

scholarly journals A new wearable multichannel magnetocardiogram system with a SERF atomic magnetometer array

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanfei Yang ◽  
Mingzhu Xu ◽  
Aimin Liang ◽  
Yan Yin ◽  
Xin Ma ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Data Acquisition ◽  
Current Distribution ◽  
Spin Exchange ◽  
Healthy Human ◽  
Ecg Signals ◽  
Mode Decomposition ◽  
Magnetometer Array ◽  
Data Acquisition And Processing ◽  
Electrocardiogram Ecg

AbstractIn this study, a wearable multichannel human magnetocardiogram (MCG) system based on a spin exchange relaxation-free regime (SERF) magnetometer array is developed. The MCG system consists of a magnetically shielded device, a wearable SERF magnetometer array, and a computer for data acquisition and processing. Multichannel MCG signals from a healthy human are successfully recorded simultaneously. Independent component analysis (ICA) and empirical mode decomposition (EMD) are used to denoise MCG data. MCG imaging is realized to visualize the magnetic and current distribution around the heart. The validity of the MCG signals detected by the system is verified by electrocardiogram (ECG) signals obtained at the same position, and similar features and intervals of cardiac signal waveform appear on both MCG and ECG. Experiments show that our wearable MCG system is reliable for detecting MCG signals and can provide cardiac electromagnetic activity imaging.

scholarly journals Novel Stable Capacitive Electrocardiogram Measurement System

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3668
Author(s):  
Chi-Chun Chen ◽  
Shu-Yu Lin ◽  
Wen-Ying Chang
Keyword(s):  
Measurement System ◽  
Input Resistance ◽  
Voltage Divider ◽  
Motion Artifact ◽  
Guard Ring ◽  
Capacitive Sensing ◽  
Ecg Signals ◽  
Optimal Input ◽  
Electrocardiogram Ecg ◽  
Mode Interference

This study presents a noncontact electrocardiogram (ECG) measurement system to replace conventional ECG electrode pads during ECG measurement. The proposed noncontact electrode design comprises a surface guard ring, the optimal input resistance, a ground guard ring, and an optimal voltage divider feedback. The surface and ground guard rings are used to reduce environmental noise. The optimal input resistor mitigates distortion caused by the input bias current, and the optimal voltage divider feedback increases the gain. Simulated gain analysis was subsequently performed to determine the most suitable parameters for the design, and the system was combined with a capacitive driven right leg circuit to reduce common-mode interference. The present study simulated actual environments in which interference is present in capacitive ECG signal measurement. Both in the case of environmental interference and motion artifact interference, relative to capacitive ECG electrodes, the proposed electrodes measured ECG signals with greater stability. In terms of R–R intervals, the measured ECG signals exhibited a 98.6% similarity to ECGs measured using contact ECG systems. The proposed noncontact ECG measurement system based on capacitive sensing is applicable for use in everyday life.

scholarly journals Data Independent Acquisition Based Bi-Directional Deep Networks for Biometric ECG Authentication

2021 ◽  
Vol 11 (3) ◽  
pp. 1125
Author(s):  
Htet Myet Lynn ◽  
Pankoo Kim ◽  
Sung Bum Pan
Keyword(s):  
Short Term Memory ◽  
Feature Learning ◽  
Biometric Authentication ◽  
Qrs Complex ◽  
Data Types ◽  
Ecg Signals ◽  
Data Independent Acquisition ◽  
Deep Networks ◽  
Classification Tasks ◽  
Electrocardiogram Ecg

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.

scholarly journals Simultaneous Voltage and Current Measurement Instrumentation Amplifier for ECG and PPG Monitoring

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 679
Author(s):  
Jongpal Kim
Keyword(s):  
Integrated Circuit ◽  
Cardiovascular Health ◽  
Current Noise ◽  
Instrumentation Amplifier ◽  
Current Sensing ◽  
Application Specific Integrated Circuit ◽  
Time Division ◽  
Electrocardiogram Ecg ◽  
First Time ◽  
Application Specific

An instrumentation amplifier (IA) capable of sensing both voltage and current at the same time has been introduced and applied to electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for cardiovascular health monitoring applications. The proposed IA can switch between the voltage and current sensing configurations in a time–division manner faster than the ECG and PPG bandwidths. The application-specific integrated circuit (ASIC) of the proposed circuit design was implemented using 180 nm CMOS fabrication technology. Input-referred voltage noise and current noise were measured as 3.9 µVrms and 172 pArms, respectively, and power consumption was measured as 34.9 µA. In the current sensing configuration, a current noise reduction technique is applied, which was confirmed to be a 25 times improvement over the previous version. Using a single IA, ECG and PPG can be monitored in the form of separated ECG and PPG signals. In addition, for the first time, a merged ECG/PPG signal is acquired, which has features of both ECG and PPG peaks.

scholarly journals ECG Beats Classification Using Mixture of Features

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Manab Kumar Das ◽  
Samit Ari
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Ectopic Beat ◽  
Extraction Techniques ◽  
Efficient System ◽  
Ecg Signals ◽  
Temporal Features ◽  
S Transform ◽  
Electrocardiogram Ecg

Classification of electrocardiogram (ECG) signals plays an important role in clinical diagnosis of heart disease. This paper proposes the design of an efficient system for classification of the normal beat (N), ventricular ectopic beat (V), supraventricular ectopic beat (S), fusion beat (F), and unknown beat (Q) using a mixture of features. In this paper, two different feature extraction methods are proposed for classification of ECG beats: (i) S-transform based features along with temporal features and (ii) mixture of ST and WT based features along with temporal features. The extracted feature set is independently classified using multilayer perceptron neural network (MLPNN). The performances are evaluated on several normal and abnormal ECG signals from 44 recordings of the MIT-BIH arrhythmia database. In this work, the performances of three feature extraction techniques with MLP-NN classifier are compared using five classes of ECG beat recommended by AAMI (Association for the Advancement of Medical Instrumentation) standards. The average sensitivity performances of the proposed feature extraction technique for N, S, F, V, and Q are 95.70%, 78.05%, 49.60%, 89.68%, and 33.89%, respectively. The experimental results demonstrate that the proposed feature extraction techniques show better performances compared to other existing features extraction techniques.

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