scholarly journals Interpretation of Electrocardiogram Heartbeat by CNN and GRU

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guoliang Yao ◽  
Xiaobo Mao ◽  
Nan Li ◽  
Huaxing Xu ◽  
Xiangyang Xu ◽  
...  
Keyword(s):  
Evaluation Criteria ◽  
Original Data ◽  
Dense Layer ◽  
Ecg Signals ◽  
Unbalanced Dataset ◽  
Ectopic Beats ◽  
Local Feature Extraction ◽  
Electrocardiogram Ecg ◽  
Gated Recurrent Unit ◽  
Aided Diagnosis

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the medical field. In this paper, a classification method is proposed to classify ECG signals. Firstly, wavelet transform is used to denoise the original data, and data enhancement technology is used to overcome the problem of an unbalanced dataset. Secondly, an integrated convolutional neural network (CNN) and gated recurrent unit (GRU) classifier is proposed. The proposed network consists of a convolution layer, followed by 6 local feature extraction modules (LFEM), a GRU, and a Dense layer and a Softmax layer. Finally, the processed data were input into the CNN-GRU network into five categories: nonectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. The MIT-BIH arrhythmia database was used to evaluate the approach, and the average sensitivity, accuracy, and F1-score of the network for 5 types of ECG were 99.33%, 99.61%, and 99.42%. The evaluation criteria of the proposed method are superior to other state-of-the-art methods, and this model can be applied to wearable devices to achieve high-precision monitoring of ECG.

New technique for detection of changes in QRS morphology of ECG signals

1983 ◽  
Vol 244 (4) ◽  
pp. H560-H566
Author(s):  
S. L. Blumlein ◽  
G. Harvey ◽  
V. K. Murthy ◽  
L. J. Haywood
Keyword(s):  
Low Frequency ◽  
New Technique ◽  
Frequency Noise ◽  
Third Order ◽  
Ecg Signals ◽  
Qrs Morphology ◽  
Ectopic Beats ◽  
The Difference ◽  
A New Technique ◽  
Electrocardiogram Ecg

With the use of the electrocardiogram (ECG) as a prototype signal, a new technique was devised for detecting signals embedded in noise. Averaged "normal" digitized ECG signals formed a template to which subsequent ECG QRS complexes were compared. The difference between the averaged template signals and subsequent normal beats was white noise, whereas the difference between the template and ectopic beats consisted of nonrandom signal variation. The template to new signal comparison for the zero-, first-, second-, and third-order differences utilized an approximate F test. Accurate detection of abnormal signals associated with high- and low-frequency noise is accomplished with this method, and the practical clinical utility of the method is under study.

scholarly journals Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

2021 ◽  
Author(s):  
Murside Degirmenci ◽  
Mehmet Akif Ozdemir ◽  
Elif Izci ◽  
Aydin Akan
Keyword(s):  
Heart Rhythm ◽  
Classification Performance ◽  
Network Architectures ◽  
Test Results ◽  
Detection Scheme ◽  
Heartbeat Classification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg ◽  
Aided Diagnosis

Abstract Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias.Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. Results: The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study.Conclusions: Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.

scholarly journals An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection

2021 ◽  
Vol 12 ◽  
Author(s):  
Rahimeh Rouhi ◽  
Marianne Clausel ◽  
Julien Oster ◽  
Fabien Lauer
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rhythm ◽  
Point Of View ◽  
Compact Set ◽  
Ecg Signals ◽  
Feature Based ◽  
Electrocardiogram Ecg ◽  
The Impact ◽  
Aided Diagnosis

Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.746 compared to 0.706 for a technique based on the same features based on a cascaded SVM approach. The study also highlights how this interpretable hand-crafted feature-based model can provide cardiologists with a more compact set of features and tangible information in their diagnosis.

scholarly journals ConvNet: 1D-Convolutional Neural Networks for Cardiac Arrhythmia Recognition Using ECG Signals

2021 ◽  
Vol 38 (6) ◽  
pp. 1737-1745
Author(s):  
Amine Ben Slama ◽  
Hanene Sahli ◽  
Ramzi Maalmi ◽  
Hedi Trabelsi
Keyword(s):  
Cardiac Arrhythmia ◽  
Heart Beat ◽  
Diagnostic Tools ◽  
Qrs Complex ◽  
Complex Signals ◽  
Ecg Signals ◽  
Proposed Model ◽  
Improved Performance ◽  
Electrocardiogram Ecg ◽  
Aided Diagnosis

In healthcare, diagnostic tools of cardiac diseases are commonly known by the electrocardiogram (ECG) analysis. Atypical electrical activity can produce a cardiac arrhythmia. Various difficulties can be imposed to clinicians e.g., myocardial infarction arrhythmia via the non-stationarity and irregularity heart beat signals. Through the assistance of computer-aided diagnosis methods, timely specification of arrhythmia diseases reduces the mortality rate of affected patients. In this study, a 1 Lead QRS complex -layer deep convolutional neural network is proposed for the recognition of arrhythmia datasets. By the use of this CNN model, we planned a complete structure of the classification architecture after a pre-processing stage counting the denoising and QRS complex signals detection procedure. The chief benefit of the new proposed methodology is that the automatically training the QRS complexes without requiring all original extracted ECG signals. The proposed model was trained on the increased ECG database and separated into five classes. Experimental results display that the established CNN method has improved performance when compared to the state-of-the-art studies.

scholarly journals Developing and comparing machine learning models to detect sleep apnoea using single-lead electrocardiogram (ECG) monitoring

2021 ◽  
Author(s):  
Marcel Hedman ◽  
Alex Rojas ◽  
Anmol Arora ◽  
David Ola
Keyword(s):  
Short Term Memory ◽  
Model Performance ◽  
Sleep Apnoea ◽  
Sequential Data ◽  
Ecg Signals ◽  
Specificity And Sensitivity ◽  
Electrocardiogram Ecg ◽  
Gated Recurrent Unit ◽  
High Disease Burden ◽  
Ecg Database

AbstractBackgroundSleep apnoea has a high disease burden but remains underdiagnosed, in part due to the expensive and resource intensive nature of polysomnography, its definitive investigation. Emerging literature suggests that it may be possible to detect sleep apnoea using single-lead ECG signals, such as those obtained from smartwatches. In this study, we use two forms of recurrent neural networks (RNNs) to detect sleep apnoea events from single-lead ECG signals.MethodsWe use single-lead ECG data from the PhysioNet Apnea-ECG database, which contains data from 70 patients. We train a bidirectional gated recurrent unit (GRU) model and a bidirectional long short-term memory (LSTM) model on labelled ECG signals from 35 patients and test the models on the remaining 35 patients in the dataset.ResultsBoth models achieved 97.1% accuracy, sensitivity and specificity to detect whether the ECG recordings belonged to a patient diagnosed with sleep apnoea. This corresponds to 34/35 patients in the dataset. At detecting individual apnoea events, the GRU and LSTM models achieved 90.4% and 91.7% accuracies respectively.DiscussionThe models achieved high levels of accuracy, specificity and sensitivity. Bidirectional RNNs are strengthened by the ability of the models to be informed by both past and future states when analysing sequential data, such as ECGs. The models also require minimal human intervention as they automatically extract features from the data. If single-lead ECGs prove a suitable tool for sleep apnoea detection, this may enhance the diagnosis of sleep apnoea and potentially allow widespread screening for the condition.ConclusionsWe note that using models such as bidirectional RNNs has the potential to augment model performance. However, more research and validation is required in order to test whether these may be applicable to other datasets and in clinical practice.

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.

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 Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Jiang ◽  
Xiaofei Li ◽  
Yao Dong
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Hybrid Model ◽  
Hybrid System ◽  
Wind Farm ◽  
Evaluation Criteria ◽  
Renewable Resource ◽  
Original Data ◽  
Determine Parameter ◽  
Forecasting Method

With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance.

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