scholarly journals TWO-LEAD JOINT DETECTION ALGORITHM OF QRS COMPLEXES IN ECG SIGNAL

2021 ◽  
Author(s):  
WANSONG XU ◽  
TIANWU CHEN ◽  
FANYU DU
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Joint Detection ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Threshold Adjustment ◽  
Current Algorithm ◽  
Electrocardiogram Ecg

Objective: The detection of QRS complexes is an important part of computer-aided analysis of electrocardiogram (ECG). However, most of the existing detection algorithms are mainly for single-lead ECG signals, which requires high quality of signal. If the signal quality decreases suddenly due to some interference, then the current algorithm is easy to cause misjudgment or missed detection. To improve the detection ability of QRS complexes under sudden interference, we study the QRS complexes information on multiple leads in-depth, and propose a two-lead joint detection algorithm of QRS complexes. Methods: Firstly, the suspected QRS complexes are screened on the main lead. For the suspected QRS complexes with low confidence and the complexes that may be missed, further accurate detection and joint judgment shall be carried out at the corresponding position of the auxiliary lead. At the same time, the adaptive threshold adjustment algorithm and backtracking mechanism are used to modify the detection results. Results: The proposed detection algorithm is validated using 48 ECG records of the MIT-BIH arrhythmia database, and achieves average detection accuracy of 99.71%, sensitivity of 99.88% and positive predictivity of 99.81%. Conclusion: The proposed algorithm has high accuracy, which can effectively deal with the sudden interference of ECG signal. Meanwhile, the algorithm requires small amount of computation, and can be embedded into hardware for real-time detection.

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

ALGORITHM FOR AUTOMATIC DETECTION OF ECG WAVES

2011 ◽  
Vol 11 (01) ◽  
pp. 15-29 ◽  
Author(s):  
DIB. NABIL ◽  
F. BEREKSI-REGUIG
Keyword(s):  
Detection Accuracy ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Accurate Identification ◽  
Ecg Signals ◽  
Good Ability ◽  
Noisy Conditions ◽  
Electrocardiogram Ecg ◽  
T Waves

An accurate measurement of the different electrocardiogram (ECG) intervals is dependent on the accurate identification of the beginning and the end of the P, QRS, and T waves. Available commercial systems provide a good QRS detection accuracy. However, the detection of the P and T waves remains a serious challenge due to their widely differing morphologies in normal and abnormal beats. In this paper, a new algorithm for the detection of the QRS complex as well as for P and T waves identification is provided. The proposed algorithm is based on different approaches and methods such as derivations, thresholding, and surface indicator. The proposed algorithm is tested and evaluated on ECG signals from the universal MIT-BIH database. It shows a good ability to detect P, QRS, and T waves for different cases of ECG signal even in very noisy conditions. The obtained QRS, sensitivity and positive predictivity are respectively 95.39% and 98.19%. The developed algorithm is also able to separate the overlapping P and T waves.

scholarly journals R-DECO: an open-source Matlab based graphical user interface for the detection and correction of R-peaks

2019 ◽  
Vol 5 ◽  
pp. e226 ◽  
Author(s):  
Jonathan Moeyersons ◽  
Matthew Amoni ◽  
Sabine Van Huffel ◽  
Rik Willems ◽  
Carolina Varon
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Detection Algorithm ◽  
Ecg Signal ◽  
Previous Knowledge ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Data Formats ◽  
Electrocardiogram Ecg

Many of the existing electrocardiogram (ECG) toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. In this paper we present R-DECO, an easy-to-use graphical user interface (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

scholarly journals Classification of Heart conditions by Statistical Characterization of ECG Signal

2020 ◽  
Vol 16 (2) ◽  
Author(s):  
Asma Haque ◽  
Abdur Rahman
Keyword(s):  
Ventricular Tachyarrhythmia ◽  
Research Work ◽  
Ecg Signal ◽  
Statistical Characterization ◽  
Ecg Signals ◽  
Detection Algorithms ◽  
Heart Patients ◽  
Proposed Model ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal exhibits important distinctive feature for different cardiac issues. Automatic classification of electrocardiogram (ECG) signal can be used for primary detection of various heart conditions. Information about heart and ischemic changes of heart may be obtained from cleaned ECG signals. ECG signal has an important role in monitoring and diacritic of the heart patients. An accurate ECG classification is challenging problem. The accuracy often depends on proper selection of observing parameters as well as detection algorithms. Heart disorder means abnormal rhythm or abnormalities present in the heart. In this research work, we have developed a decision tree based algorithm to classify heart problems by utilizing the statistical signal characteristic (SSC) of an ECG signal. The proposed model has been tested with real ECG signal to successfully (60-98%) detect normal, apnea and ventricular tachyarrhythmia condition.

scholarly journals ECG data dependency for atrial fibrillation detection based on residual networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyo-Chang Seo ◽  
Seok Oh ◽  
Hyunbin Kim ◽  
Segyeong Joo
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Blood Clot ◽  
Detection Algorithm ◽  
Ecg Signal ◽  
Data Dependency ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Is Evaluation ◽  
Electrocardiogram Ecg

AbstractAtrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

scholarly journals Study On ECG Data Dependency For Atrial Fibrillation Detection Based On Residual Networks

2021 ◽  
Author(s):  
Hyo-Chang Seo ◽  
Seok Oh ◽  
Segyeong Joo
Keyword(s):  
Atrial Fibrillation ◽  
Deep Learning ◽  
Blood Clot ◽  
Detection Algorithm ◽  
Ecg Signal ◽  
Data Dependency ◽  
Test Dataset ◽  
Detection Algorithms ◽  
Is Evaluation ◽  
Electrocardiogram Ecg

Abstract Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart failure. To detect AF, deep learning-based detection algorithms have recently been developed. However, deep learning models were often trained with limited datasets and were evaluated within the same datasets, which makes their performance generally drops on the external datasets, known as data dependency. For this study, three different databases from PhysioNet were used to investigate the data dependency of deep learning-based AF detection algorithm using the residual neural network (Resnet). Resnet 18, 34, 50 and 152 model were trained with raw electrocardiogram (ECG) signal extracted from independent database. The highest accuracy was about 98–99% which is evaluation results of test dataset from the own database. On the other hand, the lowest accuracy was about 53–92% which was evaluation results of the external dataset extracted from different source. There are data dependency according to the train dataset and the test dataset. However, the data dependency decreased as a large amount of train data.

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

Study for Detection Algorithm of QRS Complex in ECG Signal

2013 ◽  
Vol 765-767 ◽  
pp. 2105-2108
Author(s):  
Xu Wen Li ◽  
Bi Wei Zhang ◽  
Qiang Wu
Keyword(s):  
Wavelet Transform ◽  
Detection Algorithm ◽  
Automatic Analysis ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Good Precision ◽  
Qrs Complex ◽  
Noise Interference ◽  
Wavelet Transform Modulus Maxima ◽  
Modulus Maxima

In ECG signals accurate detection to the position of QRS complex is a key to automatic analysis and diagnosis system. And its premise is that effectively remove all kinds of noise interference in ECG signal. Here, a method of detecting QRS based on EMD and wavelet transform was presented which is aim to improve the anti-noise performance of the detection algorithm. It is combined EMD with the theory of singularity detecting based on wavelet transform modulus maxima method. It has the high detection accuracy and good precision that can give an effective way to the automatic analysis for ECG signal.

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.

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