High-Accuracy Characterization of Ambulatory Holter Electrocardiogram Events

2012 ◽  
Vol 1 (3) ◽  
pp. 40-71 ◽  
Author(s):  
Mohammad Reza Homaeinezhad ◽  
Seyyed Amir Hoseini Sabzevari ◽  
Ali Ghaffari ◽  
Mohammad Daevaeiha
Keyword(s):  
High Accuracy ◽  
P Wave ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Qrs Complex ◽  
First Derivative ◽  
Time Distance ◽  
Noise Robust ◽  
Electrocardiogram Ecg

In this paper, three noise-robust high-accuracy methods aiming at the detection and delineation of the electrocardiogram (ECG) events (QRS complex, P-wave, T-wave) were developed. The ECG signal was initially appropriately preprocessed by application of a bandpass FIR filter and Discrete Wavelet Transform (DWT). The first detection-delineation method was the Walsh-Hadamard Transform (WHT). The WHT coefficients were divided into two groups and the signal was reconstructed using the second group coefficients. By this reconstruction, the values of first derivative of events are made stronger rather than the values of other parts of signal. In the second method, a feed forward artificial neural network was implemented to detect all events of the ECG signal. In the third method, the first derivative of signal was computed using a new signal smoothing algorithm with corresponding statistical properties. For decreasing False Positive (FP) errors associated with P-wave detection, a discriminating border was introduced as the post processing stage specified by three QRS parameters: the duration of a QRS complex, the time distance from the former and latter QRS complexes, and the potential difference from former QRS complex J-location and the latter QRS complex fiducial location. The proposed methods were applied to DAY general hospital high resolution holter data.

QRS COMPLEX DETECTION USING OPTIMAL DISCRETE WAVELET

2009 ◽  
Vol 08 (02) ◽  
pp. 97-109
Author(s):  
R. SHANTHA SELVA KUMARI ◽  
S. BHARATHI ◽  
V. SADASIVAM
Keyword(s):  
Maximum Error ◽  
Perfect Reconstruction ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Time Frequency ◽  
Qrs Complex Detection ◽  
Reconstructed Signal ◽  
Complex Detection ◽  
Electrocardiogram Ecg

Wavelet transform has emerged as a powerful tool for time frequency analysis of complex nonstationary signals such as the electrocardiogram (ECG) signal. In this paper, the design of good wavelets for cardiac signal is discussed from the perspective of orthogonal filter banks. Optimum wavelet for ECG signal is designed and evaluated based on perfect reconstruction conditions and QRS complex detection. The performance is evaluated by using the ECG records from the MIT-BIH arrhythmia database. In the first step, the filter coefficients (optimum wavelet) is designed by reparametrization of filter coefficients. In the second step, ECG signal is decomposed to three levels using the optimum wavelet and reconstructed. From the reconstructed signal, the range of error signal is calculated and it is compared with the performance of other suitable wavelets already available in the literature. The optimum wavelet gives the maximum error range as 10-14–10-11 which is better than that of other wavelets existing in the literature. In the third step, the baseline wandering is removed from the ECG signal for better detection of QRS complex. The optimum wavelet detects all R peaks of all records. That is using optimum wavelet 100% sensitivity and positive predictions are achieved. Based on the performance, it is confirmed that optimum wavelet is more suitable for ECG signal.

scholarly journals Denoising of ECG Signal using UFIR Smoothing with Notch Filter

2021 ◽  
Vol 9 (12) ◽  
pp. 2115-2122
Author(s):  
A. Rajani
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Heart Diseases ◽  
Notch Filter ◽  
High Accuracy ◽  
P Wave ◽  
Ecg Signal ◽  
Qrs Complex Detection ◽  
Time Space ◽  
Electrocardiogram Ecg

Abstract: The electrical activity of the heart is test with an electrocardiogram (ECG). The fundamental information for the taking decision about various types of heart diseases identified by electrocardiogram. There have been numerous attempts over decades to extract the characteristics of the heartbeat through ECG records with high accuracy and efficiency using a variety of strategies and techniques. In this paper a novel scheme is acquainted, the problem is solved by isolated time space using q-lag unbiased finite impulse response (UFIR), then the received time changing of optimal average horizon for the shape of the ECG signal. A complete statistical analysis is furnished by normalized histogram and statistical classifiers, P wave features extraction based on the detected fiducial points is deliberated. In this concept by utilizing QRS detection, morphological top-bottom hat transformation and notch filters is ameliorated PSNR and latency constraints, furnishes high accuracy and reduced elapsed time. Keywords: Electrocardiogram (ECG) denoising, unbiased finite impulse response (UFIR) filtering, P wave feature extraction, normalized histogram, QRS complex detection.

PVC arrhythmia classification based on fractional order system modeling

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Imen Assadi ◽  
Abdelfatah Charef ◽  
Tahar Bensouici
Keyword(s):  
Fractional Order ◽  
System Modeling ◽  
Premature Ventricular Contraction ◽  
Order System ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Fractal Structures ◽  
Life Threatening ◽  
Electrocardiogram Ecg

Abstract It is well known that many physiological phenomena are modeled accurately and effectively using fractional operators and systems. This type of modeling is due mainly to the dynamical link between fractional-order systems and the fractal structures of the physiological systems. The automatic characterization of the premature ventricular contraction (PVC) is very important for early diagnosis of patients with different life-threatening cardiac diseases. In this paper, a classification scheme of normal and PVC beats of the electrocardiogram (ECG) signal is proposed. The clustering features used for normal and PVC beats discrimination are the parameters of the commensurate order linear fractional model of the frequency content of the QRS complex of the ECG signal. A series of tests and comparisons have been performed to evaluate and validate the efficiency of the proposed PVC classification algorithm using the MIT-BIH arrhythmia database. The proposed PVC classification method has achieved an overall accuracy of 94.745%, a specificity of 95.178% and a sensitivity of 90.021% using all the 48 records of the database.

ECG SIGNAL GENERATION AND HEART RATE VARIABILITY SIGNAL EXTRACTION: SIGNAL PROCESSING, FEATURES DETECTION, AND THEIR CORRELATION WITH CARDIAC DISEASES

2012 ◽  
Vol 12 (04) ◽  
pp. 1240012 ◽  
Author(s):  
GOUTHAM SWAPNA ◽  
DHANJOO N. GHISTA ◽  
ROSHAN JOY MARTIS ◽  
ALVIN P. C. ANG ◽  
SUBBHURAAM VINITHA SREE
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Bradycardia ◽  
Premature Ventricular Contraction ◽  
P Wave ◽  
Ecg Signal ◽  
Qrs Complex ◽  
T Wave ◽  
St Segment ◽  
Cardiac Disorders

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.

DISCRETE WAVELET TRANSFORM APPLIED ON PERSONAL IDENTITY VERIFICATION WITH ECG SIGNAL

2009 ◽  
Vol 07 (03) ◽  
pp. 341-355 ◽  
Author(s):  
CHUANG-CHIEN CHIU ◽  
CHOU-MIN CHUANG ◽  
CHIH-YU HSU
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Personal Identity ◽  
Rejection Rate ◽  
Normal Subjects ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Identity Verification ◽  
Ecg Signals ◽  
Electrocardiogram Ecg

The main purpose of this study is to present a novel personal authentication approach with the electrocardiogram (ECG) signal. The electrocardiogram is a recording of the electrical activity of the heart and the recorded signals can be used for individual verification because ECG signals of one person are never the same as those of others. The discrete wavelet transform was applied for extracting features that are the wavelet coefficients derived from digitized signals sampled from one-lead ECG signal. By the proposed approach applied on 35 normal subjects and 10 arrhythmia patients, the verification rate was 100% for normal subjects and 81% for arrhythmia patients. Furthermore, the performance of the ECG verification system was evaluated by the false acceptance rate (FAR) and false rejection rate (FRR). The FAR was 0.83% and FRR was 0.86% for a database containing only 35 normal subjects. When 10 arrhythmia patients were added into the database, FAR was 12.50% and FRR was 5.11%. The experimental results demonstrated that the proposed approach worked well for normal subjects. For this reason, it can be concluded that ECG used as a biometric measure for personal identity verification is feasible.

scholarly journals Individual Identification Using Linear Projection of Heartbeat Features

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Yogendra Narain Singh
Keyword(s):  
Healthy Subjects ◽  
Orthogonal Basis ◽  
Individual Identification ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Projection ◽  
Novel Method ◽  
Electrocardiogram Ecg ◽  
Better Than

This paper presents a novel method to use the electrocardiogram (ECG) signal as biometrics for individual identification. The ECG characterization is performed using an automated approach consisting of analytical and appearance methods. The analytical method extracts the fiducial features from heartbeats while the appearance method extracts the morphological features from the ECG trace. We linearly project the extracted features into a subspace of lower dimension using an orthogonal basis that represent the most significant features for distinguishing heartbeats among the subjects. Result demonstrates that the proposed characterization of the ECG signal and subsequently derived eigenbeat features are insensitive to signal variations and nonsignal artifacts. The proposed system utilizing ECG biometric method achieves the best identification rates of 85.7% for the subjects of MIT-BIH arrhythmia database and 92.49% for the healthy subjects of our IIT (BHU) database. These results are significantly better than the classification accuracies of 79.55% and 84.9%, reported using support vector machine on the tested subjects of MIT-BIH arrhythmia database and our IIT (BHU) database, respectively.

scholarly journals ID: 1081 Development and validation of surface electrocardiogram in the septal accessory pathway localization in typical Wolff-Parkinson-White syndrome

2017 ◽  
Vol 4 (S) ◽  
pp. 167
Author(s):  
Si Dung Chu ◽  
Khanh Quoc Pham ◽  
Dong Van Tran
Keyword(s):  
Accessory Pathway ◽  
High Sensitivity ◽  
High Accuracy ◽  
Qrs Complex ◽  
Delta Wave ◽  
Wpw Syndrome ◽  
Development And Validation ◽  
Electrocardiogram Ecg ◽  
Surface Electrocardiogram ◽  
Wolff Parkinson White Syndrome

Objectives: This study was designed characteristics of surface electrocardiogram (ECG) for the localization of septal accessory pathway (AP) in the typical Wolff-Parkinson-White (WPW) syndrome to develop a new algorithm ECG for the septal AP localization, and to test the accuracy of the algorithm prospectively.  Subject and Methods: We studied 106 patients, in 65 patients with typical WPW syndrome have a single anterograde with the localization of Aps identified by successful radiofrequency catheter ablation (RCFA) to develop a new ECG algorithm for the septal AP localization. Then this algorithm was tested propectively in 41 patients were compared with the location of AP’s successful ablation by RCFA (gold standard).  Results: We found that the 12 lead ECG parameters in 65 patients with typical WPW syndrome such as the transition of the QRS complex, delta wave polarity in V1 lead, delta wave polarity in at least 2/3 inferior leads and morphology QRS was “QRS pattern’’ in inferior leads in diagnosis for the localization of septal APs with hight accuracy predicted from 83.3% to 100%, and for development of a new ECG algorithm. Then the following 41 patients were prospectively evaluated by the new derived algorithm for the septal pathways with high sensitivity and specificity from 84.6% to 100%.  Conclusion: 12-lead ECG parameters in typical WPW syndrome closely related to the septal AP localization, in order to develop the new ECG algorithm by parameters as above; and can be used to a new septal ECG algorithm in predicted the location APs with high accuracy predicted

scholarly journals Development and evaluation of surface electrocardiogram in the septal accessory pathway localization in typical Wolff-Parkinson-White syndrome

2018 ◽  
Vol 5 (9) ◽  
pp. 2680-2687
Author(s):  
Si Dung Chu ◽  
Khanh Quoc Pham ◽  
Dong Van Tran
Keyword(s):  
Accessory Pathway ◽  
High Sensitivity ◽  
High Accuracy ◽  
Strongly Correlated ◽  
Qrs Complex ◽  
Delta Wave ◽  
Wpw Syndrome ◽  
Electrocardiogram Ecg ◽  
Surface Electrocardiogram ◽  
Wolff Parkinson White Syndrome

Objectives: This study was designed to characterize the surface electrocardiogram (ECG) of the typical Wolff-Parkinson-White (WPW) syndrome to develop a new algorithm ECG to localize the septal accessory pathways (APs) and to prospectively test the accuracy of the algorithm. Methods: We studied 106 patients, in which 65 patients with typical WPW syndrome who had a single antero-grade with the localization of APs identified by successful radiofrequency catheter ablation (RFCA) to develop a new ECG algorithm for the septal AP localization. Then, this algorithm was tested prospectively in 41 patients to compare to the localization of APs by successful ablation by RFCA (gold standard). Results: In 65 patients with typical WPW syndrome, we found that the 12-lead ECG parameters such as the transition of the QRS complex, delta wave polarity in V1 lead, delta wave polarity in at least 2/3 inferior leads and ``QRS pattern'' in inferior leads can predict the localization of septal APs with the accuracy ranging from 83.3% to 100%. Then, 41 patients were prospectively evaluated by the new derived algorithm to localize the septal APs with high sensitivity and specificity from 84.6% to 100%. Conclusion: 12-lead ECG parameters in typical WPW syndrome are strongly correlated to the septal AP localization, which can be used to develop a new ECG algorithm to localize septal APs with high accuracy.

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.

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