Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes

2016 ◽  
Vol 40 (3) ◽  
pp. 819-830 ◽  
Author(s):  
Amit Kumar ◽  
Mandeep Singh
Keyword(s):  
Region Of Interest ◽  
Ecg Signals ◽  
St Segment ◽  
The Mean ◽  
Two Stages ◽  
St Segments ◽  
Electrocardiogram Ecg ◽  
Clinical Systems ◽  
Better Than ◽  
Ischaemic Episode

This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious beats in ischaemic episodes. The ischaemic episode detection is made through the coefficient of variation (COV), kurtosis and form factor. A bell-shaped normal distribution graph is generated for normal and ischaemic ST segments. The results show average sensitivity (Se) 97.71% and positive predictivity (+P) 96.89% for 90 records of the annotated European ST-T database (EDB) after validation. These results are significantly better than those of the available methods reported in the literature. The simplicity and automatic discarding of irrelevant beats makes this method feasible for use in clinical systems.

Electrocardiogram ST-Segment Morphology Variability Analysis Base on Correlation Coefficient Entropy and Inverse Correlation Coefficient Entropy

2012 ◽  
Vol 195-196 ◽  
pp. 550-554
Author(s):  
Ming Wei ◽  
Jin Zhong Song ◽  
Hong Yan
Keyword(s):  
Correlation Coefficient ◽  
Inverse Correlation ◽  
Clinical Appearance ◽  
Variability Analysis ◽  
Non Invasive ◽  
St Segment ◽  
St Segments ◽  
Electrocardiogram Ecg ◽  
Better Than

Electrocardiogram (ECG) is a convenient, economic, and non-invasive detecting tool in myocardial ischemia (MI). And its clinical appearance is mainly exhibited by ST-segment deviation. In the paper, the concepts of Correlation Coefficient Entropy (CCE) and Inverse Correlation Coefficient Entropy (ICCE) were proposed and used to compare the differences in morphology variability between ST segments induced by Heart Rate (HR) and by MI. After the Long-Term ST database (LTST) verification, the obvious results obtained with both methods. Whats more, It showed that CCE was better than ICCE comparatively.

scholarly journals Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

2020 ◽  
Vol 12 (10) ◽  
pp. 1685 ◽  
Author(s):  
Amin Ullah ◽  
Syed Muhammad Anwar ◽  
Muhammad Bilal ◽  
Raja Majid Mehmood
Keyword(s):  
Premature Ventricular Contraction ◽  
Careful Study ◽  
Spectral Image ◽  
Bundle Branch Block ◽  
Ecg Signals ◽  
The One ◽  
Short Time ◽  
Electrocardiogram Ecg ◽  
Better Than

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.

scholarly journals Electrocardiogram Baseline Wander Suppression Based on the Combination of Morphological and Wavelet Transformation Based Filtering

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Xiang-kui Wan ◽  
Haibo Wu ◽  
Fei Qiao ◽  
Feng-cong Li ◽  
Yan Li ◽  
...  
Keyword(s):  
Realistic Model ◽  
Suppression Effect ◽  
Ecg Signals ◽  
Morphological Filtering ◽  
Baseline Wander ◽  
Waveform Distortions ◽  
Electrocardiogram Ecg ◽  
Morphology Filter ◽  
Better Than

One of the major noise components in electrocardiogram (ECG) is the baseline wander (BW). Effective methods for suppressing BW include the wavelet-based (WT) and the mathematical morphological filtering-based (MMF) algorithms. However, the T waveform distortions introduced by the WT and the rectangular/trapezoidal distortions introduced by MMF degrade the quality of the output signal. Hence, in this study, we introduce a method by combining the MMF and WT to overcome the shortcomings of both existing methods. To demonstrate the effectiveness of the proposed method, artificial ECG signals containing a clinical BW are used for numerical simulation, and we also create a realistic model of baseline wander to compare the proposed method with other state-of-the-art methods commonly used in the literature. The results show that the BW suppression effect of the proposed method is better than that of the others. Also, the new method is capable of preserving the outline of the BW and avoiding waveform distortions caused by the morphology filter, thereby obtaining an enhanced quality of ECG.

scholarly journals Correlation of Severity of ST Segment Elevation with Respect to the Site of Right Coronary Artery Lesion

2013 ◽  
Vol 52 (191) ◽  
Author(s):  
Rabindra Simkhada
Keyword(s):  
Myocardial Infarction ◽  
Coronary Artery ◽  
Inferior Wall ◽  
St Segment Elevation ◽  
St Segment ◽  
Infarct Related Artery ◽  
St Elevation ◽  
The Mean ◽  
St Segments ◽  
Lesion Severity

Introduction: Electrocardiogram a widely available tool may predict infarct related artery in acute inferior wall myocardial infarction. Severity of ST segment elevation may correlate with proximity of lesion in right coronary artery.Methods: Patient with acute ST segment elevation inferior wall myocardial infarction who underwent coronary angiogram was studied. Differences in electrocardiogram among right coronary and left circumflex groups were evaluated. Severity of ST segments elevation in relation to site of lesion in right coronary was studied.Results: The mean age of presentation was 59.52 ± 11.01 years. Total 36 (72%) were men. A total of 42 (84%) had lesion in right and 8 (16%) in left circumflex. Age, sex,diabetes,hypertension, smoking, dyslipidemia and physical activity showed no correlation with lesion in right or circumflex coronary artery. ST segment elevation in III>II (P=0.01), ST segment depression in AVL> I (P<0.01) and ST elevation in V4R (P=0.04), correlated with right coronary lesion. Sum of ST elevation in inferior leads were 10.90 ±1.30 mm for proximal, 7.38±1.19 mm for mid and 5.50± 0.53 mm for distal right coronary with significant correlation (P<0.01).Conclusions: Electrocardiogram was reliable tool to difference right and left circumflex lesion. Severity of sum of ST segment elevations in inferior leads correlated with the proximity of lesion in right coronary._______________________________________________________________________________________Keywords: acute inferior myocardial infarction; electrocardiogram; infarct related artery._______________________________________________________________________________________

scholarly journals A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yurong Luo ◽  
Rosalyn H. Hargraves ◽  
Ashwin Belle ◽  
Ou Bai ◽  
Xuguang Qi ◽  
...  
Keyword(s):  
Separation Process ◽  
Electrode Impedance ◽  
Accurate Analysis ◽  
Baseline Drift ◽  
Biomedical Signals ◽  
Ecg Signals ◽  
Hierarchical Method ◽  
St Segment ◽  
Baseline Wander ◽  
Electrocardiogram Ecg

Noise can compromise the extraction of some fundamental and important features from biomedical signals and hence prohibit accurate analysis of these signals. Baseline wander in electrocardiogram (ECG) signals is one such example, which can be caused by factors such as respiration, variations in electrode impedance, and excessive body movements. Unless baseline wander is effectively removed, the accuracy of any feature extracted from the ECG, such as timing and duration of the ST-segment, is compromised. This paper approaches this filtering task from a novel standpoint by assuming that the ECG baseline wander comes from an independent and unknown source. The technique utilizes a hierarchical method including a blind source separation (BSS) step, in particular independent component analysis, to eliminate the effect of the baseline wander. We examine the specifics of the components causing the baseline wander and the factors that affect the separation process. Experimental results reveal the superiority of the proposed algorithm in removing the baseline wander.

scholarly journals Develop a Pattern Algorithm to Construct Respiration Signal Using ECG Components

2019 ◽  
Vol 7 (4) ◽  
pp. 179-183
Author(s):  
Dilshad H. Sallo
Keyword(s):  
Envelope Method ◽  
The Mean ◽  
Respiration Signal ◽  
The Right ◽  
Electrocardiogram Ecg ◽  
Better Than

The aim of this paper is designing an algorithm dubbed "pattern" to detect electrocardiogram (ECG) components accurately by searching exactly in the right places of peaks and getting exhaustive information related to the heart. Then, using the obtained results to propose a method for constructing respiration signal properly, by calculating the mean of R peaks to determine inspiration and expiration phases and calculating the amount of change for other peaks to be added during inspiration phase and subtracted during the expiration phase. The proposed method improves envelope method which only depends on the size of R to construct respiration signals. The results show that the pattern algorithm is guaranteed method and useful for detecting ECG components and exploiting them for constructing respiration signal work better than envelope method.

NEW ALGORITHM FOR QRS COMPLEX DETECTION

2005 ◽  
Vol 05 (04) ◽  
pp. 507-515 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Qrs Complex ◽  
Qrs Detection ◽  
False Negatives ◽  
Ecg Signals ◽  
Qrs Complex Detection ◽  
The Status ◽  
Linear Transform ◽  
Complex Detection ◽  
Electrocardiogram Ecg ◽  
Better Than

The Electrocardiogram (ECG), represents the electrical activity of the heart. It is characterized by a number of waves P, QRS, T which are correlated to the status of the heart activity. The most predominant wave set is the QRS complex. In this paper, we have developed a new algorithm for the detection of the QRS complexes. The algorithm consists of several steps: signal to noise enhancement, differentiation, first-order backward difference, non linear transform, moving window integrator and QRS detection. This algorithm is tested on ECG signals from the universal MIT-BIH arrhythmia database and compared with Pan and Tompkins' QRS detection method. The results we obtain show that our method performs better than the Pan and Tompkins' method. Our algorithm results in lower false positives and lower false negatives.

scholarly journals 72 hour Holter monitoring, 7 day Holter monitoring, and 30 day intermittent patient-activated heart rhythm recording in detecting arrhythmias in cryptogenic stroke patients free from arrhythmia in a screening 24 h Holter

Open Medicine ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 697-701
Author(s):  
Andrzej Kułach ◽  
Milena Dewerenda ◽  
Michał Majewski ◽  
Anetta Lasek-Bal ◽  
Zbigniew Gąsior
Keyword(s):  
Cryptogenic Stroke ◽  
Heart Rhythm ◽  
Holter Monitoring ◽  
Diagnostic Value ◽  
Ecg Monitoring ◽  
Stroke Patients ◽  
The Mean ◽  
In Cis ◽  
Electrocardiogram Ecg ◽  
Better Than

AbstractIntroductionAccording to recent studies, silent atrial fibrillation (AF) is a common cause of cryptogenic ischemic stroke (CIS). 12-lead electrocardiogram (ECG) and 24 h Holter are not efficient to reveal an occult arrhythmic cause of stroke.ObjectivesThe aim of the study was to evaluate 72 h Holter, 7 day Holter monitoring, and intermittent single-lead ECG recording in patients with CIS to identify cases with the arrhythmic cause of stroke in patients with CIS in whom 24 h ECG Holter was free from arrhythmia.Methods72 patients (aged 60 ± 9 years, 44 males) with CIS and no arrhythmic findings in 24 h Holter were enrolled. All patients had 7 day Holter monitoring and received handheld ECG recorder (CheckMe, Viatom) for ambulatory 30 ± 3 days ECG recording. AF, supraventricular tachycardia (SVT runs of ≥5 QRS), and other arrhythmias were assessed in the first 72 h of Holter recording, in 7 day-recording, and in handheld ECG strips.Results72 h-recording revealed AF in four cases (5.6%) and SVT in 18 (25%) cases. 7 day Holter confirmed AF in seven patients (10%) and SVT in 27 patients (37.5%). There was no difference in regards to CHADS2VASc score between patients with SVT and non-arrhythmic group (3.6 ± 1.1 vs 3.4 ± 1.6; p = NS). Symptoms did not correlate with findings. Patient-activated handheld ECG recorders were used with good compliance. The mean number of recordings was 49 ± 30. Except for PACs, there was only one case of AF documented in 3,531 strips.Conclusions7 day Holter performs better than 72 h and reveals supraventricular arrhythmias in every third and AF in 10% of CIS patients who were free from arrhythmia in 24 h ECG monitoring. 30 day intermittent ECG monitor does not yield diagnostic value in CIS.

DMSA-scintigraphy in paediatrics: Is the evaluation of the geometric mean necessary for the calculation of the differential renal function?

2001 ◽  
Vol 40 (04) ◽  
pp. 107-110 ◽  
Author(s):  
B. Roßmüller ◽  
S. Alalp ◽  
S. Fischer ◽  
S. Dresel ◽  
K. Hahn ◽  
...  
Keyword(s):  
Renal Function ◽  
Geometric Mean ◽  
Region Of Interest ◽  
Posterior View ◽  
Renal Abnormality ◽  
Anterior View ◽  
Differential Renal Function ◽  
The Mean ◽  
The Right ◽  
To Receive

SummaryFor assessment of differential renal function (PF) by means of static renal scintigraphy with Tc-99m-dimer-captosuccinic acid (DMSA) the calculation of the geometric mean of counts from the anterior and posterior view is recommended. Aim of this retrospective study was to find out, if the anterior view is necessary to receive an accurate differential renal function by calculating the geometric mean compared to calculating PF using the counts of the posterior view only. Methods: 164 DMSA-scans of 151 children (86 f, 65 m) aged 16 d to 16 a (4.7 ± 3.9 a) were reviewed. The scans were performed using a dual head gamma camera (Picker Prism 2000 XP, low energy ultra high resolution collimator, matrix 256 x 256,300 kcts/view, Zoom: 1.6-2.0). Background corrected values from both kidneys anterior and posterior were obtained. Using region of interest technique PF was calculated using the counts of the dorsal view and compared with the calculated geometric mean [SQR(Ctsdors x Ctsventr]. Results: The differential function of the right kidney was significantly less when compared to the calculation of the geometric mean (p<0.01). The mean difference between the PFgeom and the PFdors was 1.5 ± 1.4%. A difference > 5% (5.0-9.5%) was obtained in only 6/164 scans (3.7%). Three of 6 patients presented with an underestimated PFdors due to dystopic kidneys on the left side in 2 patients and on the right side in one patient. The other 3 patients with a difference >5% did not show any renal abnormality. Conclusion: The calculation of the PF from the posterior view only will give an underestimated value of the right kidney compared to the calculation of the geometric mean. This effect is not relevant for the calculation of the differntial renal function in orthotopic kidneys, so that in these cases the anterior view is not necesssary. However, geometric mean calculation to obtain reliable values for differential renal function should be applied in cases with an obvious anatomical abnormality.

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.

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