Distinguishing Normal and Abnormal ECG Signal

Rao, P. Tirumala; Rao, S. Koteswarao; Manikanta, G.; Kumar, Satish

doi:10.17485/ijst/2016/v9i10/85449

Cited by 11 publications

(4 citation statements)

References 6 publications

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“… 38 Our DLM integrated whole ECG information, including but not limited to atrial fibrillation, ventricular premature complex, tachycardia, prolonged QT interval, and abnormal T wave axis, to establish mortality risk and performed better due to its powerful feature extraction capability. Although physicians have already used ECG to recognize abnormal signals to decide on further intervention, 39 our DLM can provide further risk stratification in normal/abnormal patients, which is consistent with previous studies. 23 Moreover, since the above abnormal morphologies were widely associated with CVDs, 40 the ECG mortality risk integrating these signals was also associated with future CVDs, which may be used as an integrating indicator for CVD risk stratification.…”

Section: Discussionsupporting

confidence: 89%

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

et al. 2023

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Background The electrocardiogram (ECG) may be the most popular test in the management of cardiovascular disease (CVD). Although wide applications of artificial intelligence (AI)-enabled ECG have been developed, an integrating indicator for CVD risk stratification was not investigated. Since mortality may be the most important global outcome, this study aimed to develop a survival deep learning model (DLM) to establish a critical ECG value and explore the associations with various CVD events. Methods We trained a DLM with 451,950 12-lead resting ECGs obtained from 210,552 patients, for whom 23,592 events occurred. The internal validation set included 27,808 patients with one ECG for each patient. The external validations were performed in a community hospital with 33,047 patients and two transnational data sets with 233,647 and 1631 ECGs. We distinguished the cause of mortality and additionally investigated CVD-related outcomes, including new-onset acute myocardial infarction (AMI), stroke (STK), and heart failure (HF). Results The DLM achieved C-indices of 0.858/0.836 in internal/external validation sets by using ECG over a 10-year period. The high-mortality-risk group identified by the proposed DLM presented a hazard ratio (HR) of 14.16 (95% confidence interval (CI): 11.33–17.70) compared to the low-risk group in the internal validation and presented a higher risk of cardiovascular (CV) mortality (HR: 18.50, 95% CI: 9.82–34.84), non-CV mortality (HR: 13.68, 95% CI: 10.76–17.38), AMI (HR: 4.01, 95% CI: 2.24–7.17), STK (HR: 2.15, 95% CI: 1.70–2.72), and HF (HR: 6.66, 95% CI: 4.54–9.77), which was consistent in an independent community hospital. The transnational validation also revealed HRs of 4.91 (95% CI: 2.63–9.16) and 2.29 (95% CI: 2.15–2.44) for all-cause mortality in the SaMi-Trop and Clinical Outcomes in Digital Electrocardiography 15% (CODE15) cohorts. Conclusions The mortality risk by AI-enabled ECG may be applied in passive electronic-health-record-based CVD risk screening, which may identify more asymptomatic and unaware high-risk patients.

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Section: Discussionsupporting

confidence: 89%

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

et al. 2023

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“…In this methodology comparison of QRS complex is used to identify and diagnose the abnormal conditions of the heart. These can be identified in detail by comparing the clinical and statistical parameters [3].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The ST segment starts where the QRS complex ends. The ST segment which lies before the onset of the T wave, represents the period between the end of ventricular depolarisation and repolarisation [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density

Thamizhvani¹,

Joseph²,

Babu³

et al. 2018

IJET

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Abnormality of the heart is monitored by Electrocardiograph (ECG). The ECG waveform is formed of PQRS pattern. Differentiation of the abnormalities based on the ECG signal is simple algorithm for diagnosis. ECG data of normal, atrial fibrillation and congestive heart failure is obtained from a authorized database. R peak from the QRS complex is detected using Pan-Tompkins algorithm for analysis. Mean RR and heart rate variability parameters are extracted from the QRS complex detected. With these results, the difference in the three ECG signals can be determined. For further detailed comparison, frequency component variation is analysed using power spectral density. Based on density spectrum, the differentiation of normal and abnormal ECG signals can be determined.

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“…To differentiate between normal and abnormal ECG signals, see [17]. QRS complex duration, RR intervals, and heart rates of 7 ECG signals were compared.…”

Section: Introductionmentioning

confidence: 99%

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

Mondal,

Choudhary,

Rathee

2024

Biomed. Phys. Eng. Express

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[Aim of the study: This research endeavors to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilising longer electrocardiogram (ECG) signal events based on Apnea ECG dataset. Apnea ECG is often used for the detection of sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification.Used method: In order to extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorising ECG signals by utilising several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6).Brief Description of Results: The results emphasise the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognises 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy.Findings: The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest that wavelet families and machine learning can improve cardiac abnormality detection and categorization.]

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Distinguishing Normal and Abnormal ECG Signal

Cited by 11 publications

References 6 publications

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

Mortality risk prediction of the electrocardiogram as an informative indicator of cardiovascular diseases

Comparison of Normal and Abnormal Conditions in ECG Using RR Variablity and Spectral Density

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

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