Ventricular fibrillation waveform characteristics of the surface ECG: Impact of the left ventricular diameter and mass

Bonnes, Judith L.; Thannhauser, Jos; Nas, Joris; Westra, Sjoerd W.; Jansen, R.M.G.; Meinsma, Gjerrit; Boer, Menko‐Jan de; Smeets, Joep L.R.M.; Keuper, Wessel; Brouwer, Marc A.

doi:10.1016/j.resuscitation.2017.03.029

Cited by 10 publications

(6 citation statements)

References 39 publications

(77 reference statements)

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“…There have been a few studies that attempted to infer the underlying characteristics of the heart based on features extracted from the electrograms. The analysis of the electrogram characteristics (particularly frequency) were observed to have a correlation with the diameter of the left ventricle [20], but there was no observed relation to the mass of the heart. The conduction velocity and amplitude were used as determining factors for reducing the recurrence of future VT episodes [21].…”

Section: Current Researchmentioning

confidence: 89%

“…However, the lethal nature of VA and the practical infeasibility in obtaining APP characteristics in a non-invasive manner are obstacles in studying VA. Given that literature has shown that there exists relationships between the electrograms and APP characteristics (and in turn could influence specific events) [20,22], this dissertation will develop adaptive signal decomposition methods to extract information from multi-channel electrograms that could be used to infer specific events and the APP characteristics during VAs.…”

Section: Motivationmentioning

confidence: 99%

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Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Balasundaram¹

2021

Preprint

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Ventricular fibrillation (VF) is one of the major causes for sudden cardiac deaths (SCD). The duration from the onset of VF to SCD is a few minutes, making it difficult to study VF. This dissertation proposes methods to extract meaningful information from VF electrograms and formulate associations to underlying structural and physiological properties of the cardiac tissue and clinical events of interest during VF. This was achieved by analyzing clues in the electrograms during VF to infer the underlying anatomical and physiological properties of the cardiac tissue and certain clinical events of interest, which is otherwise not easily available. The proposed methods will be of great assistance for the diagnosis and treatment planning of cardiac arrhythmias. The proposed adaptive time-frequency (TF) signal decomposition was separated into two categories based on two purposes: (1) Time-specific event detection and (2) Time-averaged VA characterization. For the time-specific event detection (in this work rotor detection), electrogram signal features related to the rotor event were identified with an adaptive TF decomposition and amodified criterion function. Using the proposed features and a linear discriminant analysis based classifier with leave-one-out cross validation, overall classification accuracies of 80.77% and 79.41% were achieved in detecting rotor events and separating them from similar but non-rotor events. In the time-averaged ventricular arrhythmia characterization, previously established signal features were used to associate electrogram clues to the structural and physiological characteristics of the cardiac tissue. Using label-consistent K-means singular value decomposition dictionary learning process, dictionaries of TF basis functions were generated to capture specific electric structures and physiological characteristics of the underlying cardiac tissue. The association of these characteristics with the extracted electrogram clues were validated using a cross-validation technique. The cross-validated results ranged from 65.58% to 81.80% for the 7 characteristics used in this study. Further to this, to build a decision-support system with non-linear separable capabilities that could automate and infer the heart events and/or characteristics from the identified electrogram signal structures, neural network models were generated. The cross-validated accuracies ranged from 66.99% to 85.90% for each of the developed models for the decision-support system.

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Section: Current Researchmentioning

confidence: 89%

Section: Motivationmentioning

confidence: 99%

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Balasundaram¹

2021

Preprint

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“…AMSA is directly related to the heart metabolic status, correlating with myocardial energy phosphates concentrations. However, little is known about other patient characteristics that may influence the ventricular fibrillation waveform [4,51]. For example, the presence of comorbidities is a well known risk factor for poor survival.…”

Section: Amplitude Spectrum Area In Retrospective Clinical Studiesmentioning

confidence: 99%

Optimizing defibrillation during cardiac arrest

Babini

Ruggeri

Ristagno

2021

Current Opinion in Critical Care

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Purpose of reviewCurrent cardiac arrest guidelines are based on a fixed, time-based defibrillation strategy. Rhythm analysis and shock delivery (if indicated) are repeated every 2 min requiring cyclical interruptions of chest compressions. This approach has several downsides, such as the need to temporarily stop cardiopulmonary resuscitation (CPR) for a variable amount of time, thus reducing myocardial perfusion and decreasing the chance of successful defibrillation. A tailored defibrillation strategy should identify treatment priority for each patient, that is chest compressions (CCS) or defibrillation, minimize CCs interruptions, speed up the delivery of early effective defibrillation and reduce the number of ineffective shocks. Recent findingsReal-time ECG analysis (using adaptive filters, new algorithms robust to chest compressions artifacts and shock-advisory algorithms) is an effective strategy to correctly identify heart rhythm during CPR and reduce the hands-off time preceding a shock. Similarly, ventricular fibrillation waveform analysis, that is amplitude spectrum area (AMSA) represents a well established approach to reserve defibrillation in patients with high chance of shock success and postpone it when ventricular fibrillation termination is unlikely. Both approaches demonstrated valuable results in improving cardiac arrest outcomes in experimental and observational study.

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“…The principal ECG changes in patients with increased LV-D and LV-S in LV hypertrophy include augmented QRS amplitude, prolonged QRS conduction time, changes in instantaneous and mean QRS vectors, ST depression and/or T-wave inversion, and P-wave abnormalities, such as left atrial enlargement ( 7 , 8 ). VF frequency was consistently lower in patients with an increased LV diameter ( 9 ). However, these ECG changes were neither sensitive nor specific for increased LV-D or LV-S detection.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction

et al. 2022

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BackgroundHeart failure (HF) is a global disease with increasing prevalence in an aging society. However, the survival rate is poor despite the patient receiving standard treatment. Early identification of patients with a high risk of HF is important but challenging. Left ventricular end-diastolic diameter (LV-D) increase was an independent risk factor of HF and adverse cardiovascular (CV) outcomes. In this study, we aimed to develop an artificial intelligence (AI) enabled electrocardiogram (ECG) system to detect LV-D increase early.ObjectiveWe developed a deep learning model (DLM) to predict left ventricular end-diastolic and end-systolic diameter (LV-D and LV-S) with internal and external validations and investigated the relationship between ECG-LV-D and echocardiographic LV-D and explored the contributions of ECG-LV-D on future CV outcomes.MethodsElectrocardiograms and corresponding echocardiography data within 7 days were collected and paired for DLM training with 99,692 ECGs in the development set and 20,197 ECGs in the tuning set. The other 7,551 and 11,644 ECGs were collected from two different hospitals to validate the DLM performance in internal and external validation sets. We analyzed the association and prediction ability of ECG-LVD for CV outcomes, including left ventricular (LV) dysfunction, CV mortality, acute myocardial infarction (AMI), and coronary artery disease (CAD).ResultsThe mean absolute errors (MAE) of ECG-LV-D were 5.25/5.29, and the area under the receiver operating characteristic (ROC) curves (AUCs) were 0.8297/0.8072 and 0.9295/0.9148 for the detection of mild (56 ≦ LV-D < 65 mm) and severe (LV-D ≧ 65 mm) LV-D dilation in internal/external validation sets, respectively. Patients with normal ejection fraction (EF) who were identified as high ECHO-LV-D had the higher hazard ratios (HRs) of developing new onset LV dysfunction [HR: 2.34, 95% conference interval (CI): 1.78–3.08], CV mortality (HR 2.30, 95% CI 1.05–5.05), new-onset AMI (HR 2.12, 95% CI 1.36–3.29), and CAD (HR 1.59, 95% CI 1.26–2.00) in the internal validation set. In addition, the ECG-LV-D presents a 1.88-fold risk (95% CI 1.47–2.39) on new-onset LV dysfunction in the external validation set.ConclusionThe ECG-LV-D not only identifies high-risk patients with normal EF but also serves as an independent risk factor of long-term CV outcomes.

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Ventricular fibrillation waveform characteristics of the surface ECG: Impact of the left ventricular diameter and mass

Cited by 10 publications

References 39 publications

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Morphologically constrained adaptive signal decompositions in studying ventricular arrhythmias

Optimizing defibrillation during cardiac arrest

Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction

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