Poor R-wave progression and myocardial infarct size after anterior myocardial infarction in the coronary intervention era

Kurisu, Satoshi; Iwasaki, Tadaaki; Watanabe, Noriaki; Ikenaga, Hiroki; Satō, Takashi; Higaki, Tadanao; Ishibashi, Ken; Dohi, Yoshihiro; Fukuda, Yukihiro; Kihara, Yasuki

doi:10.1016/j.ijcha.2014.09.002

Cited by 8 publications

(7 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2.2 2.1 3.9 7.7 3.7 5.1 1.9 1.9 0.9 1.0 0.9 0.9 0.9 0.9 0. duration: 124.1 ms and 107.7 ms (apical and inferolateral MI) vs. 90.4 ms (normal). PRWP mainly occurred in extensive anterior, septal, and apical MI, similar as reported in the literature [43], [45]. Specifically, the R wave amplitude in the septal MI was sometimes flattened, while the R wave of V6 tended to be larger than that of V5 in the apical MI, as Fig.…”

Section: B Sensitivity Analysis Of Qrs For Different Post-mi Characte...supporting

confidence: 86%

“…To quantify discrepancy between QRS shapes, we employed a global measure, DTW, which compared signals of different lengths with an additional penalty for the difference in QRS duration between the two signals [18]. Furthermore, we introduced four QRS abnormalities reported in literature, i.e., QRS duration prolongation [41], pathological Q-waves [42], poor R wave progression (PRWP) [43], and fragmented QRS (fQRS) [44]. The reader is referred to Fig.…”

Section: B Sensitivity Analysis Of Qrs For Different Post-mi Characte...mentioning

confidence: 99%

See 1 more Smart Citation

Deep Computational Model for the Inference of Ventricular Activation Properties

Camps

Banerjee

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Myocardial infarction (MI) demands precise and swift diagnosis. Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of MI. The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT platform, and particularly in the context of studying MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG), focusing on the development of a comprehensive CDT platform specifically designed for MI.The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently propose a deep computational model to infer infarct location and distribution from the simulated QRS. The in silico experimental results show that our model can effectively capture the complex relationships between the QRS signals and the corresponding infarct regions, with promising potential for clinical application in the future. The code will be released publicly once the manuscript is accepted for publication.

show abstract

Section: B Sensitivity Analysis Of Qrs For Different Post-mi Characte...supporting

confidence: 86%

Section: B Sensitivity Analysis Of Qrs For Different Post-mi Characte...mentioning

confidence: 99%

Deep Computational Model for the Inference of Ventricular Activation Properties

Camps

Banerjee

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…For instance, poor R-wave progression (PRWP) is an important ECG finding that may be associated with many cardiac conditions, which have mortality implications for the medical director. PRWP refers to the failure to gradually increase for R wave toward V1 to V6 leads, which is commonly observed in the typical anterior myocardial infarction, LBBB, right, and left ventricular hypertrophy, Wolff-Parkinson-White syndrome and so on ( Mackenzie, 2005 ; Kurisu et al, 2015 ). A prospective cohort study of 5613 healthy population ( Anttila et al, 2010 ) suggested that PRWP was more frequent in women (7.0%) than that in men (2.7%) in three age groups 30 years or older.…”

Section: Discussionmentioning

confidence: 99%

R-Wave Singularity: A New Morphological Approach to the Analysis of Cardiac Electrical Dyssynchrony

Zhan

Shi

et al. 2020

Front. Physiol.

View full text Add to dashboard Cite

R-wave singularity (RWS) measures the intermittence or discontinuousness of R waves. It has been broadly used in QRS (QRS complex of electrocardiogram) detection, electrocardiogram (ECG) beats classification, etc. In this article, we novelly developed RWS to the analysis of QRS morphology as the measurement of ventricular dyssynchrony and tested the hypothesis that RWS could enhance the discrimination between control and acute myocardial infarction (AMI) patients. Holter ECG recordings were obtained from the Telemetric and Holter ECG Warehouse database, among which database Normal was extracted as normal controls (n = 202) and database AMI (n = 93) as typical subjects of autonomic nervous system dysfunction and cardiac electrical dyssynchrony with high risk for cardiac arrhythmias and sudden cardiac death. Experimental results demonstrate that RWS measured by Lipschitz exponent calculated from 5-min Holter recordings was significantly less negative in early AMI and late AMI than that in Normal subjects for overall, elderly, and elderly male groups, which suggested the heterogeneous depolarization of the ventricular myocardium during AMI. Receiver operating characteristic curve analyses show that combined with heart rate variability parameters, Lipschitz exponent provides higher accuracy in distinguishing between the patients with AMI and healthy control subjects for overall, elderly, elderly male, and elderly female groups. In summary, our study demonstrates the significance of using RWS to probe the cardiac electrical dyssynchrony for AMI. Lipschitz exponent may be valuable and complementary for existing cardiac resynchronization therapy and autonomic nervous system assessment.

show abstract

“…After obtaining the cardiac cycles and key segments, we formulate rules for 15 ECG abnormalities according to criteria in the previous work [37], [40], [41]. The rules are somewhat simplified, but still maintain a high degree of consistency with experts' attention on ECGs.…”

Section: B Rule Inference Modulementioning

confidence: 99%

“…The formulas we use for identifying ECG abnormalities according to criteria in the medical literature [37], [40], [41]. t(s) indicates the duration of a segment s, computed according to Eq.…”

Section: Table Imentioning

confidence: 99%

Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network

Bian

Chen

et al. 2023

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

A large number of people suffer from lifethreatening cardiac abnormalities, and electrocardiogram (ECG) analysis is beneficial to determining whether an individual is at risk of such abnormalities. Automatic ECG classification methods, especially the deep learning based ones, have been proposed to detect cardiac abnormalities using ECG records, showing good potential to improve clinical diagnosis and help early prevention of cardiovascular diseases. However, the predictions of the known neural networks still do not satisfactorily meet the needs of clinicians, and this phenomenon suggests that some information used in clinical diagnosis may not be well captured and utilized by these methods. In this paper, we introduce some rules into convolutional neural networks, which help present clinical knowledge to deep learning based ECG analysis, in order to improve automated ECG diagnosis performance. Specifically, we propose a Handcrafted-Rule-enhanced Neural Network (called HRNN) for ECG classification with standard 12-lead ECG input, which consists of a rule inference module and a deep learning module. Experiments on two large-scale public ECG datasets show that our new approach considerably outperforms existing state-of-the-art methods. Further, our proposed approach not only can improve the diagnosis performance, but also can assist in detecting mislabelled ECG samples. Our codes are available at https://github.com/alwaysbyx/ecg processing.

show abstract

Poor R-wave progression and myocardial infarct size after anterior myocardial infarction in the coronary intervention era

Cited by 8 publications

References 17 publications

Deep Computational Model for the Inference of Ventricular Activation Properties

Deep Computational Model for the Inference of Ventricular Activation Properties

R-Wave Singularity: A New Morphological Approach to the Analysis of Cardiac Electrical Dyssynchrony

Identifying Electrocardiogram Abnormalities Using a Handcrafted-Rule-Enhanced Neural Network

Contact Info

Product

Resources

About