Both selective and nonselective His bundle, but not myocardial, pacing preserve ventricular electrical synchrony assessed by ultra-high-frequency ECG

Čurila, Karol; Procházková, Radka; Jurák, Pavel; Jastrzębski, Marek; Halámek, Josef; Moskal, P.; Štros, Petr; Veselá, Jana; Waldauf, Petr; Viščor, Ivo; Plešinger, Filip; Sussenbek, Ondrej; Heřman, Dalibor; Osmančík, Pavel; Smíšek, Radovan; Leinveber, Pavel; Czarnecka, Danuta; Widimský, Petr

doi:10.1016/j.hrthm.2019.11.016

Cited by 43 publications

(35 citation statements)

References 16 publications

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“…Electrical and mechanical synchrony assessment of the left ventricle suggests that NS-HBP does not induce left ventricular dyssynchrony, but RV synchrony likely is affected. 6,24,31 The importance of this finding is not completely clear, but a nonrandomized comparison between S-HBP and NS-HBP found no statistically significant difference in outcomes between them. 30 The current consensus among the HBP community is that NS-HBP does not risk adverse consequences unless the intrinsic His-ventricular (HV) interval is very long or the rare circumstance in which patients are highly sensitive to minimal dyssynchrony.…”

Section: Figurementioning

confidence: 95%

“…The slow, dyssynchronous activation pattern produced is similar to conventional RV pacing. 24 If MOC is mistakenly diagnosed as either NS-HBP or S-HBP, the patient may develop morbidity from heart failure due to pacing-induced cardiomyopathy, which also carries a mortality risk. [25][26][27] HBP seems to be highly effective at preventing and treating pacing-induced cardiomyopathy, albeit in nonrandomized studies.…”

Section: Distinguishing Moc From Hbpmentioning

confidence: 99%

“…However, the differences between S-HBP and NS-HBP are an area of ongoing scientific enquiry. 24,30,31 NS-HBP allows continued pacing if infrahisian conduction block occurs, whereas this could be symptomatic or even fatal with S-HBP. Therefore, distinguishing between S-HBP and NS-HBP ECG appearances is clinically important for programming in patients at risk for infrahisian conduction block.…”

Section: Distinguishing S-hbp From Ns-hbpmentioning

confidence: 99%

“…Whether the fact that some myocardium is activated through cell-to-cell myocardial conduction means that dyssynchrony occurs, risking pacing-induced cardiomyopathy, has been debated. 24…”

Section: Distinguishing S-hbp From Ns-hbpmentioning

confidence: 99%

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Discriminating electrocardiographic responses to His-bundle pacing using machine learning

Arnold¹,

Howard²,

Gopi

et al. 2020

Cardiovascular Digital Health Journal

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BACKGROUND His-bundle pacing (HBP) has emerged as an alternative to conventional ventricular pacing because of its ability to deliver physiological ventricular activation. Pacing at the His bundle produces different electrocardiographic (ECG) responses: selective His-bundle pacing (S-HBP), non-selective His bundle pacing (NS-HBP), and myocardium-only capture (MOC). These 3 capture types must be distinguished from each other, which can be challenging and time-consuming even for experts. OBJECTIVE The purpose of this study was to use artificial intelligence (AI) in the form of supervised machine learning using a convolutional neural network (CNN) to automate HBP ECG interpretation. METHODS We identified patients who had undergone HBP and extracted raw 12-lead ECG data during S-HBP, NS-HBP, and MOC. A CNN was trained, using 3-fold cross-validation, on 75% of the segmented QRS complexes labeled with their capture type. The remaining 25% was kept aside as a testing dataset. RESULTS The CNN was trained with 1297 QRS complexes from 59 patients. Cohen kappa for the neural network's performance on the 17-patient testing set was 0.59 (95% confidence interval 0.30 to 0.88; P ,.0001), with an overall accuracy of 75%. The CNN's accuracy in the 17-patient testing set was 67% for S-HBP, 71% for NS-HBP, and 84% for MOC. CONCLUSION We demonstrated proof of concept that a neural network can be trained to automate discrimination between HBP ECG responses. When a larger dataset is trained to higher accuracy, automated AI ECG analysis could facilitate HBP implantation and follow-up and prevent complications resulting from incorrect HBP ECG analysis.

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Section: Figurementioning

confidence: 95%

Section: Distinguishing Moc From Hbpmentioning

confidence: 99%

Section: Distinguishing S-hbp From Ns-hbpmentioning

confidence: 99%

“…Whether the fact that some myocardium is activated through cell-to-cell myocardial conduction means that dyssynchrony occurs, risking pacing-induced cardiomyopathy, has been debated. 24…”

Section: Distinguishing S-hbp From Ns-hbpmentioning

confidence: 99%

See 2 more Smart Citations

Discriminating electrocardiographic responses to His-bundle pacing using machine learning

Arnold¹,

Howard²,

Gopi

et al. 2020

Cardiovascular Digital Health Journal

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show abstract

“…According to previous studies, both selective and non-selective HBP are comparable and pose an advantage over the right ventricular pacing [3].…”

mentioning

confidence: 77%

His bundle pacing in a patient after heart transplant and complete atrioventricular block

Boczar¹,

Ząbek²,

Ulman³

et al. 2021

Kardiol Pol

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DeepPlayer: An open‐source SignalPlant plugin for deep learning inference

et al. 2022

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Background and Objective: Machine learning has become a powerful tool in several computation domains. The most progressive way of machine learning, deep learning, has already surpassed several algorithms designed by human experts. It also applies to the field of biomedical signal processing. However, while many experts produce deep learning models, there is no software platform for signal processing, allowing the convenient use of pre-trained deep learning models and evaluating them using any inspected signal. This may also hinder understanding, interpretation, and explanation of results. For these reasons, we designed DeepPlayer. It is a plugin for the free signal processing software SignalPlant. The plugin allows loading deep learning models saved in the Open Neural Network Exchange (ONNX) file format and evaluating them on any given signal. Methods:The DeepPlayer plugin and its graphical user interface were designed in C# programming language and the .NET framework. We used the inference library OnnxRuntime, which supports graphics card acceleration. The inference is executed in asynchronous tasks for a live preview and evaluation of the signals. Model outputs can be exported back to SignalPlant for further processing, such as peak detection or thresholding. Results:We developed the DeepPlayer plugin to evaluate deep learning models in SignalPlant.The plugin keeps with SignalPlant's interactive work with signals, such as live preview or easy selection of associated signals. The plugin can load classification or regression models and allows standard pre-processing and post-processing methods. We prepared several deep learning models to test the plugin. Additionally, we provide a tutorial training script that outputs an ONNX format model with correctly set metadata information. These, and the source code of the DeepPlayer plugin, are publicly accessible via GitHub and Google Colab service. Conclusion:The DeepPlayer plugin allows running deep learning models easily and interactively. Therefore, experts and non-AI experts alike can explore and apply deep learning models for (biomedical) signal processing. Its ease of use and interactivity might also contribute to a better understanding and acceptance of AI methods in biomedicine.

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Both selective and nonselective His bundle, but not myocardial, pacing preserve ventricular electrical synchrony assessed by ultra-high-frequency ECG

Cited by 43 publications

References 16 publications

Discriminating electrocardiographic responses to His-bundle pacing using machine learning

Discriminating electrocardiographic responses to His-bundle pacing using machine learning

His bundle pacing in a patient after heart transplant and complete atrioventricular block

DeepPlayer: An open‐source SignalPlant plugin for deep learning inference

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