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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Detection of Junctional Ectopic Tachycardia by Central Venous Pressure
verfasst von : Xin Tan, Yanwan Dai, Ahmed Imtiaz Humayun, Haoze Chen, Genevera I. Allen, Parag N. Jain MD
Erschienen in: Artificial Intelligence in Medicine
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Abstract
Central venous pressure (CVP) is the blood pressure in the venae cavae, near the right atrium of the heart. This signal waveform is commonly collected in clinical settings, and yet there has been limited discussion of using this data for automatically detecting and monitoring arrhythmia and other cardiac events. In this paper, we introduce a signal processing and feature engineering pipeline for CVP waveform analysis. Through a case study on pediatric junctional ectopic tachycardia (JET), we show that our extracted CVP features reliably detect JET with comparable results to the more commonly used electrocardiogram (ECG) features. This machine learning pipeline can thus improve the clinical diagnosis and ICU monitoring of arrhythmia. It can also corroborate and complement the ECG-based diagnosis, especially when the ECG measurements are unavailable or corrupted.