Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment.

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-07-17 DOI:10.1088/1361-6579/ad605c
I R de Vries, J O E H van Laar, M B van der Hout-van der Jagt, R Vullings
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Abstract

Objective.Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method.Approach.This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG.Main results.The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities.Significance.This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

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利用稀疏域卡尔曼滤波和向量心电图环路配准对无创胎儿心电图进行无监督去噪。
尽管心电图(ECG)具有作为胎儿监测或诊断工具的潜力,但无创胎儿心电图的使用因测量过程中相对较高的噪声和胎儿移动而变得复杂。此外,基于机器学习的解决方案也很难获得干净的参考数据。为了解决这些问题,这项工作旨在将胎儿旋转校正与心电图去噪整合到一个单一的无监督端到端可训练方法中。该方法使用心电图的三维表示法向量心电图(VCG)作为输入,并扩展了之前引入的卡尔曼-LISTA 方法,在估计胎儿旋转时使用卡尔曼滤波器,同时对旋转校正后的向量心电图进行去噪。结果表明,该方法的性能优于去噪自动编码器 3dB 以上,同时旋转跟踪误差小于 33°。此外,该方法还对心电图导联之间的信噪比差异和不同旋转速度具有鲁棒性。今后的工作应着眼于提高该方法的通用性,并评估该方法在研究和临床应用中的价值。这种价值可能不仅来自去噪胎儿心电图,还来自该方法对胎儿旋转的客观测量,因为它有可能早期发现胎儿并发症。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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