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解决变异性问题：从心冲击图中准确提取特征成分。

Solving variability: Accurately extracting feature components from ballistocardiograms.

作者信息

Yang Tianyi, Yuan Haihang, Yang Junqi, Zhou Zhongchao, Abe Masayuki, Nakayama Yoshitake, Huang Shao Ying, Yu Wenwei

机构信息

Department of Medical Engineering, Chiba University, Chiba City, Chiba Prefecture, Japan.

Nanayume Co. Ltd, Chiba City, Chiba Prefecture, Japan.

出版信息

Digit Health. 2024 Sep 5;10:20552076241277746. doi: 10.1177/20552076241277746. eCollection 2024 Jan-Dec.

DOI:10.1177/20552076241277746
PMID:39247094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11378244/
Abstract

OBJECTIVE

A ballistocardiogram (BCG) is a vibration signal generated by the ejection of the blood in each cardiac cycle. The BCG has significant variability in amplitude, temporal aspects, and the deficiency of waveform components, attributed to individual differences, instantaneous heart rate, and the posture of the person being measured. This variability may make methods of extracting J-waves, the most distinct components of BCG less generalizable so that the J-waves could not be precisely localized, and further analysis is difficult. This study is dedicated to solving the variability of BCG to achieve accurate feature extraction.

METHODS

Inspired by the generation mechanism of the BCG, we proposed an original method based on a profile of second-order derivative of BCG waveform (2ndD-P) to capture the nature of vibration and solve the variability, thereby accurately localizing the components especially when the J-wave is not prominent.

RESULTS

In this study, 51 recordings of resting state and 11 recordings of high-heart-rate from 24 participants were used to validate the algorithm. Each recording lasts about 3 min. For resting state data, the sensitivity and positive predictivity of proposed method are: 98.29% and 98.64%, respectively. For high-heart-rate data, the proposed method achieved a performance comparable to those of low-heart-rate: 97.14% and 99.01% for sensitivity and positive predictivity, respectively.

CONCLUSION

Our proposed method can detect the peaks of the J-wave more accurately than conventional extraction methods, under the presence of different types of variability. Higher performance was achieved for BCG with non-prominent J-waves, in both low- and high-heart-rate cases.

摘要

目的

心冲击图（BCG）是每个心动周期中血液喷射产生的振动信号。由于个体差异、瞬时心率和被测者的姿势，BCG在幅度、时间方面以及波形成分的缺失方面具有显著的变异性。这种变异性可能会使提取BCG最明显成分J波的方法难以普遍适用，从而无法精确地定位J波，进而难以进行进一步分析。本研究致力于解决BCG的变异性问题，以实现准确的特征提取。

方法

受BCG产生机制的启发，我们提出了一种基于BCG波形二阶导数轮廓（2ndD-P）的原创方法，以捕捉振动的本质并解决变异性问题，从而在J波不突出时也能准确地定位成分。

结果

在本研究中，使用了来自24名参与者的51份静息状态记录和11份高心率记录来验证该算法。每份记录持续约3分钟。对于静息状态数据，所提方法的灵敏度和阳性预测值分别为98.29%和98.64%。对于高心率数据，所提方法的性能与低心率数据相当：灵敏度和阳性预测值分别为97.14%和99.01%。

结论

在存在不同类型变异性的情况下，我们提出的方法比传统提取方法能更准确地检测J波的峰值。对于J波不突出的BCG，在低心率和高心率情况下均取得了更高的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b000/11378244/00c12826bcd2/10.1177_20552076241277746-fig18.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b000/11378244/f580b35acfc5/10.1177_20552076241277746-fig1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b000/11378244/70857e7192dc/10.1177_20552076241277746-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b000/11378244/87a6dad70b14/10.1177_20552076241277746-fig11.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b000/11378244/b03f6af93358/10.1177_20552076241277746-fig15.jpg
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