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通过具有类微分算子的小波变换对异常类中的可电击和不可电击心律失常进行高精度区分。

High accuracy distinction of shockable and non-shockable arrhythmias in abnormal classes through wavelet transform with pseudo differential like operators.

机构信息

Graduate School of Engineering, Kanagawa University, Yokohama, Japan.

Inst. Angewandte Mathematik, and HCM, University of Bonn, Bonn, Germany.

出版信息

Sci Rep. 2023 Jun 12;13(1):9513. doi: 10.1038/s41598-023-36463-z.

Abstract

Arrhythmia is an abnormal rhythm of the heart which leads to sudden death. Among these arrhythmias, some are shockable, and some are non-shockable arrhythmias with external defibrillation. The automated external defibrillator (AED) is used as the automated arrhythmia diagnosis system and requires an accurate and rapid decision to increase the survival rate. Therefore, a precise and quick decision by the AED has become essential in improving the survival rate. This paper presents an arrhythmia diagnosis system for the AED by engineering methods and generalized function theories. In the arrhythmia diagnosis system, the proposed wavelet transform with pseudo-differential like operators-based method effectively generates a distinguishable scalogram for the shockable and non-shockable arrhythmia in the abnormal class signals, which leads to the decision algorithm getting the best distinction. Then, a new quality parameter is introduced to get more details by quantizing the statistical features on the scalogram. Finally, design a simple AED shock and non-shock advice method by following this information to improve the precision and rapid decision. Here, an adequate topology (metric function) is adopted to the space of the scatter plot, where we can give different scales to select the best area of the scatter plot for the test sample. As a consequence, the proposed decision method gives the highest accuracy and rapid decision between shockable and non-shockable arrhythmias. The proposed arrhythmia diagnosis system increases the accuracy to 97.98%, with a gain of 11.75% compared to the conventional approach in the abnormal class signals. Therefore, the proposed method contributes an additional 11.75% possibility for increasing the survival rate. The proposed arrhythmia diagnosis system is general and could be applied to distinguish different arrhythmia-based applications. Also, each contribution could be used independently in various applications.

摘要

心律失常是一种异常的心脏节律,可导致猝死。在这些心律失常中,有些是可电击的,有些是非电击性心律失常,需要外部除颤。自动体外除颤器(AED)是一种自动化的心律失常诊断系统,需要做出准确而快速的决策,以提高生存率。因此,AED 做出精确而快速的决策对于提高生存率至关重要。本文通过工程方法和广义函数理论,提出了一种 AED 心律失常诊断系统。在心律失常诊断系统中,所提出的基于拟微分算子的小波变换方法有效地为异常类信号中的可电击性和非电击性心律失常生成了可区分的标度图,从而使决策算法获得最佳区分。然后,引入了一个新的质量参数,通过量化标度图上的统计特征来获得更多细节。最后,根据这些信息设计了一种简单的 AED 电击和非电击建议方法,以提高精度和快速决策。这里,采用了足够的拓扑(度量函数)到散点图的空间中,我们可以在其中为测试样本选择散点图的最佳区域赋予不同的尺度。因此,所提出的决策方法在可电击性和非电击性心律失常之间给出了最高的准确性和快速决策。所提出的心律失常诊断系统在异常类信号中,将准确率提高到了 97.98%,比传统方法提高了 11.75%。因此,所提出的方法为提高生存率增加了 11.75%的可能性。所提出的心律失常诊断系统具有通用性,可以应用于区分不同的基于心律失常的应用。此外,每个贡献都可以独立应用于各种应用中。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4546/10261052/2f4c954e8095/41598_2023_36463_Fig1_HTML.jpg

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