通过可解释深度学习识别缺血后室性心动过速电生理学研究中的致心律失常部位

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-09-15 DOI:10.1016/j.bspc.2024.106844
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引用次数: 0

摘要

背景和目的心内电图(EGM)中的异常心室电位(AVP)经常被认为是电解剖图(EAM)程序中缺血后室性心动过速(VT)致心律失常部位的标记。对它们的检测严重依赖于操作者,而且非常耗时。本研究探讨了采用可解释深度学习来支持生理性 EGM 和 AVPs 之间的判别。方法通过同步queezed 小波变换,训练了三个卷积神经网络来根据目标信号的时频表示对其进行判别。结果所提出的方法取得了很高的性能,准确率高达 89%。与传统的电压图和局部激活时间图相比,该方法还显示了致心律失常部位的一致性定位。此外,通过使用显著性图谱,AVPs 的判别特征在高频(即 103-125 Hz 频段,通常与每个网络相关)得到了突出显示,这与之前的证据一致。所提出的方法为开发有效的人工智能驱动系统铺平了道路。这些系统将使 VT EAM 程序中的 AVP 识别更快、更可信、更独立于操作人员。此外,即使不在所采用的模型中注入先验知识,对显著性图的分析也表明,CNN 容易根据当前知识自主选择 EGM 的时间频率范围。
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Arrhythmogenic sites identification in post-ischemic ventricular tachycardia electrophysiological studies by explainable deep learning

Background and objective

Abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs) are frequently considered as markers of arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) during electroanatomic mapping (EAM) procedures. Their detection is strongly operator-dependent and time-consuming. This work explores the adoption of explainable deep learning to support the discrimination between physiological EGMs and AVPs.

Methods

Three convolutional neural networks were trained to discriminate the target signals based on their time–frequency representations by synchrosqueezed wavelet transform. The efficacy of the method was assessed on 2561 real bipolar EGMs collected from nine post-ischemic VT patients.

Results

The proposed approach achieved high performance, with accuracy levels reaching up to 89%. It also demonstrated coherent localization of the arrhythmogenic sites with respect to conventional voltage and local activation time maps. Moreover, by using saliency maps, AVPs discriminant signatures were highlighted at high frequencies (i.e., in the 103–125 Hz band, which was generally relevant for every network), in line with prior evidence.

Conclusion

For the first time, deep learning has been successfully applied and robustly evaluated in the field. The proposed approach paves the way to the development of effective AI-driven systems. These systems will enable a faster, trustworthy and operator-independent identification of AVPs in VT EAM procedures. Furthermore, even without injecting prior knowledge in the adopted models, the analysis of saliency maps revealed that CNNs are prone to autonomously select time–frequency ranges of the EGMs in agreement with the current knowledge.

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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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