Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-10-11 DOI:10.1016/j.cmpb.2024.108455
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引用次数: 0

Abstract

Background and Objective:

Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals.

Methods:

A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used.

Results:

The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods.

Conclusions:

The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.
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利用从希尔伯特-黄变换和小波变换中提取的心电图特征与可解释视觉变换器和 CNN 模型的多模态融合,对心脏性猝死进行早期预测。
背景和目的:心脏性猝死(SCD)是一个严重的健康问题,其特点是心脏功能突然衰竭,通常由心室颤动(VF)引起。早期预测 SCD 对及时干预至关重要。然而,目前的方法只能在 SCD 发生前几分钟进行预测,从而限制了干预时间。本研究旨在开发一种基于深度学习的模型,利用心电图(ECG)信号对 SCD 进行早期预测:方法:开发了一种基于多模态可解释深度学习的模型,用于分析 SCD 发病前 5 至 30 分钟不连续时间间隔的心电信号。将原始心电信号、通过小波变换生成的二维扫描图以及通过心电信号的希尔伯特-黄变换(HHT)生成的二维希尔伯特频谱应用于多种深度学习算法。对于原始心电图,采用一维卷积神经网络(1D-CNN)和长短期记忆网络相结合的方法进行特征提取和时间模式识别。此外,为了从扫描图和希尔伯特频谱中提取和分析特征,还使用了视觉变换器(ViT)和二维神经网络:所开发的模型在提前 30 分钟预测 SCD 发病方面取得了较高的性能,准确率、精确率、召回率和 F1 分数分别为 98.81%、98.83%、98.81% 和 98.81%。此外,所提出的模型还能准确地对 SCD 患者和正常对照组进行分类,准确率达到 100%。因此,所提出的方法优于现有的最先进方法:所开发的模型能够捕捉到 SCD 发病前多个离散时间间隔(从 5 分钟到 30 分钟,以 5 分钟为增量)记录的心电信号上的各种模式,从而对 SCD 进行鉴别。所提出的模型大大提高了早期 SCD 的预测能力,为高危患者的连续心电图监测提供了宝贵的工具。
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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