AttBiLFNet:用于在不平衡心电图信号中准确、高效地检测心律失常的新型混合网络。

IF 2.6 4区 工程技术 Q1 Mathematics
Enes Efe, Emrehan Yavsan
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引用次数: 0

摘要

在心血管疾病领域,心律失常是导致猝死的主要异常现象之一。包括心律失常在内的这些异常现象可通过心电图检测出来,而心电图是分析心脏疾病的重要组成部分。然而,心电图等传统方法面临主观分析和监测时间有限等挑战。本研究提出了一种新型混合模型 AttBiLFNet,用于精确检测心电图信号中的心律失常,包括不平衡类分布。AttBiLFNet 集成了双向长短期记忆(BiLSTM)网络和卷积神经网络(CNN),并使用焦点损失函数纳入了注意力机制。这种架构能够利用 BiLSTM 的双向信息流自主提取特征,这在捕捉长距离依赖关系方面被证明是非常有利的。注意力机制提高了模型对输入序列相关片段的关注度,这在类不平衡分类场景中尤为有利,因为在这种场景中,少数类样本往往会被忽略。焦点损失函数有效地解决了类不平衡的影响,从而提高了整体分类性能。所提出的 AttBiLFNet 模型达到了 99.55% 的准确率和 98.52% 的精确率。此外,还计算了 MF1、K 分数和灵敏度等性能指标,并将该模型与文献中的各种方法进行了比较。经验证据表明,AttBiLFNet 在准确度和计算效率方面都优于其他方法。引入的模型是及时识别心律失常的可靠工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

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来源期刊
Mathematical Biosciences and Engineering
Mathematical Biosciences and Engineering 工程技术-数学跨学科应用
CiteScore
3.90
自引率
7.70%
发文量
586
审稿时长
>12 weeks
期刊介绍: Mathematical Biosciences and Engineering (MBE) is an interdisciplinary Open Access journal promoting cutting-edge research, technology transfer and knowledge translation about complex data and information processing. MBE publishes Research articles (long and original research); Communications (short and novel research); Expository papers; Technology Transfer and Knowledge Translation reports (description of new technologies and products); Announcements and Industrial Progress and News (announcements and even advertisement, including major conferences).
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