在心电图信号上使用ConvNeXt-X模型检测混合心血管节律不良疾病
Md Alamin Talukder1, Majdi Khalid2, Mohsin Kazi3
1Department of Computer Science and Engineering, International University of Business Agriculture and Technology, Dhaka, Bangladesh. alamin.cse@iubat.edu.
Scientific reports
|December 5, 2024
概括
这项研究引入了一种新的深度学习方法,使用ConvNeXt-X模型和数据平衡来准确检测心血管节律失常. 混合方法显著提高了分类准确性,有助于早期诊断和治疗.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 心血管节律失常,或心律不规律,带来严重的健康风险,如中风和心力衰竭.
- 准确和早期发现心律失常对于有效的患者管理至关重要.
- 传统的检测方法面临着不平衡数据集的挑战,阻碍了罕见心律失常类型的识别.
研究的目的:
- 开发和评估一种新的混合方法,用于增强心律不整的分类.
- 整合ConvNeXt-X深度学习模型与数据平衡技术,以提高准确性.
- 在MIT-BIH心律失常数据库上评估不同ConvNeXt变体和平衡方法的性能.
主要方法:
- 使用了三种ConvNeXt变体 (ConvNeXtTiny,ConvNeXtBase,ConvNeXtSmall) 来进行心律失常的分类.
- 实施的数据平衡技术:随机过量采样 (RO) 和SMOTE-TomekLink (STL).
- 在基准MIT-BIH心律失常数据库上评估了混合模型.
主要成果:
- 与SMOTE-TomekLink (STL) 结合的ConvNeXtTiny模型实现了最高的精度 (99.75%).
- 使用随机过量采样 (RO) 的ConvNeXtTiny达到了99.72%的准确性.
- 在所有评估的ConvNeXt模型中,STL始终改善了少数群体类检测和整体性能.
结论:
- 通过数据平衡技术增强的ConvNeXt-X模型在可靠和精确的心律失常检测方面表现出高效.
- 拟议的方法为改善心血管医疗保健的诊断准确性提供了显著的潜力.
- 这种方法可以支持临床决策,并通过更好的心律失常管理来提高患者的治疗结果.
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01:28Electrocardiogram Fundamentals
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Introduction
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01:23Holter Monitor: 24-Hour Monitoring
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01:28Disturbances in Heart Rhythm
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