基于深度学习的心音信号检测方法提高心血管疾病检测性能

IF 3.3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Ozgen Safak, Mehmet Tolga Hekim, Tolga Cakmak, Fatih Demir, Kursat Demir
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引用次数: 0

摘要

背景/目的:心血管疾病是世界范围内的主要死亡原因之一。这些疾病的早期诊断可将未来死亡的风险降至最低。用听诊器听心音是诊断心脏病最简单、最快捷的方法之一。虽然心音是一种快速简便的诊断方法,但它们需要重要的专家解释。最近,基于这些专家解释训练的人工智能模型在决策支持系统的开发中变得流行。方法:提出的方法使用2016年流行的PhysioNet/CinC Challenge数据集来处理PCG信号。然后进行谱图图像变换以增加这些信号的代表性。基于深度学习的模型允许同时训练残差和注意块,并使用MLP-mixer模型进行特征提取。结合NCA算法和ReliefF算法的优点,提出了一种从特征集中选择最强特征的新算法。采用SVM算法进行分类。结果:采用该方法,在所有准确性、敏感性、特异性、精密度和f1评分指标上均达到98%以上的成功率。结论:提出了一种基于人工智能的心血管疾病高精度检测决策支持系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving the Detection Performance of Cardiovascular Diseases from Heart Sound Signals with a New Deep Learning-Based Approach.

Background/Objectives: Cardiovascular diseases are among the leading causes of death worldwide. Early diagnosis of these conditions minimizes the risk of future death. Listening to heart sounds with a stethoscope is one of the easiest and fastest methods for diagnosing heart conditions. While heart sounds are a quick and easy diagnostic method, they require significant expert interpretation. Recently, artificial intelligence models trained based on these expert interpretations have become popular in the development of decision support systems. Methods: The proposed approach uses the popular 2016 PhysioNet/CinC Challenge dataset for PCG signals. Spectrogram image transformation was then performed to increase the representativeness of these signals. A deep learning-based model that allows for the simultaneous training of residual and attention blocks and the MLP-mixer model was used for feature extraction. A new algorithm combining the strengths of NCA and ReliefF algorithms was proposed to select the strongest features in the feature set. The SVM algorithm was used for classification. Results: With this proposed approach, over 98% success was achieved in all accuracy, sensitivity, specificity, precision, and F1-score metrics. Conclusions: As a result, an artificial intelligence-based decision support system that detects cardiovascular diseases with high accuracy is presented.

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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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