实用性与精确性相结合:集成多通道PCG传感器的可穿戴背心,用于有效的冠状动脉疾病预筛查

IF 6.3 2区 医学 Q1 BIOLOGY Computers in biology and medicine Pub Date : 2025-05-01 Epub Date: 2025-03-06 DOI:10.1016/j.compbiomed.2025.109904
Matthew Fynn , Kayapanda Mandana , Javed Rashid , Sven Nordholm , Yue Rong , Goutam Saha
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引用次数: 0

摘要

全世界死亡和发病的主要原因是心血管疾病(CVD),其中冠状动脉疾病(CAD)是最大的子类别。不幸的是,心肌梗死或中风可以表现为CAD的第一个症状,强调了早期发现疾病的重要性。因此,全球需要一种具有成本效益、非侵入性、可靠且易于使用的系统来预筛选CAD。以前的研究利用心音图(PCG)信号对CAD引起的弱杂音进行分类。然而,这些研究往往涉及繁琐且不方便的数据收集方法,需要精确的受试者准备和环境条件。本研究提出了一种简单方便的新型数据采集系统(DAQS)。DAQS将多通道PCG传感器集成到可穿戴背心中。整个信号采集过程可以在两分钟内完成,从安装背心到记录信号并拆除它,不需要专业培训。这说明了大规模筛查的潜力,这在目前最先进的方案下是不切实际的。七个PCG信号被采集,六个来自胸部,一个来自受试者的背部,这标志着一种新的方法。我们的分类方法利用线性频率倒谱系数(LFCC)作为特征,并采用支持向量机(SVM)来区分正常和cad影响的心跳,优于适用于便携式应用的其他低计算方法。利用特征级融合,将多个通道组合在一起,最佳组合产生最高的主题级精度和f1分数,分别为80.44%和81.00%,比表现最好的单一通道提高了7%。该系统的性能指标已被证明具有临床意义,使DAQS适合实际使用。此外,该系统在经皮腔内冠状动脉成形术(PTCA)或冠状动脉旁路移植术(CABG)患者的术后监测中显示出前景,可以有效识别干预后的再狭窄病例。
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Practicality meets precision: Wearable vest with integrated multi-channel PCG sensors for effective coronary artery disease pre-screening
The leading cause of mortality and morbidity worldwide is cardiovascular disease (CVD), with coronary artery disease (CAD) being the largest sub-category. Unfortunately, myocardial infarction or stroke can manifest as the first symptom of CAD, underscoring the crucial importance of early disease detection. Hence, there is a global need for a cost-effective, non-invasive, reliable, and easy-to-use system to pre-screen CAD. Previous studies have explored weak murmurs arising from CAD for classification using phonocardiogram (PCG) signals. However, these studies often involve tedious and inconvenient data collection methods, requiring precise subject preparation and environmental conditions. This study proposes using a novel data acquisition system (DAQS) designed for simplicity and convenience. The DAQS incorporates multi-channel PCG sensors into a wearable vest. The entire signal acquisition process can be completed in under two minutes, from fitting the vest to recording signals and removing it, requiring no specialist training. This exemplifies the potential for mass screening, which is impractical with current state-of-the-art protocols. Seven PCG signals are acquired, six from the chest and one from the subject’s back, marking a novel approach. Our classification approach, which utilizes linear-frequency cepstral coefficients (LFCC) as features and employs a support vector machine (SVM) to distinguish between normal and CAD-affected heartbeats, outperformed alternative low-computational methods suitable for portable applications. Utilizing feature-level fusion, multiple channels are combined, and the optimal combination yields the highest subject-level accuracy and F1-score of 80.44% and 81.00%, respectively, representing a 7% improvement over the best-performing single channel. The proposed system’s performance metrics have been demonstrated to be clinically significant, making the DAQS suitable for practical use. Moreover, the system shows promise in post-procedural monitoring for subjects undergoing percutaneous transluminal coronary angioplasty (PTCA) or coronary artery bypass grafting (CABG), effectively identifying cases of restenosis following intervention.
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来源期刊
Computers in biology and medicine
Computers in biology and medicine 工程技术-工程:生物医学
CiteScore
11.70
自引率
10.40%
发文量
1086
审稿时长
74 days
期刊介绍: Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.
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