A system for biomedical audio signal processing based on high performance computing techniques

IF 5.8 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
A. Muñoz-Montoro, Pablo Revuelta, Alberto Villalón-Fernández, Rubén Muñiz, J. Ranilla
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引用次数: 1

Abstract

. In this paper, a noninvasive portable prototype is presented for biomedical audio signal processing. The proposed prototype is suitable for monitoring the health of patients. The proposed hardware setup consists of a cost-effective microphone, multipurpose microcontroller and computing node that could be a mobile phone or general-purpose computer. Using parallel and high-performance techniques, this setup allows one to register and wirelessly multicast the recorded biomedical signals to computing nodes in real time. The developed prototype was used as a case study to estimate the heart rate (HR) from the captured biomedical audio signal. In this regard, the developed algorithm for estimating HR comprises three stages: preprocessing, separation, and HR estimation. In the first stage, the signal captured by the microphone is adapted for processing. Subsequently, a separation stage was proposed to alleviate the acoustic interference between the lungs and heart. The separation is performed by combining a non-negative matrix factorization algorithm, clustering approach, and soft-filter strategy. Finally, HR estimation was obtained using a novel and efficient method based on the autocorrelation function. The developed prototype could be used not only for the estimation of the HR, but also for the retrieval of other biomedical information related to the recording of cardiac or respiratory audio signals. The proposed method was evaluated using well-known datasets and compared with state-of-the-art algorithms for source-separation. The results showed that it is possible to obtain an accurate separation and reliable real-time estimation in terms of source separation metrics and relative error in the tested scenarios by combining multi-core architectures with parallel and high-performance techniques. Finally, the proposed prototype was validated in a real-world scenario.
基于高性能计算技术的生物医学音频信号处理系统
. 提出了一种用于生物医学音频信号处理的无创便携式样机。所提出的原型适用于监测患者的健康状况。提出的硬件设置包括一个经济高效的麦克风,多用途微控制器和计算节点,可以是移动电话或通用计算机。使用并行和高性能技术,这种设置允许人们实时注册和无线多播记录的生物医学信号到计算节点。该原型被用作案例研究,用于从捕获的生物医学音频信号中估计心率(HR)。在这方面,开发的算法估计人力资源包括三个阶段:预处理,分离和人力资源估计。在第一阶段,对麦克风捕获的信号进行处理。随后,提出了一个分离阶段,以减轻肺和心脏之间的声干扰。分离是通过结合非负矩阵分解算法、聚类方法和软滤波策略来实现的。最后,采用一种新颖有效的基于自相关函数的HR估计方法。所开发的样机不仅可用于HR的估计,还可用于与心脏或呼吸音频信号记录相关的其他生物医学信息的检索。使用已知的数据集对所提出的方法进行了评估,并与最先进的源分离算法进行了比较。结果表明,将多核架构与并行和高性能技术相结合,可以在测试场景中获得准确的分离和可靠的源分离度量和相对误差实时估计。最后,在实际场景中验证了所提出的原型。
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来源期刊
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering 工程技术-工程:综合
CiteScore
9.90
自引率
21.50%
发文量
21
审稿时长
>12 weeks
期刊介绍: Integrated Computer-Aided Engineering (ICAE) was founded in 1993. "Based on the premise that interdisciplinary thinking and synergistic collaboration of disciplines can solve complex problems, open new frontiers, and lead to true innovations and breakthroughs, the cornerstone of industrial competitiveness and advancement of the society" as noted in the inaugural issue of the journal. The focus of ICAE is the integration of leading edge and emerging computer and information technologies for innovative solution of engineering problems. The journal fosters interdisciplinary research and presents a unique forum for innovative computer-aided engineering. It also publishes novel industrial applications of CAE, thus helping to bring new computational paradigms from research labs and classrooms to reality. Areas covered by the journal include (but are not limited to) artificial intelligence, advanced signal processing, biologically inspired computing, cognitive modeling, concurrent engineering, database management, distributed computing, evolutionary computing, fuzzy logic, genetic algorithms, geometric modeling, intelligent and adaptive systems, internet-based technologies, knowledge discovery and engineering, machine learning, mechatronics, mobile computing, multimedia technologies, networking, neural network computing, object-oriented systems, optimization and search, parallel processing, robotics virtual reality, and visualization techniques.
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