Design of a convolutional neural network for classification of biomedical signals

IF 1.3 4区 计算机科学 Q1 Computer Science
Jaime Jalomo, Edith Preciado, Jorge Gudiño
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引用次数: 0

Abstract

Biomedical signals are current case of Avant-garde study, thanks to advances in artificial intelligence, every day new methods are implemented that are useful for the treatment of this signals, mainly to detect anomalies or diseases with greater precision. A solution on base of the Deep Learning is proposed, this technology has proven to be efficient in handling high-level feature data, in it featured neural networks convolutionals (NNC) which are ideal in image management. In this paper, electrocardiographic signals (ECG) designed from a dynamic mathematical model in a two convolution layer NNC for classification are used.
用于生物医学信号分类的卷积神经网络设计
生物医学信号是前卫研究的当前案例,由于人工智能的进步,每天都有新的方法被实施,这些方法对治疗这些信号有用,主要是为了更精确地检测异常或疾病。提出了一种基于深度学习的解决方案,该技术已被证明在处理高级特征数据方面是有效的,其中特征神经网络卷积(NNC)是图像管理的理想选择。本文利用动态数学模型设计的心电信号在两层卷积神经网络中进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IBM Journal of Research and Development
IBM Journal of Research and Development 工程技术-计算机:硬件
自引率
0.00%
发文量
0
审稿时长
6-12 weeks
期刊介绍: The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems. Papers are written for the worldwide scientific research and development community and knowledgeable professionals. Submitted papers are welcome from the IBM technical community and from non-IBM authors on topics relevant to the scientific and technical content of the Journal.
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