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相关概念视频

Energy and Power Signals01:17

Energy and Power Signals

In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Convergence of Fourier Series01:21

Convergence of Fourier Series

The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at zero. It...
Signal Flow Graphs01:18

Signal Flow Graphs

Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
Bewley Lattice Diagram01:12

Bewley Lattice Diagram

The Bewley lattice diagram, developed by L. V. Bewley, effectively organizes the reflections occurring during transmission-line transients. It visually represents how voltage waves propagate and reflect within a transmission line, making it easier to understand the complex interactions that occur.

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相关实验视频

Updated: Jun 8, 2026

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping
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从雷达信号生成心电图波形:一个深度学习的观点.

Farhana Ahmed Chowdhury1, Md Kamal Hosain1, Md Sakib Bin Islam2

  • 1Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi, 6204, Bangladesh.

Computers in biology and medicine
|May 15, 2024
PubMed
概括

这项研究引入了一种新的深度学习方法,可以从非接触式雷达数据生成心电图 (ECG) 波形,克服持续心脏监测传统方法的局限性.

关键词:
在美国,CNN是CNN.深度学习是一种深度学习.这是一个ECGECGECGECGECG.这是一个MultiResLinkNet.原始雷达数据 原始雷达数据

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Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能
  • 信号处理 信号处理

背景情况:

  • 传统的心电图 (ECG) 诊断面临诸多挑战,包括患者的不适,运动器件,以及需要专门的设备和训练有素的专业人员.
  • 用于持续ECG监测的可穿戴传感器在重症监护机构可能不切实际.
  • 尽管存在局限性,心电图仍然对诊断和监测心脏疾病至关重要,因为它具有非侵入性和详细的心脏信息.

研究的目的:

  • 开发一种基于深度学习的创新方法,从非接触式雷达数据生成连续的ECG波形.
  • 消除对侵袭性或可穿戴生物传感器和昂贵设备在ECG采集中的需求.
  • 为了实现心脏病的远程监测,特别是高风险患者.

主要方法:

  • 提出了一个名为MultiResLinkNet的新型一维卷积神经网络 (1D CNN) 模型.
  • 通过使用公开可访问的雷达基准数据集与地面真相生理信号,训练和评估端到端DL架构.
  • 通过使用时间和光谱测量,评估了该框架在将雷达信号转换为心电图数据的性能,用于Resting,Valsalva和Apnea (RVA) 场景.

主要成果:

  • 与最先进的网络相比,MultiResLinkNet模型展示了优越的ECG细分性能.
  • 获得了高的时间平均值 (休息时间:66.09 ± 19.33,瓦萨尔瓦:60.14 ± 21.92,RVA:61.86 ± 21.37).
  • 显示的高光谱相关值 (休息时间: 82.44 ± 18.42,瓦萨尔瓦: 77.05 ± 23.26,呼吸暂停: 74.66 ± 23.17,RVA: 79.96 ± 20.82) 的误差最小.
  • 定性评估显示,生成和实际的心电图波形之间存在很强的相似性.

结论:

  • 拟议的深度学习方法有效地从非接触式雷达数据生成连续的ECG波形.
  • 这种创新方法为远程和非侵入性心脏监测提供了一个有希望的解决方案.
  • 这项技术在监测高风险患者,包括接受手术的患者方面具有重大潜力.