相关实验视频
Updated: Jun 27, 2026
06:49
Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
Published on: June 16, 2014
17.2K
基于时间序列成像方法的BCG信号质量评估
Sungtae Shin1, Soonyoung Choi1, Chaeyoung Kim2
1Department of Mechanical Engineering, Dong-A University, Busan 49315, Republic of Korea.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
这项研究开发了一种方法来分类手臂胆心图 (BCG) 信号质量,以准确,非侵入性血压监测. 使用基于图像的深度学习,该系统在区分高质量和低质量的BCG信号方面实现了87.5%的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 可穿戴技术可穿戴技术
背景情况:
- 弹性心电图 (BCG) 信号为使用可穿戴设备中的加速度计进行非侵入性,持续的血压监测提供了潜力.
- BCG信号容易受到运动器件的噪音的影响,降低了血压估计的准确性.
- 有效的信号质量分类对于维持BCG血压测量系统的性能至关重要.
研究的目的:
- 开发和评估一种二元分类模型,以区分高质量和低质量的手臂弹性心电图 (BCG) 信号.
- 为了防止噪音高的BCG数据导致的非侵入性血压估计的性能下降.
- 探索时间序列成像技术和卷积神经网络 (CNN) 架构用于BCG信号分类.
主要方法:
- 用四种时间序列成像方法 (重复图,格雷梅角总和场,格雷梅角差异场,马尔科夫过渡场) 来将BCG信号转换为图像.
- 包括ResNet,SqueezeNet,DenseNet和LeNet在内的卷积神经网络 (CNN) 模型被用于图像分类.
- 用于培训,验证和测试分类模型的数据集是9626个BCG节拍.
主要成果:
- 在与ResNet和SqueezeNet CNN模型相结合时,Gramain角差场方法实现了高二进制分类准确率,高达87.5%.
- 这表明将时间BCG信号转换为质量评估的图像表示的有效性.
- 该研究成功地展示了一种方法来识别可用的BCG信号,以可靠地监测血压.
结论:
- 开发的二元分类模型有效地区分高质量和低质量的BCG信号,这对于准确的非侵入性血压监测至关重要.
- 时间序列成像与深度学习CNN相结合,为BCG信号质量评估提供了强大的方法.
- 这种方法有望提高连续血压测量可穿戴设备的可靠性和性能.
更多相关视频
09:09Cortical Actin Flow in T Cells Quantified by Spatio-temporal Image Correlation Spectroscopy of Structured Illumination Microscopy Data
Published on: December 17, 2015
9.8K
12:54Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
Published on: October 2, 2021
3.3K
相关概念视频
01:17Pulse amplitude and quality
Pulse amplitude is a crucial indicator of cardiac health because it provides valuable insights into the strength of left ventricular contractions and the overall uniformity of blood circulation within the vasculature. The strength of the pulse is directly related to the force with which the heart contracts and the volume of blood being pumped.
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
A weak or absent pulse may indicate reduced cardiac output or poor left ventricular contraction, which can be signs of cardiovascular dysfunction or...
01:30Classification of Signals
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
01:22Basic Continuous Time Signals
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
01:22Basic Operations on Signals
Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
01:11Sampling Continuous Time Signal
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
In the...
01:10Reconstruction of Signal using Interpolation
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...