在远程医疗中用于多源混合频数据融合的新型稀疏线性混合模型
Wesam Alramadeen1, Yu Ding1, Carlos Costa2
1Department of Systems Science and Industrial Engineering, State University of New York at Binghamton, Binghamton, NY, USA 13902, USA.
IISE transactions on healthcare systems engineering
|August 28, 2023
概括
这项研究引入了一种新的稀疏线性混合模型,用于从复杂的健康数据中预测睡眠障碍严重程度指标. 该模型准确地识别了关键特征,改善了远程监控中的自动诊断.
科学领域:
- 数字健康数字健康
- 生物统计学 生物统计学
- 心脏病学 心脏病学
背景情况:
- 数字健康和远程监控产生了庞大而复杂的数据集.
- 现有的模型在多来源,混合频率的健康数据方面扎.
- 缺乏用于睡眠障碍的疾病严重性指标 (DSI) 的自动预测.
研究的目的:
- 从多来源,混合频率数据开发一个严格的DSI预测模型.
- 为解决睡眠障碍远程监控高维数据方面的挑战.
- 为了实现睡眠障碍的自动监测和诊断.
主要方法:
- 提出了一个稀疏的线性混合模型,使用修改的乔莱斯基分解和组激光罚款.
- 开发了一种新的预期最大化 (EM) 算法,与主要化最大化 (MM) 集成,用于模型估计.
- 将该方法应用于用于睡眠障碍远程监测和诊断的SHHS数据集.
主要成果:
- 确定了与现有的睡眠障碍研究相一致的显著特征组.
- 与基准方法相比,拟议的方法显示出更高的预测准确性.
- 成功地将该模型应用于现实世界的远程监控数据,以诊断睡眠障碍.
结论:
- 开发的稀疏线性混合模型有效地从复杂的健康数据中预测DSI.
- 这种方法增强了自动睡眠障碍监测和诊断的功能.
- 这些发现支持在数字健康应用中使用先进的统计建模.
更多相关视频
11:28Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
11.7K
08:19Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
Published on: October 20, 2023
1.1K
相关概念视频
Linear Approximation in Frequency Domain
110
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
110
Linear Approximation in Time Domain
100
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
100
Reconstruction of Signal using Interpolation
234
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...
234
Sampling Continuous Time Signal
275
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...
275
