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相关概念视频

Probability Laws01:49

Probability Laws

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Overview
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Binomial Probability Distribution01:15

Binomial Probability Distribution

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A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
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Poisson Probability Distribution01:09

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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开发一种概率模型,用电脑电图来检测高分辨率的嗜睡.

Ahnaf Rashik Hassan1, Muammar Kabir1, Shumit Saha2

  • 1Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada; KITE, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.

Sleep medicine
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概括
此摘要是机器生成的。

这项研究开发了一种新的脑电图 (EEG) 模型,以准确量化睡眠开始过程,区分清醒,嗜睡和睡眠. 该模型实现了高检测准确度,为现实世界的嗜睡监测提供了潜力.

关键词:
分类算法分类算法分类算法集群验证的验证昏昏欲睡的情况 昏昏欲睡的情况电脑脑电图 (EEG) 是一种电脑电图.睡眠 睡眠 睡眠 睡眠清醒的状态 清醒的状态

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科学领域:

  • 神经科学是一个神经科学.
  • 睡眠医学 睡眠医学
  • 生物医学工程 生物医学工程

背景情况:

  • 从清醒过渡到睡眠是一个渐进的过程,在当前的睡眠评分方法中经常过于简单化.
  • 准确量化睡眠开始动态对于理解睡眠障碍和昼夜节律至关重要.

研究的目的:

  • 开发一种高效,高分辨率和可靠的模型,用于定量评估清醒/睡眠过渡动态.
  • 使用电脑电图 (EEG) 信号来精确测量睡眠开始.

主要方法:

  • 从53名受试者那里收集过夜的EEG数据.
  • 从EEG中提取相对功率特征,构建3秒段的清醒概率模型.
  • 通过使用统计和集群质量分析,确定并验证了三个不同的集群:清醒,嗜睡和睡眠.

主要成果:

  • 该模型成功地区分了清醒,嗜睡和睡眠状态.
  • 高集群紧度是由0.74的平均轮值和0.43.4的戴维斯-博尔丁指数表示的.
  • 该方法实现了93.21%的检测准确度,在确定的集群中存在显著差异 (p < .0001).

结论:

  • 开发的基于EEG的方法准确地检测出短暂的清醒,嗜睡和睡眠在多睡眠学数据中的短暂情节.
  • 这项概念验证研究表明,在各种环境中,对于在昏昏欲睡检测中的应用有前途.