论文标题
隐藏半马尔科夫模型中状态持续时间的参数化：心电图中的应用
Parameterization of state duration in Hidden semi-Markov Models: an application in electrocardiography
论文作者
论文摘要
这项工作旨在基于从一个示例中学习的时间序列分类提供新的模型。我们假设时间序列可以很好地将其描述为一个参数随机过程，这是一种隐藏的半马尔科夫模型，代表具有可变持续时间的回归模型序列。我们为时间序列模式识别引入了一个参数随机模型，并提供了其参数的最大样本估计。特别是，我们有兴趣检查国家持续时间的两种不同表示形式：i）离散密度分布需要在每个可能的持续时间内进行估计； ii）一个连续密度函数的参数家族，这里是伽马分布，只有两个参数可以估算。对心跳分类的应用揭示了每种替代方案的主要优势和劣势。
This work aims at providing a new model for time series classification based on learning from just one example. We assume that time series can be well characterized as a parametric random process, a sort of Hidden semi-Markov Model representing a sequence of regression models with variable duration. We introduce a parametric stochastic model for time series pattern recognition and provide a maximum-likelihood estimation of its parameters. Particularly, we are interested in examining two different representations for state duration: i) a discrete density distribution requiring an estimate for each possible duration; and ii) a parametric family of continuous density functions, here the Gamma distribution, with just two parameters to estimate. An application on heartbeat classification reveals the main strengths and weaknesses of each alternative.
