论文标题
对隐藏的马尔可夫模型和复发性神经网络的综述，用于生物医学信号中的事件检测和定位
A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signals
论文作者
论文摘要
生物医学信号具有控制我们日常身体活动的复杂生理过程的标志性节奏。这些节奏的特性表明了维持稳态的生理过程之间相互作用动态的性质。与疾病或疾病相关的异常通常是在节奏结构中的破坏，这使得隔离这些节奏和区分它们之间的能力是必不可少的。如今，计算机辅助诊断系统在几乎每个医疗机构中都无处不在，并且在可穿戴技术方面更加紧密，节奏或事件检测是它们执行的许多智能步骤中的第一个。这些节奏是如何隔离的？如何开发可以描述及时过程之间过渡的模型？文献中存在许多方法，可以解决这些问题并将生物医学信号解码为单独的节奏。在这里，我们揭示了时间序列中用于检测和隔离的节奏或事件的最有效方法，并突出显示它们应用于不同的生物医学信号的方式以及它们如何贡献信息融合。还讨论了这些方法的关键优势和局限性，以及在生物医学信号中遇到的挑战。
Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals.
