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.

生物医学信号带有控制我们日常身体活动的复杂生理过程的特征性节律。这些节律的性质表明维持动态平衡的生理过程之间相互作用动力学的性质。与疾病或病症相关的异常通常表现为节律结构的破坏，这使得隔离这些节律以及区分它们的能力是必不可少的。如今，计算机辅助诊断系统已在几乎每个医疗机构中普遍存在，并且在可穿戴技术中更加紧密地存在，并且节奏或事件检测是它们执行的许多智能步骤中的第一步。这些节奏是如何隔离的？如何开发一个可以描述流程之间及时过渡的模型？文献中存在许多解决这些问题并执行将生物医学信号解码为独立节奏的方法。在这里，我们将揭开用于检测和隔离时间序列中的节律或事件的最有效方法的神秘性，并重点介绍将其应用于不同生物医学信号的方式以及它们如何促进信息融合。还讨论了这些方法的主要优势和局限性，以及在生物医学信号中应用所遇到的挑战。我们将揭开用于检测和隔离时间序列中的节律或事件的最有效方法的神秘性，并重点介绍将其应用于不同生物医学信号的方式以及它们如何促进信息融合。还讨论了这些方法的主要优势和局限性，以及在生物医学信号中应用所遇到的挑战。我们将揭开用于检测和隔离时间序列中的节律或事件的最有效方法的神秘性，并重点介绍将其应用于不同生物医学信号的方式以及它们如何促进信息融合。还讨论了这些方法的主要优势和局限性，以及在生物医学信号中应用所遇到的挑战。