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.
翻译: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节奏。这些节奏的特性表明生理过程之间相互作用的动态性质,保持自足状态。与疾病或疾病有关的异常现象通常表现为使这些节奏和区分这些节奏的能力分离的节奏结构中断,这是不可或缺的。计算机辅助诊断系统几乎在每一个医疗设施中都是无处不在的,在可磨损的技术方面更为密切,节奏或事件探测是它们所执行的许多智能步骤中的第一个。这些节奏是如何孤立的?如何发展一种模型来描述过程在时间上的转变?文献中有许多方法可以解决这些问题并将生物医学信号解密成不同的节奏。在这里,我们解开用于探测和隔离节奏或时间序列中的事件的最有效方法的神秘性,并突出这些方法应用于不同的生物医学信号的方式以及它们如何促进信息融合。这些方法的关键优点和局限性也得到了讨论,生物医学信号的应用也遇到了挑战。