  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.

Machine Learning;Signal Processing;Quantitative Methods