A review of Hidden Markov models and Recurrent Neural Networks for event detection and localization in biomedical signals

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties these indicate the nature interaction dynamics among maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in structure which makes isolating and ability to differentiate between them, indispensable. Computer aided diagnosis systems are ubiquitous nowadays almost every medical facility more closely wearable technology, rhythm event detection is first many intelligent steps they perform. How isolated? develop model can describe transition time? Many methods exist literature address questions perform decoding biomedical into separate rhythms. In here, we demystify most effective used for isolation events time series highlight way were applied different how contribute information fusion. key strengths limitations also discussed well challenges encountered application signals.