A Review of Hidden Markov Models and Recurrent Neural Networks for Event Detection and Localization in Biomedical Signals

TL;DR

This paper reviews the use of Hidden Markov Models and Recurrent Neural Networks for detecting and localizing events in biomedical signals, emphasizing their effectiveness, limitations, and application challenges in medical diagnostics.

Contribution

It provides a comprehensive overview of HMMs and RNNs in biomedical event detection, highlighting their strengths, limitations, and application challenges in a unified framework.

Findings

HMMs effectively model physiological process transitions.

RNNs capture complex temporal dependencies in biomedical signals.

Both methods face challenges with signal noise and variability.

Abstract

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.