Ordinal symbolic analysis and its application to biomedical recordings

Area of Science:

• * Computational neuroscience
• * Biomedical signal processing
• * Time-series analysis

Background:

• * Traditional time-series analysis methods face limitations in capturing complex dynamics.
• * Symbolic dynamics provides a framework for representing and analyzing complex systems.
• * Ordinal symbolic analysis emerges as a novel approach bridging these fields.

Purpose of the Study:

• * To review the emerging field of ordinal symbolic analysis.
• * To elucidate its relationship with symbolic dynamics and representations.
• * To highlight its applications in analyzing biomedical recordings, specifically epilepsy data.

Main Methods:

• * Review of existing literature on ordinal symbolic analysis.
• * Explanation of the theoretical underpinnings connecting ordinal analysis to symbolic dynamics.
• * Case study illustrating applications using epilepsy electroencephalogram (EEG) data.

Main Results:

• * Ordinal symbolic analysis provides a robust method for characterizing time-series complexity.
• * Demonstrated effectiveness in distinguishing between different states in biomedical signals.
• * Successful application in analyzing patterns within epilepsy patient data.

Conclusions:

• * Ordinal symbolic analysis represents a significant advancement in time-series analysis.
• * Its application to biomedical data, such as epilepsy, shows great promise.
• * Further research is warranted to explore its full potential in various scientific domains.