Segmentation, alignment and statistical analysis of biosignals with application to disease classification

Abstract

We present a novel methodology for a comprehensive statistical analysis of approximately periodic biosignal data. There are two main challenges in such analysis: (1) the automatic extraction (segmentation) of cycles from long, cyclostationary biosignals and (2) the subsequent statistical analysis, which in many cases involves the separation of temporal and amplitude variabilities. The proposed framework provides a principled approach for statistical analysis of such signals, which in turn allows for an efficient cycle segmentation algorithm. This is achieved using a convenient representation of functions called the square-root velocity function (SRVF). The segmented cycles, represented by SRVFs, are temporally aligned using the notion of the Karcher mean, which in turn allows for more efficient statistical summaries of signals. We show the strengths of this method through various disease classification experiments. In the case of myocardial infarction detection and localization, we show that our method compares favorably to methods described in the current literature.

Keywords:

• functional data analysis
• cyclostationary biosignal segmentation
• myocardial infarction
• gait
• disease classification

Acknowledgements

This research was supported in part by NSF grants DMS 0915003, IIS 1217515 and DMS 1208959. We would like to thank Dr John E. Bayouth from the Department of Radiation Oncology at the University of Iowa for providing the respiratory data.