Compared with snapshot health information, long-term and high-frequency physiological time series provides health information from the other dimension. I will discuss recently developed signal processing tools in nonlinear-type time-frequency analysis and manifold learning inspired by dealing with this kind of time series. The developed tools will simultaneously handle several challenges when extracting useful biorhythm features—the time series is usually of single channel and composed of multiple oscillatory components with complicated statistical features, like time-varying amplitude, frequency and non-sinusoidal pattern, and the signal quality is often compromised by nonstationary noise and artifact. I will demonstrate how to apply it to some clinical challenges. The established theoretical supports will also be discussed if time permits.
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