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Electrocardiogram

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Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals

James J Yang¹, Anne Buu²

¹Department of Biostatistics and Data Science, University of Texas Health Science Center, Houston, Texas, U.S.A.

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Summary

This summary is machine-generated.

An adaptive sequential Singular Spectrum Analysis (SSA) method optimizes window length for signal extraction from noisy time series. This approach improves accuracy and efficiency for both short and long datasets, reducing reconstruction errors.

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Area of Science:

Time Series Analysis
Signal Processing
Data Science

Background:

Singular Spectrum Analysis (SSA) is effective for signal extraction from noisy time series.
Conventional SSA's performance and computational cost are highly dependent on window length selection.
Large window lengths are problematic for long time series, increasing errors and computational burden.

Purpose of the Study:

To introduce an adaptive sequential SSA method for optimal window length selection.
To enhance signal extraction efficiency and minimize reconstruction errors.
To provide a versatile SSA method applicable to time series of all lengths.

Main Methods:

Developed an adaptive sequential SSA algorithm.
Iteratively selected optimal window lengths for eigen-sequence extraction.
Validated the method using simulated data (sinusoidal functions and noise) and real-world heart rate data.

Main Results:

The adaptive sequential SSA method efficiently extracts essential eigen-sequences with minimal reconstruction error.
Demonstrated efficacy on both simulated and real-world datasets, including heart rate data.
Identified distinct patterns in heart rate data related to vaping events.

Conclusions:

The adaptive sequential SSA method is a robust and flexible tool for signal extraction.
The method addresses limitations of conventional SSA, particularly for long time series.
Future research can leverage extracted eigen-sequences for digital biomarker development, such as for vaping behavior.