Actigraphy Signal Computation from Physiological Artifacts
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Solution Overview
Problem
Existing physiological monitoring systems that do not include actigraphy devices struggle to measure body movements, leading to artifacts in signals from sensors like ECG and respiratory effort, which affect diagnostic accuracy in long-term monitoring.
Innovation Solution
A system that computes a Body Motion Artifact (BMA) signal from physiological parameter signals using local signal variance, Short-Time Fourier Transform, or wavelet transform, and then generates an actigraphy signal to quantify body movements, allowing for body movement estimation without traditional actigraphy sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional actigraphy sensors (accelerometers) are used to measure body movements, then body motion can be directly measured, but the device complexity and cost increase
Solution Approach 1:
The patent uses physiological signals (ECG, respiratory effort) as intermediary carriers to extract body motion information. Instead of directly measuring motion with accelerometers, the system uses existing physiological sensors to capture motion-induced artifacts, then processes these artifacts to derive actigraphy data, thereby avoiding the need for additional motion sensors
Solution Approach 2:
The patent creates a computational copy of actigraphy signals by processing physiological signal artifacts. The body motion artifact signal is extracted and transformed through DSP algorithms to generate an actigraphy-like output, effectively copying the function of traditional actigraphy devices using different physical measurements
2Device complexity
If physiological signals are monitored without considering body motion artifacts, then physiological parameter measurement is simplified, but measurement precision deteriorates due to artifacts
Solution Approach 1:
The patent extracts the body motion artifact component from the physiological signal using DSP techniques. By separating the artifact portion from the genuine physiological signal, the system can either remove the artifact to improve measurement accuracy or utilize the artifact separately to derive motion information, thus resolving the conflict between signal simplicity and measurement precision
Solution Approach 2:
The patent transforms the physiological signal parameters through various DSP operations (Fourier transform, wavelet transform, autocorrelation) to reveal hidden motion information. By changing the representation parameters of the signal, the system converts motion artifacts from noise to useful measurement data, improving both precision and utility
3Measurement precision
If body motion artifacts are removed from physiological signals, then measurement precision improves, but loss of information occurs regarding body movement
Solution Approach 1:
The patent segments the physiological signal into distinct components: genuine physiological activity and body motion artifacts. Through DSP analysis, the system separates these components in the frequency or time-frequency domain, allowing independent processing of each segment. This enables precise physiological measurement while preserving motion information in a separate channel
Data Source
AI summary
An actigraphy method includes receiving a physiological parameter signal as a function of time for a physiological parameter other than body motion (such as electrocardiography or a respiration monitor), computing a body motion artifact (BMA) signal as a function of time from the physiological parameter signal (for example, using a local signal power signal, a local variance signal, a short-time Fourier transform, or a wavelet transform over epochs of duration on order a few minutes or less), and computing an actigraphy signal as a function of time from the BMA signal, for example by applying a linear transform to the BMA signal and optionally applying filtering such as median removal and/or high-pass filtering.