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Efficient implementation of Stockwell Transform for real-time embedded processing of physiologic signals
Summary
This study presents an efficient Stockwell Transform for real-time time-frequency analysis of physiological signals. The method enables microcontrollers to process complex biological data, advancing wearable health monitoring.
Area of Science:
- Biomedical Engineering
- Signal Processing
- Wearable Technology
Background:
- Physiologic monitoring is crucial for studying normal and pathologic bodily signals.
- Current wearable technologies often lack onboard processing, limiting analysis to data collection.
- Embedded processors in wearables have limited computational power for complex signal analysis.
Purpose of the Study:
- To propose an efficient implementation of the Stockwell Transform for microcontrollers.
- To enable real-time time-frequency analysis of biological signals on low-power devices.
- To overcome the computational limitations of embedded processors in wearable technology.
Main Methods:
- Developed a compact filter bank implementation of the Stockwell Transform with pre-computed filter taps.
- Optimized the transform by removing low-amplitude filter taps due to the Gaussian windowing function's properties.
- Implemented the efficient Stockwell Transform on a Texas Instruments MSP430 microcontroller.
Main Results:
- Demonstrated the feasibility of real-time time-frequency analysis of physiological signals on a microcontroller.
- Evaluated the computational efficiency of the proposed Stockwell Transform implementation.
- Successfully processed simulated electrocardiogram (ECG) data.
Conclusions:
- The efficient Stockwell Transform implementation enables real-time analysis of biological signals on resource-constrained microcontrollers.
- This advancement can significantly enhance the capabilities of wearable physiological monitoring systems.
- The method offers a pathway for more sophisticated, on-device analysis of health data.
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