EMG Time-Frequency Analysis for Patient-Ventilator Synchronization
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Solution Overview
Problem
Existing ventilator systems often experience patient-ventilator asynchrony during spontaneous breaths, leading to patient discomfort and delayed weaning, due to a delay in detecting the onset of inhalation effort by the patient.
Innovation Solution
A ventilation system utilizing electromyography (EMG) signals for patient-ventilator synchronization, employing online time-frequency analysis to split the EMG signal into components, identify useful frequency bands, and detect the onset of spontaneous breath through a combination of components exceeding calibrated thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional pressure/flow sensors are used to detect breath onset, then the detection is simple, but there is a delay of several hundred milliseconds between patient muscle activation and pressure change detection
Solution Approach 1:
The patent replaces mechanical pressure/flow sensors with an electromyographic (EMG)-based detection system. EMG sensors detect muscle activation electrical signals directly, eliminating the delay caused by mechanical pressure transmission. This substitution of detection methodology reduces breath onset detection delay while maintaining reasonable system complexity through standardized EMG sensor integration.
Solution Approach 2:
The patent introduces EMG signals as an intermediary between patient breath effort and ventilator trigger. Instead of directly measuring pressure changes at the airway (which cause delay), the system uses EMG signals from respiratory muscles as an intermediate indicator that predicts breath onset before pressure changes occur, thereby reducing detection delay.
2Measurement precision
If EMG signal processing is simplified, then the device complexity is reduced, but the synchronization precision between patient and ventilator deteriorates
Solution Approach 1:
The patent segments the EMG signal into multiple frequency components using spectral analysis. By dividing the complex EMG signal into distinct frequency bands (e.g., using Fast Fourier Transform), the system can identify specific patterns associated with breath onset more precisely. This segmentation improves detection precision while managing processing complexity through structured analysis of frequency components.
Solution Approach 2:
The patent transforms the EMG signal from the time domain to the frequency domain by changing the analysis parameters. Using spectral decomposition, the system converts time-based signal characteristics into frequency-based features, enabling more precise breath onset detection. This parameter transformation allows sophisticated detection algorithms to work with simplified frequency components rather than raw complex signals.
Data Source
AI summary
A computer-implemented method for detecting onset of a spontaneous breath by a patient coupled to a ventilation system includes receiving, at a processor, an electromyography (EMG) signal from an EMG sensor disposed on the patient. The method also includes pre-conditioning, via the processor, the EMG signal to separate the EMG signal into a plurality of components having EMG information utilizing a set of bandpass filters. The method further includes individually analyzing, via the processor, each component of the plurality of components to detect an onset of the spontaneous breath by the patient. The method still further includes determining, via the processor, the onset of the spontaneous breath by the patient is occurring when at least two components of the plurality of components indicate the onset of the spontaneous breath by the patient.