Apnea Detection via Heartbeat-Synchronized Chest Impedance Filtering
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Solution Overview
Problem
Existing respiratory monitors for premature infants fail to accurately distinguish heart signals from respiratory signals, leading to missed apnea events and inadequate warnings for medical personnel.
Innovation Solution
A system that re-samples chest impedance signals based on the heartbeat, filters out heart-related fluctuations using Fourier transforms, and calculates the probability of apnea, enabling early detection and automated stimulation of the infant during apnea events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical respiratory monitors use chest impedance to detect breathing, then they can monitor respiratory rate, but they fail to distinguish heart signals from respiratory signals leading to missed apnea events
Solution Approach 1:
The patent segments the chest impedance signal into distinct frequency components using Fourier transform analysis. By separating the signal into frequency bands, the system can identify and filter out heart-related fluctuations (typically higher frequency) from respiratory signals (lower frequency), thereby improving apnea detection accuracy while reducing false alarms caused by cardiac interference
Solution Approach 2:
The patent introduces an intermediary processing layer between raw chest impedance measurement and apnea detection. This intermediary layer applies signal processing techniques including Fourier transform and filtering to isolate the respiratory component from the composite chest impedance signal, enabling reliable apnea detection without false alarms from heart signals
2Reliability
If existing monitors do not filter heart signals, then the system remains simple, but they fail to recognize apnea and provide warning signals
Solution Approach 1:
The patent extracts the harmful heart signal component from the chest impedance measurement by applying Fourier transform analysis and frequency-based filtering. This extraction process removes cardiac interference while preserving the respiratory signal, enabling reliable apnea detection without requiring complex additional hardware
Solution Approach 2:
The patent replaces simple mechanical threshold-based detection with signal processing techniques. By using Fourier transform and digital filtering algorithms, the system achieves more reliable apnea detection through mathematical analysis of the chest impedance signal spectrum, substituting mechanical simplicity with computational intelligence
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system significantly improves apnea detection accuracy, allowing for timely medical intervention and reducing false alarms, with a high agreement rate with expert analysis and potential to save lives.
Implementation Method 1
filters out heart-related fluctuations using Fourier transforms
Data Source
AI summary
Existing monitors for apnea miss many serious events because they do not adequately distinguish the heart signal in chest impedance from the respiratory signal. Described herein is a respiratory monitoring system and method for improved detection and response to apnea, particularly in a NICU setting but also useful in a home setting. This method filters from the chest impedance the part of the impedance that is caused by the beating of the heart in a human subject, and then identifies in real time significant silence in the filtered chest impedance signal, including determining the probability of apnea. If the probability of apnea exceeds a threshold value, the apneic subject can be stimulated using automated interactions such as a vibrating mattress or air blower.