Respiratory Event Detection Using EIT and Ventilator Waveform Alignment
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Solution Overview
Problem
Existing mechanical ventilation systems face challenges in detecting asynchronous respiratory events, which can lead to patient injuries such as overinflating or underinflating the patients lungs, which can lead to injuries to the patient, which can result in the patient, requiring expert, real-time attention from a trained professional at the bedside. Identifying PVAs when a trained professional is not present may reduce the detrimental effects of PVA's on the patient. Identifying PVAs when a trained professional is not present may reduce the occurrence of PVA's by reducing the time before the PVA's are detected and adjustments are made to the mechanical ventilation system, which may reduce the detrimental effects of PVA's on the patient.
Innovation Solution
A system using electrical impedance tomography (EIT) to measure regional lung impedance and volume changes, correlating with lung volume distribution, to detect asynchronous respiratory events by identifying trigger points and asynchronies through waveform analysis and filtering, and providing real-time feedback for adjustments to mechanical ventilator settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sleep studies with sensors and wires are used, then diagnostic accuracy is improved, but patient comfort and accessibility deteriorate
Solution Approach 1:
The patent extracts the essential respiratory monitoring function from the complex traditional polysomnography system. By using the existing smartphone camera to capture video of the patient's chest or abdomen movements, the system eliminates the need for specialized medical sensors, wires, and clinical equipment, thereby maintaining diagnostic capability while dramatically improving patient comfort and accessibility.
Solution Approach 2:
The patent introduces a smartphone as an intermediary device between the patient and the diagnostic system. The smartphone's camera and processing capabilities serve as a mediator that translates simple video footage into respiratory event detection, replacing the need for direct connection between specialized medical sensors and the diagnostic system.
2Reliability
If continuous monitoring is implemented to detect all respiratory events, then detection completeness is improved, but false alarms from motion artifacts worsen
Solution Approach 1:
The system continuously analyzes video footage and provides real-time feedback on respiratory patterns. By comparing expected respiratory movements with actual observed movements, the system can identify and filter out motion artifacts that don't conform to normal respiratory patterns, thereby reducing false alarms while maintaining continuous monitoring capability.
Solution Approach 2:
The patent employs dynamic analysis of video footage to distinguish between genuine respiratory events and motion artifacts. By analyzing the temporal and spatial characteristics of movements dynamically, the system can adapt to different patient conditions and movement patterns, improving detection accuracy without increasing false alarm rates.
3Adaptability or versatility
If asynchronous respiratory events are detected during wakeful periods, then comprehensive respiratory health monitoring is improved, but traditional methods fail to capture these events
Solution Approach 1:
The patent creates a universal monitoring system that functions across multiple states - detecting respiratory events both during sleep and during wakeful periods. The same smartphone-based video analysis technology serves multiple purposes: monitoring sleep apnea, detecting daytime respiratory issues, and providing comprehensive respiratory health assessment, thereby achieving versatile monitoring coverage without sacrificing detection reliability.
Data Source
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AI summary
A method includes retrieving a lung volume waveform from an electrical impedance tomography device. The method further includes retrieving at least one of a flow waveform and a pressure waveform. The method also includes aligning the lung volume waveform and the at least one of the flow waveform and the pressure waveform with respect to time. The method further includes comparing the lung volume waveform and the at least one of the flow waveform and the pressure waveform. The method also includes determining if an asynchronous respiratory event occurred based on comparing the lung volume waveform and the at least one of the flow waveform and the pressure waveform. The method further includes classifying the asynchronous respiratory event. The method also includes providing an alert identifying the asynchronous respiratory event. A system includes a receiver, a processor, and a memory device configured to perform the method.