Acoustic Respiratory Sensor Probe-Off Detection
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Solution Overview
Problem
Existing patient monitoring systems often fail to detect a faulty or unstable connection between sensors and patients, leading to misinterpretation of readings, false alarms, and potential misdiagnoses due to a lack of awareness about probe-off conditions.
Innovation Solution
The implementation of sensors and sensor systems with probe-off detection features that compare signals from acoustic sensors with those from secondary sensors, such as optical sensors, to determine the integrity of the connection, using methods like signal correlation and low-frequency waveform analysis to output indications of connection quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If acoustic sensors are used for patient monitoring, then respiratory and cardiac sounds can be detected, but false alarms and misinterpretations occur when the probe is improperly connected
Solution Approach 1:
The system continuously monitors the acoustic signal quality and provides feedback about probe connection status. By analyzing signal characteristics in real-time, the system detects when the probe is improperly connected and alerts clinicians, preventing false alarms and misinterpretations of patient data.
Solution Approach 2:
A secondary optical sensor is introduced as an intermediary to verify probe connection status. The optical sensor detects physiological signals independently, and its correlation with acoustic signals confirms proper probe attachment, serving as a mediator to validate the primary monitoring channel.
2Reliability
If signal comparison methods are implemented to detect probe-off conditions, then connection integrity can be verified, but system complexity increases
Solution Approach 1:
The system uses a single integrated platform that performs both acoustic respiratory monitoring and optical pulse detection. By making the monitoring system multi-functional, the same hardware infrastructure supports multiple detection methods, reducing overall system complexity while improving reliability through cross-validation.
Solution Approach 2:
The system automatically compares signals from different sensors and self-diagnoses probe connection status without requiring additional manual checks or complex external verification systems. The built-in correlation analysis performs self-validation, simplifying the overall system architecture.
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
This solution effectively reduces false alarms, ensures accurate monitoring by alerting medical personnel to probe-off conditions, and prevents prolonged unmonitored physiological sounds, thereby improving patient care and diagnostic accuracy.
Implementation Method 1
The 'piezoelectric effect' is the appearance of an electric potential and current across certain faces of a crystal when it is subjected to mechanical stresses. Due to their capacity to convert mechanical deformation into an electric voltage, piezoelectric crystals have been broadly used in devices such as transducers, strain gauges and microphones.
Data Source
AI summary
Embodiments described herein include sensors and sensor systems having probe-off detection features. For example, sensors and physiological monitors described herein include hardware and/or software capable of providing an indication of the integrity of the connection between the sensor and the patient. In various embodiments, the physiological monitor is configured to output an indication of a probe-off condition for an acoustic sensor (or other type of sensor). For example, in an embodiment, a signal from an acoustic sensor is compared with a signal from a second sensor to determine a probe-off condition.