Multi-Sensor Cardiopulmonary Device for Early Warning
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Solution Overview
Problem
Current cardiopulmonary management systems face challenges in providing early warnings for worsening heart failure and other cardiopulmonary conditions due to the complexity of physical changes in individuals, which can be difficult for single sensors to detect timely and accurately.
Innovation Solution
A multi-sensor device with electrodes and a sound sensor, configured to measure cardiopulmonary parameters such as thoracic impedance, electrocardiogram, and heart sounds, which calculates a score based on changes in these parameters to trigger alarms when thresholds are exceeded, and uses machine learning to predict future conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single sensor is used to detect cardiopulmonary conditions, then the device complexity is reduced, but the measurement precision and reliability of detecting worsening conditions deteriorates
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor components, each responsible for detecting specific cardiopulmonary parameters (thoracic impedance, electrocardiogram, heart sounds, respiratory rate). This segmentation allows each sensor to specialize in detecting particular physiological changes, improving overall detection accuracy while maintaining manageable device complexity through modular architecture.
Solution Approach 2:
Multiple sensors detecting different cardiopulmonary parameters are merged into a single integrated monitoring system. The sensors work together to provide comprehensive monitoring of heart failure conditions, where the combined data from thoracic impedance, ECG, heart sounds, and respiratory rate sensors creates a more reliable and precise detection capability than any single sensor could achieve alone.
2Reliability
If multiple sensors are used to monitor cardiopulmonary parameters, then the measurement precision and reliability improve, but the device complexity increases
Solution Approach 1:
The multi-sensor device is designed with universal functionality to monitor multiple cardiopulmonary parameters simultaneously using a single integrated platform. The system can detect thoracic impedance changes, electrocardiogram signals, heart sounds, and respiratory rate, making it a multi-functional monitoring device that improves reliability without requiring multiple separate devices, thus managing complexity through consolidation rather than proliferation.
Solution Approach 2:
The system incorporates automated algorithms that self-manage the complex task of integrating and analyzing data from multiple sensors. The processor automatically correlates signals from different sensors, identifies patterns indicating worsening heart failure, and generates alerts without requiring manual intervention. This self-service capability handles the complexity of multi-sensor integration internally, presenting a simplified interface to users while maintaining high reliability through comprehensive monitoring.
3Reliability
If continuous monitoring of multiple parameters is performed, then the early warning capability improves, but the use of energy increases
Solution Approach 1:
The monitoring system employs periodic sampling of cardiopulmonary parameters rather than truly continuous monitoring. Sensors take measurements at regular intervals, and the processor analyzes these periodic data points to detect trends indicating worsening conditions. This periodic action maintains early warning capability by capturing sufficient physiological information while significantly reducing energy consumption compared to uninterrupted continuous monitoring.
Solution Approach 2:
The system uses feedback mechanisms where the processor continuously analyzes incoming sensor data and adjusts monitoring intensity based on detected patterns. When parameters remain within normal ranges, the system operates in a lower-energy mode with reduced sampling frequency. When anomalies or trends suggesting deterioration are detected, the system increases monitoring intensity and alert frequency. This adaptive feedback approach maintains high reliability for early warning while optimizing energy consumption by avoiding unnecessary high-power operation during stable periods.
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 provides non-invasive, early warnings for worsening cardiopulmonary conditions with high sensitivity, allowing for timely adjustments in treatment and reducing hospital readmissions, while offering continuous monitoring of cardiac and pulmonary health.
Implementation Method 1
a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body... measure cardiopulmonary parameters from the subject... thoracic impedance change
Implementation Method 2
a sound sensor... measure cardiopulmonary parameters from the subject... sound change... heart sounds
Data Source
AI summary
A cardiopulmonary management system includes a multi-sensor device and a controller. The multi-sensor device includes an elongated body having a first end and a second end, a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body, an electrical circuitry disposed near a middle of the body, and a sound sensor. The multi-sensor device is configured to be disposed on a skin of a subject and to measure cardiopulmonary parameters from the subject. The controller is configured to derive a score of sound change, heart rate change, thoracic impedance change, respiratory rate change, QRS wavelength change, QT interval change, electromechanical activation time change, and left ventricular systolic time change based on the measured cardiopulmonary parameters. The controller is further configured to control an operation of the system when the derived score is greater than a threshold.