Signal Processing Unit for Cardiogenic and Respiratory Signal Isolation
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Solution Overview
Problem
Existing methods struggle to isolate cardiogenic and respiratory signals from a sum signal that is a superimposition of cardiac activity and breathing or ventilation, as direct measurement is often not possible without stressing the patient, and analytical models are difficult to set up and validate.
Innovation Solution
A process and signal processing unit that calculates estimated cardiogenic and respiratory signals using a training phase to generate a signal estimating unit, which compensates for the influence of cardiac activity and breathing/ventilation in the sum signal, allowing for the determination of these signals without direct measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of respiratory or cardiogenic signals is performed, then measurement precision is improved, but device complexity and patient stress increase
Solution Approach 1:
The patent extracts the desired respiratory or cardiogenic signal from the sum signal by identifying and removing the interfering component. The signal processing unit separates the respiratory signal from cardiac interference or extracts the cardiogenic signal by removing respiratory components, achieving precise signal isolation without complex direct measurement systems.
Solution Approach 2:
The patent uses an intermediary approach by measuring the sum signal (containing both respiratory and cardiogenic components) and using signal processing as a mediator to separate the individual signals. This avoids the need for separate direct measurement systems while still achieving accurate signal extraction.
2Device complexity
If analytical models are used to separate signals, then device complexity is reduced, but reliability and accuracy deteriorate due to model setup and validation difficulties
Solution Approach 1:
The signal processing unit performs self-service by automatically adapting to individual patient variations without requiring manual model setup or validation. The system learns patient-specific signal characteristics and adjusts its separation algorithm accordingly, ensuring reliable and accurate signal estimation without complex analytical model configuration.
Solution Approach 2:
The patent implements a training phase before actual signal separation where the system pre-processes data to establish patient-specific signal characteristics. This preliminary action enables the system to reliably separate signals in subsequent measurements without requiring complex real-time model validation.
3Ease of operation
If standard signal processing methods are used, then ease of operation is improved, but adaptability to individual patient variations deteriorates
Solution Approach 1:
The patent implements dynamic adaptation where the signal processing unit adjusts its parameters and algorithms based on individual patient variations. The system evolves from static standard processing to dynamic patient-specific processing, maintaining ease of operation while improving adaptability through automatic adjustment.
Solution Approach 2:
The system changes processing parameters based on patient-specific characteristics identified during the training phase. By adapting parameters such as filtering characteristics, separation algorithms, and signal thresholds to individual patients, the system maintains operational simplicity while achieving high adaptability.
Data Source
AI summary
A process and signal processing unit (5) determine a cardiogenic signal (Sigkar,est) or a respiratory signal (Sigres,est) from a sum signal (SigSum), resulting from a superimposition of cardiac activity and breathing of a patient (P). A signal estimating unit (6), which yields a shape parameter as a value of a transmission channel parameter (LF), is generated during a training phase. A sample with a sample element per heartbeat is used. During a use phase, the transmission channel parameter is measured for each heartbeat, a shape parameter value is calculated by the application of the signal estimating unit and is used to calculate an estimated cardiogenic signal segment (SigHz,kar.LF) or an estimated respiratory signal segment. The cardiogenic signal segments are combined into the cardiogenic signal or the respiratory signal segments are combined into the respiratory signal or the cardiogenic signal segments are subtracted from the sum signal.