Physiological Signal Synchronization Using Common-Mode Components
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Solution Overview
Problem
Synchronizing physiological signals from multiple sensors is challenging due to differences in sensitivity, response time, and communication channel latencies, and using timestamps or generated signals can complicate electronics and lead to inaccurate synchronization.
Innovation Solution
Utilize a common-mode signal embedded within each collected signal, such as a heartbeat or brain wave, to synchronize physiological signals by filtering and cross-correlating these components, reducing the need for additional devices and resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If timestamps or generated signals are used to synchronize physiological signals, then synchronization accuracy is improved, but device complexity and resource requirements increase
Solution Approach 1:
The system uses the physiological signals themselves to generate synchronization markers by detecting common-mode components (such as heartbeat or respiration patterns) that appear in multiple sensor signals. This self-service approach eliminates the need for external synchronization devices or complex timestamping infrastructure, resolving the contradiction by achieving accurate synchronization through the signals' inherent characteristics rather than adding external complexity
Solution Approach 2:
The common-mode signal acts as an intermediary that links multiple physiological signals together. By identifying and using this shared signal component present across different sensors, the system establishes a natural reference point for synchronization without requiring additional hardware or complex coordination mechanisms, thus improving synchronization accuracy while avoiding increased device complexity
2Measurement precision
If generated synchronization signals are used, then synchronization is achieved, but interference with physiological signals or medical device functioning may occur
Solution Approach 1:
Instead of using external generated signals that risk interfering with physiological signals, the system converts the naturally occurring common-mode components in the physiological signals themselves into synchronization markers. This approach transforms what could be considered noise or redundancy into a useful synchronization resource, achieving accurate synchronization without introducing harmful interference to the physiological signals or medical device operation
3Measurement precision
If additional devices or resources are used for synchronization, then synchronization accuracy is improved, but computational cost and resource requirements increase
Solution Approach 1:
The system achieves synchronization by processing the existing physiological signals themselves to extract common-mode components, rather than relying on additional synchronization devices or resources. This self-service method uses the signals already being collected by the medical device, eliminating the need for extra hardware and reducing computational overhead while maintaining synchronization accuracy
Solution Approach 2:
The common-mode signal extraction process serves multiple functions simultaneously: it identifies synchronization markers for time alignment and also characterizes the physiological signal itself. This multi-functionality allows the system to achieve synchronization using the same signal processing pipeline already in place for physiological monitoring, avoiding additional computational costs or resource requirements
Data Source
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AI summary
Methods, systems, and computer-readable media for synchronizing physiological signals collected from two or more physiological sensors (170) are provided. Examples of physiological signals may include respiration, heartbeat, electroencephalogram, and gastrointestinal signals. A method, an apparatus, and a computer program for synchronizing two or more physiological signals may involve collecting first data from a first sensor and second data from a second sensor, each of the first data and the second data represents a plurality of physiological signal components; extracting a common physiological signal component of the first data and the second data; correlating the common physiological signal component of the plurality of physiological signal components of the first data and of the plurality of physiological signal components of the second data; and based on the correlating, synchronizing a first physiological signal component included in the first data and a second physiological signal component included in the second data.