Biological Signal Synchronization Across Monitors Using Sequence Numbers
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Solution Overview
Problem
Existing systems fail to achieve precise synchronization of biological signals from multiple medical devices due to network latencies and clock inconsistencies, hindering effective data analysis and predictive capabilities.
Innovation Solution
A method for synchronizing biological signals by assigning sequence numbers to sampling cycles, unwrapping these numbers to correct for network delays, and calculating adjusted timestamps to align waveforms across devices, using techniques like linear regression and cross-correlation to ensure millisecond-level accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transmitted over networks from multiple medical devices, then data collection capability is improved, but time synchronization precision deteriorates due to network latencies
Solution Approach 1:
The patent applies preliminary action by assigning sequence numbers to packets before transmission occurs. Each device tags its data packets with monotonically increasing sequence numbers and estimated transmission times before the actual network transmission. This pre-tagging allows the central system to later reconstruct accurate timestamps by combining the pre-assigned sequence numbers with actual receipt times, thereby compensating for network latency variations and achieving millisecond-level synchronization precision across multiple devices.
2Device complexity
If multiple devices operate independently without synchronized clocks, then device complexity is reduced, but signal synchronization precision deteriorates
Solution Approach 1:
The patent introduces an intermediary mechanism where a central system acts as a mediator to synchronize signals from independently operating devices. Each device independently assigns sequence numbers and estimated transmission times to its packets without requiring synchronized clocks. The central system receives these packets, uses the sequence numbers to establish temporal order, and calculates adjusted timestamps by comparing sequence number progression with actual receipt times. This intermediary approach enables precise signal synchronization while allowing devices to operate with simple, independent clock systems.
3Measurement precision
If sequence numbers are used to track sampling cycles, then data identification accuracy is improved, but data processing complexity increases due to unwrapping operations
Solution Approach 1:
The patent applies self-service by designing a sequence number system that automatically tracks and identifies data packets through their natural monotonically increasing property. Each device increments its sequence number with every sampling cycle, creating an inherent temporal marker. The unwrapping operation at the central system simply detects discontinuities in this natural sequence and applies corrective offsets, rather than requiring complex external tracking mechanisms. This self-organizing approach maintains high data identification accuracy while minimizing processing complexity through the use of simple counter-based identification.
Data Source
AI summary
A method for time-synchronizing waveforms from different patient monitors that does not require devices to have high-precision synchronized clocks or to be coupled to a triggering synchronization signal generator. Comparable signals may be obtained from different devices either by placing selected sensors from the devices in the same locations, or by filtering signals from one device to obtain a signal comparable to signals from another device. Filtering may for example transform waveforms into independent components and identify a component that matches a signal from another device. The comparable signals may then be transformed into frequency variation curves, such as time intervals between peak values, to facilitate detection of the time shift between the signals. Cross correlation of the frequency variation curves may be used to locate the precise time shift between the signals. Use of frequency variation curves may be more robust than directly comparing and correlating the original signals.