Implantable Heart Sound Signal Filtering for Artifact-Resistant Monitoring
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Solution Overview
Problem
Conventional implantable medical devices (IMDs) face challenges in accurately detecting heart sound characteristics due to inferior quality of heart sound signals, which can lead to incorrect determinations of heart function and diagnoses, particularly when affected by factors such as device location, orientation, motion artifacts, and electromagnetic interference.
Innovation Solution
An IMD equipped with a monitoring device and an accelerometer processes cardiac activity and heart sound signals using initial and quality criteria to filter and generate a heart sound ensemble, adjusting dynamic time windows based on heart sound characteristics, and controlling the device based on the ensemble.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If heart sound signals are obtained using conventional implantable medical devices, then the device can monitor cardiac activity, but the signal quality is inferior and prone to artifacts
Solution Approach 1:
The patent segments the heart sound signal processing into multiple stages: initial candidate identification, quality assessment based on multiple criteria, and ensemble generation. This multi-stage filtering process separates useful heart sound signals from noise and artifacts systematically, improving signal quality while maintaining reliability
Solution Approach 2:
The patent introduces an intermediary processing layer that includes quality criteria assessment and ensemble generation. This intermediary process acts as a mediator between the raw heart sound signals and the final diagnostic interpretation, filtering out motion artifacts and electromagnetic interference through multiple quality checks
2Measurement precision
If multiple processing criteria are applied to filter heart sound signals, then signal accuracy improves, but device complexity increases
Solution Approach 1:
The processing complexity is segmented into distinct modules: initial criteria for candidate identification, quality criteria for filtering, and ensemble generation. Each module performs a specific function with well-defined inputs and outputs, making the overall complex system manageable and maintainable while achieving high measurement precision
Solution Approach 2:
The patent applies preliminary filtering criteria to eliminate obviously poor quality signals before more complex analysis. This preliminary action reduces the computational burden of subsequent processing steps by removing candidates that would not meet quality standards, thereby managing device complexity while maintaining precision
3Device complexity
If heart sound signals with low fidelity are used, then the device can operate with simpler hardware, but incorrect determinations of heart function occur
Solution Approach 1:
The patent implements feedback through the ensemble generation process, where multiple individual heart sound signals are combined and averaged. This feedback mechanism compensates for low fidelity in individual signals by reinforcing consistent features across multiple beats, thereby improving measurement precision without requiring more complex hardware
Solution Approach 2:
The patent applies excessive processing action by implementing multiple quality criteria and ensemble generation beyond what a single signal would require. This partial or excessive processing of multiple signals compensates for hardware limitations, achieving accurate heart function determination despite using simpler hardware that captures lower fidelity individual signals
Data Source
AI summary
A method for controlling an implantable medical device (IMD) is provided. The method can include obtaining, with a monitoring device, cardiac activity signals, and obtaining, with an accelerometer, candidate heart sound (HS) signals. The method can also include analyzing the cardiac activity signals based on an initial criteria and eliminating candidate HS signals based on the analyzing to provide remainder candidate HS signals. The method also can include analyzing the remainder candidate HS signals based on HS quality criteria related to the remainder candidate HS signals, eliminating additional candidate HS signals from the remainder candidate HS signals based on analyzing the remainder candidate HS signals to provide a HS ensemble, and controlling the IMD based on the HS ensemble. An IMD is also provided.