Physiological Signal Segmentation for Ambulatory Heart Failure Detection
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Solution Overview
Problem
Current ambulatory medical devices face challenges in accurately detecting physiologic events indicative of heart failure decompensation due to confounding events, leading to false positive and false negative detections, which can result in unnecessary interventions and delayed necessary therapies.
Innovation Solution
The implementation of a system that includes a signal receiver circuit, a confounding event detector circuit, and a signal processing circuit to adjust physiological signals and detect target events by segmenting and processing data segments based on characteristics of confounding events, thereby reducing false detections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physiological signals are monitored continuously to detect heart failure decompensation events, then detection sensitivity is improved, but false positive and false negative detections increase due to confounding events
Solution Approach 1:
The physiological signal is divided into multiple segments based on detected confounding events. Each segment is processed separately to remove the influence of confounding events, allowing accurate detection of target physiological events while maintaining high detection sensitivity and reliability
Solution Approach 2:
Confounding events are detected and extracted from the physiological signal using dedicated detection circuits. By identifying and separating these interfering events, the system can focus on detecting true target events without false positives or negatives
2Device complexity
If signal processing algorithms are simplified to reduce device complexity, then device complexity is reduced, but the ability to distinguish confounding events from target events deteriorates
Solution Approach 1:
The signal processing is segmented into distinct stages: confounding event detection, signal segmentation, and target event detection. This modular approach maintains high differentiation accuracy while keeping each processing stage relatively simple and manageable
Solution Approach 2:
Confounding events are detected and marked before the target event detection algorithm is applied. This preliminary action allows the subsequent detection algorithm to focus solely on target events without being confounded by interfering signals, maintaining accuracy without increasing overall complexity
Data Source
AI summary
Devices and methods for detecting a physiological target event such as events indicative of HF decompensation status are described. An ambulatory medical device is configured to determine the presence and timing of a confounding event, segment a sensed physiological signal into at least two data segments, adjust the physiological signal by removing or lessening the impact of the confounding event on the physiological signal. The adjusted data can be presented to the user, and the ambulatory medical device can detect the target events using the adjusted physiologic signal. In some embodiments, the ambulatory medical device can be configured to detect an event indicative of HF decompensation using a physiological signal and the information of the detected confounding event.