Implantable Device Off-line IEGM Reprogramming
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Solution Overview
Problem
Current implantable medical devices face issues with false-positive and false-negative detections of abnormal cardiac events, leading to inappropriate therapy delivery and unnecessary data storage, due to improper sensitivity settings and detection parameter configurations.
Innovation Solution
An off-line analysis method is implemented to identify and adjust sensitivity and detection parameters in implantable medical devices, allowing for the reduction or elimination of false detections by analyzing recorded IEGM data and reprogramming the device to improve event detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensitivity settings and detection parameters are increased to improve detection of abnormal cardiac events, then detection sensitivity is improved, but false-positive detections increase
Solution Approach 1:
The device performs preliminary analysis of IEGM data to identify patterns and characteristics of false-positive detections before final event classification. By pre-processing and pre-analyzing the cardiac signals, the device can adjust detection parameters dynamically to reduce false positives while maintaining sensitivity for true abnormal events.
Solution Approach 2:
The system implements feedback mechanisms where detected events are analyzed and used to adjust subsequent detection parameters. When false positives are identified through pattern recognition and clinical correlation, the device automatically adjusts sensitivity settings and detection thresholds to prevent recurrence, while maintaining high sensitivity for genuine abnormal events.
2Reliability
If detection parameters are adjusted to reduce false-positive detections, then false-positive rate decreases, but false-negative detections increase
Solution Approach 1:
The device employs dynamic adjustment of detection parameters based on real-time analysis of cardiac signal characteristics. Instead of fixed thresholds, the system continuously adapts sensitivity settings based on signal morphology, rate variability, and contextual patterns, allowing optimal balance between false-positive and false-negative reduction across different cardiac conditions.
Solution Approach 2:
The system changes multiple detection parameters simultaneously rather than adjusting single thresholds. By modifying combinations of parameters including sensitivity levels, detection windows, morphology criteria, and rate zones in coordinated fashion, the device achieves better overall detection accuracy without sacrificing sensitivity for true abnormal events.
3Loss of information
If all detected events are recorded in device memory for review, then data completeness is improved, but memory usage increases
Solution Approach 1:
The device extracts and records only the most clinically relevant features and parameters from complete IEGM data, rather than storing all raw signals. By identifying and preserving key diagnostic information such as event morphology, timing intervals, and critical patterns while discarding redundant data, the system maintains data completeness for clinical review while significantly reducing memory requirements.
Solution Approach 2:
The system implements selective discarding of data that has been fully analyzed and determined to be non-critical, while maintaining archives of important events. Less significant detected events are discarded after initial analysis, while clinically relevant events are preserved in memory for comprehensive review, optimizing the balance between data completeness and memory utilization.
Data Source
AI summary
Techniques are provided for use by implantable medical devices such as pacemakers or by external systems in communication with such devices. An intracardiac electrogram (IEGM) is sensed within a patient in which the device is implanted using a cardiac signal sensing system. Cardiac events of interest such as arrhythmias, premature atrial contractions (PACs), premature ventricular contractions (PVCs) and pacemaker mediated tachycardias (PMTs) are detected within the patient using event detection systems and then portions of the IEGM representative of the events of interest are recorded in device memory. Subsequently, during an off-line or background analysis, the recorded IEGM data is retrieved and analyzed to identify false detections. In response to false detections, the cardiac signal sensing systems and/or the event detection systems of the implantable device are selectively adjusted or reprogrammed to reduce or eliminate any further false detections, including false-positives or false-negatives. Various adaptive reprogramming techniques are described.