Implantable Device Far Field Signal Arrhythmia Detection
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Solution Overview
Problem
Implantable medical devices (IMDs) face challenges in accurately detecting ventricular arrhythmias due to inappropriate programming of rate thresholds and detection times, leading to either unnecessary therapy delivery or omission, and misclassification of cardiac signals as 'sinus' or 'trashed' events, resulting in irregular marker patterns that fail to satisfy arrhythmia detection criteria.
Innovation Solution
The method involves sensing both near field (NF) and far field (FF) signals between electrodes in an IMD to apply an arrhythmia detection algorithm, identifying event markers, and analyzing FF signals for under-detected arrhythmia indicators to adjust therapy and sensing parameters, thereby improving arrhythmia detection and reducing inappropriate therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If faster rate thresholds and longer detection times are programmed to reduce inappropriate therapies, then false positive detection is reduced, but arrhythmia detection accuracy deteriorates and therapy may be omitted or delayed
Solution Approach 1:
The patent introduces a new dimension of analysis by implementing a second arrhythmia detection algorithm that operates in parallel with the primary algorithm. This secondary algorithm uses different detection criteria and parameters, allowing the system to cross-validate detections and resolve ambiguities that would cause the primary algorithm to miss arrhythmias or generate false positives. The multi-algorithm approach adds a dimensional layer to the detection process without requiring changes to the fundamental detection timing and threshold parameters.
Solution Approach 2:
The system implements feedback mechanisms where the output of one detection algorithm influences the operation of the other. When the primary algorithm detects a potential arrhythmia, the secondary algorithm analyzes the same data with different criteria to confirm or refute the detection. This cross-validation feedback loop allows the system to maintain strict detection parameters while still achieving high accuracy through mutual verification between algorithms.
2Reliability
If longer detection times are required to satisfy arrhythmia detection criteria, then false positive detections are reduced, but detection time is extended and therapy delivery is delayed
Solution Approach 1:
The patent implements preliminary action by having the secondary detection algorithm continuously analyze cardiac signals in advance, maintaining a ready state with alternative detection criteria. When the primary algorithm's detection criteria are not yet satisfied, the secondary algorithm may have already identified arrhythmia patterns using different parameters. This preliminary analysis capability allows the system to reduce detection time without sacrificing reliability, as the secondary algorithm is prepared to confirm detections that the primary algorithm might otherwise miss or delay.
Solution Approach 2:
The system applies parameter changes by utilizing different detection parameters and criteria in the secondary algorithm compared to the primary algorithm. The secondary algorithm may use different rate thresholds, detection window sizes, or event counting requirements. This parameter diversity allows the system to satisfy detection criteria more quickly in certain scenarios while maintaining overall detection reliability through the complementary nature of the different parameter sets.
3Measurement precision
If arrhythmia detection algorithms use strict event classification criteria, then detection precision is improved, but irregular marker patterns cause algorithms to fail to declare arrhythmia episodes
Solution Approach 1:
The patent introduces an intermediary mechanism where the secondary detection algorithm acts as a mediator between the strict classification criteria of the primary algorithm and the need for reliable arrhythmia declaration. When the primary algorithm produces irregular marker patterns that prevent declaration, the secondary algorithm analyzes the same events with more flexible or alternative classification criteria. This intermediary analysis resolves the contradiction by providing a pathway to reliable declaration even when primary algorithm criteria are not fully satisfied, without compromising the precision of event classification itself.
Solution Approach 2:
The system applies dynamics by allowing the detection system to adaptively switch between or combine results from multiple algorithms based on the characteristics of the detected patterns. When strict classification produces irregular patterns, the system dynamically engages the secondary algorithm with alternative criteria. This dynamic approach maintains measurement precision while improving reliability, as the system flexibly responds to different pattern types with the most appropriate detection strategy.
Data Source
AI summary
Methods, devices and program products are provided for under control of one or more processors within an implantable medical device (IMD). Sensing near field (NF) and far field (FF) signals are between first and second combinations of electrodes coupled to the IMD. The method applies an arrhythmia detection algorithm to the NF signals for identifying events within the NF signal and designates events marker based thereon and monitors the event markers to detect a candidate arrhythmia condition in the NF signals. The candidate under-detected condition comprises at least one of an under-detected arrhythmia or over-sensing. In response to detection of the candidate arrhythmia condition, the method analyzes the FF signals for a presence of an under-detected arrhythmia indicator. The method delivers an arrhythmia therapy based on the presence of the under-detected arrhythmia indicator in the FF signals and the candidate under-detected arrhythmia condition in the NF signals.