Heart Failure Diagnosis Using In-Suspicion Model-Cycles
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Solution Overview
Problem
Existing cardiac decompensation prevention methods generate a high number of false alarms, which are not only unnecessary but also potentially worrisome for patients, and require significant computational and memory resources for analysis, making them inefficient and power-intensive for implantable devices.
Innovation Solution
The method involves generating and comparing 'in-suspicion' and 'off-suspicion' model-cycles based on cardiac activity signals, such as electrogram and endocardial acceleration, to differentiate between cardiac decompensation situations without triggering alarms prematurely, using morphological indicators and efficient data processing to reduce false alerts and conserve resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing cardiac decompensation prevention methods are used, then sensitivity of detection is maintained, but false alarms increase significantly
Solution Approach 1:
The patent segments the detection process into two distinct phases: an 'in-suspicion' phase where model-cycles are generated and stored, and an 'off-suspicion' phase where actual comparison and alert generation occur. This segmentation allows the system to accumulate diagnostic data without triggering false alarms during the accumulation phase, only generating alerts when the stored model-cycles are compared against new data in the off-suspicion phase.
Solution Approach 2:
The patent performs preliminary actions by generating and storing model-cycles during the in-suspicion phase before actual diagnostic decisions are made. These pre-generated model-cycles serve as reference patterns that are later compared against new cardiac activity data, allowing the system to distinguish between normal variations and actual decompensation events, thereby reducing false alarms.
2Measurement precision
If comprehensive signal analysis is performed, then detection precision is improved, but computational resources and power consumption increase
Solution Approach 1:
The patent implements periodic action by alternating between two operational modes: during the in-suspicion phase, the device accumulates and stores model-cycles without intensive processing, and during the off-suspicion phase, it performs the computationally intensive comparison operations. This periodic switching between low-power data collection and higher-power analysis reduces overall power consumption while maintaining comprehensive signal analysis capability.
Solution Approach 2:
The patent creates simplified copies of cardiac activity data in the form of model-cycles during the in-suspicion phase. These model-cycles are condensed representations that capture essential diagnostic information. By working with these compact copies rather than raw comprehensive signals during the comparison phase, the system reduces computational complexity and power requirements while preserving detection precision.
3Measurement precision
If comprehensive signal analysis is performed, then detection precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analytical task into two simpler sub-tasks: generating and storing model-cycles during the in-suspicion phase, and comparing new data against stored model-cycles during the off-suspicion phase. This segmentation reduces the computational complexity of each individual phase while maintaining the overall precision benefits of comprehensive signal analysis.
Solution Approach 2:
The patent performs preliminary data processing and model-generation during the in-suspicion phase, preparing the data in advance for the comparison phase. By pre-processing and organizing the cardiac activity data into standardized model-cycles, the system reduces the computational burden during the actual diagnostic comparison, thereby reducing device complexity while maintaining detection precision.
Data Source
AI summary
Methods, devices, and processor-readable storage media are provided for the diagnosis of heart failure. A method in this context includes collecting, using an implantable device, reference episodes, the reference episodes comprising, at least one of: electrical activity signals of a myocardium; myocardium hemodynamic activity signals, or indicators reflecting variation of physical parameters, variation of activity, and variation of hemodynamic phases between phases of effort and phases of recovery; generating an in-suspicion model-cycle and an off-suspicion model-cycle based on the reference episodes; and determining whether to generate an early heart failure alert, based on a difference between the in-suspicion model-cycle and the off-suspicion model-cycle.