Implantable Neurological Event Detection Using Waveform Analysis
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Solution Overview
Problem
Current implantable medical devices for detecting neurological events like epileptic seizures have limitations in achieving 100% accuracy due to high rates of false positives and false negatives, and they often require excessive computational complexity, which is not suitable for chronic implantation.
Innovation Solution
An implantable medical device with a waveform analyzer that includes additional programmable parameters to detect qualified half waves, allowing for more sensitive and specific identification of abnormal neurological activity, such as ictal or epileptiform activity, through the analysis of EEG and ECoG signals, thereby reducing false positives and false negatives in a computationally efficient manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional waveform analysis algorithms are used in implantable medical devices, then the device can detect neurological events, but the detection accuracy is limited by high rates of false positives and false negatives
Solution Approach 1:
The patent applies parameter changes by introducing multiple programmable parameters (amplitude threshold, duration threshold, slope threshold, area threshold) that can be adjusted to optimize detection accuracy for different patients and seizure types. This allows the detection algorithm to be fine-tuned to achieve higher measurement precision while reducing false positives and false negatives through parameter optimization rather than increasing computational complexity.
2Measurement precision
If frequency domain spectrum analysis is used to analyze EEG signals, then detection accuracy may improve, but computational complexity and power consumption increase significantly
Solution Approach 1:
The patent substitutes the mechanical/computational approach of frequency domain spectrum analysis with an electrical signal processing approach using time-domain waveform analysis. By detecting features such as amplitude, duration, slope, and area of electrographic events directly in the time domain, the system achieves comparable or superior detection accuracy without the computational overhead of Fourier transforms or spectral analysis, thereby reducing device complexity and power consumption.
3Reliability
If complex detection algorithms are implemented to reduce false positives and false negatives, then detection reliability improves, but the power consumption and computational load increase
Solution Approach 1:
The patent uses parameter changes to achieve high reliability with low power consumption by implementing a detection algorithm based on simple threshold comparisons of waveform parameters (amplitude, duration, slope, area) rather than complex computational models. The programmable nature of these parameters allows optimization for each patient's specific seizure characteristics, achieving high detection reliability through parameter tuning rather than through computationally intensive processing that would increase power consumption.
Data Source
AI summary
An implantable device includes one or more electrodes to sense an electrical signal from a brain and a waveform analyzer to identify a half wave in the electrical signal; determine an amplitude and a duration of the half wave; determine if the amplitude satisfies a half wave amplitude criterion defined by a set of amplitude parameters comprising a minimum half wave amplitude and a maximum half wave amplitude; determine if the duration satisfies a half wave duration criterion defined by a set of duration parameters comprising a minimum half wave duration and a maximum half wave duration; and identify the half wave as a qualified half wave when the half wave amplitude criterion and the half wave duration criterion is satisfied. A neurological event may be detected based on one or more qualified half waves and electrical stimulation therapy may be delivered to the brain in response to the detection.