Wavelet HFO Signal Processing for Physiological–Pathological Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting and treating epilepsy are limited by the inability to accurately predict seizures and deliver targeted therapeutic interventions due to a lack of electrophysiologic control parameters, leading to insufficient or excessive electrical stimulation, and existing neuroprosthetics fail to distinguish between physiological and pathological high-frequency oscillations.
Innovation Solution
A signal processing method using wavelet convolution and topographical analysis to distinguish physiological from pathological high-frequency oscillations, enabling a closed-loop brain stimulation device to enhance memory and reduce seizure probability by applying targeted electrical or optogenetic stimulation based on HFO detection and characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If electrical stimulation is applied to control epilepsy, then seizure control may be improved, but the stimulation may be insufficient or excessive due to lack of precise detection
Solution Approach 1:
The patent segments HFO detection into distinct frequency ranges (80-200 Hz for ripples, 200-600 Hz for fast ripples) and uses separate detection algorithms for each range. This segmentation allows precise identification of different HFO types associated with different pathological conditions, enabling more accurate control of electrical stimulation therapy.
Solution Approach 2:
The patent transforms the detection problem from time-domain analysis to time-frequency domain analysis using wavelet convolution. This dimensional transformation enables simultaneous detection of frequency and temporal characteristics of HFOs, providing two-dimensional characterization that improves detection precision and distinguishes physiological from pathological oscillations.
2Adaptability or versatility
If neuroprosthetics are used to treat epilepsy, then treatment options are expanded, but the devices cannot distinguish between physiological and pathological HFOs
Solution Approach 1:
The patent implements a feedback mechanism where detected HFO characteristics (frequency, power, duration) are used to modulate the electrical stimulation parameters. The system continuously monitors HFO activity and adjusts stimulation intensity and timing accordingly, creating a closed-loop control system that adapts to the patient's neural state in real-time.
Solution Approach 2:
The patent changes multiple parameters simultaneously to characterize HFOs: frequency range (80-600 Hz), power threshold (3 standard deviations above mean), duration (5-250 ms), and waveform morphology. These multi-parameter changes enable differentiation between physiological and pathological HFOs, allowing neuroprosthetics to selectively target pathological activity while preserving physiological functions.
3Reliability
If electrical stimulation is delivered without precise HFO detection, then device complexity is reduced, but therapeutic efficacy decreases
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of the neural signal before HFO detection, removing artifacts and noise in advance. This preliminary action simplifies subsequent detection steps and reduces computational complexity during real-time operation, while maintaining high therapeutic efficacy through accurate HFO identification.
Solution Approach 2:
The patent introduces wavelet convolution as an intermediary transformation step between raw signal acquisition and HFO detection. This intermediary technique efficiently extracts frequency-time characteristics with lower computational cost compared to traditional Fourier methods, reducing device complexity while preserving detection reliability.
Data Source
AI summary
A device and a signal processing method that can be used with a device to recognize and distinguish a physiological high-frequency oscillation (HFO) from a pathological high-frequency oscillation. The signal processing method detects a physiological HFO in the electrical brain signal one regimen of electrical or optogenetic brain stimulation can be triggered, alternatively if the method detects a pathological HFO associated with epilepsy a different regimen of electrical or optogenetic brain stimulation can be triggered. Thus, the signal processing method can be utilized in a closed loop brain stimulation device that serves the dual purpose of both enhancing memory encoding, consolidation, and recall, or improving cognition, and reducing the probability of a seizure in a patient with epilepsy.