Brain Frequency Signatures for DBS Electrode Localization
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Solution Overview
Problem
Current deep brain stimulation (DBS) surgeries for Parkinson's disease lack accurate localization of the sub-thalamic nucleus (STN), often mistaking the red nucleus for the STN, due to insufficient digital processing of microelectrode recordings, leading to suboptimal electrode placement and treatment outcomes.
Innovation Solution
A system and method for generating unique frequency signatures for brain organelles, including the STN, by aggregating and classifying microelectrode recordings from multiple patients, using Fourier transforms and Z-scores to create representative signatures that aid in real-time tissue detection during DBS surgeries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If power spectral density of beta band frequency is used to detect STN, then automated detection capability is improved, but measurement precision deteriorates due to insufficient discrimination between STN and red nucleus
Solution Approach 1:
The patent segments the frequency spectrum into multiple bands (delta, theta, alpha, beta, gamma) and analyzes each band separately. By dividing the spectral analysis into distinct frequency segments, the system can identify unique patterns in each band rather than relying on a single beta band measurement, thereby improving discrimination between STN and red nucleus while maintaining automated detection capability
Solution Approach 2:
The patent transitions from single-band frequency analysis to multi-dimensional spectral analysis by examining multiple frequency bands simultaneously. This dimensional expansion allows the system to capture more comprehensive tissue characteristics, enabling precise differentiation between STN and red nucleus through patterns across multiple frequency dimensions rather than relying on one-dimensional beta band power
2Adaptability or versatility
If empirical decision-making by physicians is used for electrode placement, then flexibility and adaptability are improved, but productivity and consistency of treatment outcomes deteriorate
Solution Approach 1:
The patent implements real-time feedback by continuously monitoring frequency spectral patterns during electrode insertion and providing immediate information about the electrode's position relative to target structures. This feedback loop allows the system to automatically adjust electrode placement to maintain optimal positioning, thereby ensuring consistent treatment outcomes while preserving surgical flexibility through real-time adaptability
3Ease of operation
If single frequency band analysis is used, then ease of operation is improved, but measurement precision and tissue differentiation capability deteriorate
Solution Approach 1:
The patent segments the frequency spectrum into multiple distinct bands (delta: 1-4 Hz, theta: 4-8 Hz, alpha: 8-13 Hz, beta: 13-30 Hz, gamma: 30-100 Hz) and analyzes each band separately. This segmentation allows the system to maintain operational simplicity through standardized analysis protocols for each band while achieving superior tissue differentiation by examining patterns across all bands rather than relying on a single frequency range
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of target identification and electrode placement by providing distinct frequency signatures for various brain tissues, enabling neurologists to make informed decisions during surgeries, thereby enhancing the effectiveness of DBS treatments.
Implementation Method 1
The MER data is classified using the desired option, and preferably by retrospective 'marking' of the exemplary recordings. Using the signature calculation tool, a MATLAB program, the classified representative MER data is further transformed into the representative frequency domain, for example, using a Fourier transform.
Data Source
AI summary
A system and method for calculating representative frequency signatures for target regions of the brain. The method includes aggregating representative classified microelectrode recordings taken from regions of the brain of multiple patients to an initial database. The classified microelectrode recording data is then transformed into the frequency domain. Frequencies profiles, grouped according to each target region of the brain they represent, are stored in a database. A coherence or Z-score representing the prominence of a frequency with respect to other frequencies within a group of frequencies profiles for each target region of the brain they represent is then calculated and stored, as well as the average power of the frequencies within the group of frequency profiles. Redundant frequencies or frequencies above or below pre-determined thresholds are removed from the group of frequency. The resulting representative frequency signature for each target region of the brain is stored as a group of frequencies, a coherence or Z-score and an average power of the respective frequencies. During a DBS surgery, the desired representative frequency signature file(s) representing target regions of the brain are selected, which representative frequency signature file(s) include a list of frequencies, their respective powers, and Z-scores. During DBS surgery, real time microelectrode recordings of brain signals from a patient are obtained, and the powers of frequencies of the real time microelectrode recordings are calculated at frequencies found in the signatures. The calculated powers of frequencies of the real time microelectrode recordings are compared to the respective powers in the pre-selected representative frequency signature files, and a percent error via the commonly accepted method, and averaging the group of errors via the commonly accepted root mean square calculation is calculated. A negative averaged percent error over time is plotted and displayed as a measure of frequency signature strength in order to detect movement of the stimulating electrode being implanted with respect to the target region of the brain.