Subcortical Structure Classification via Neural Activity Analysis
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Solution Overview
Problem
Current microelectrode recording techniques for deep brain stimulation are inexact and subject to variable interpretation, requiring trained personnel and being affected by uncontrollable factors in the operating room, leading to challenges in accurately classifying target neural structures during surgery.
Innovation Solution
Analyzing neural activity as an electrode traverses the brain to classify structural regions by extracting features and synergistically combining them into a color-coded map, providing an objective indication of structural demarcations and allowing for accurate classification of subcortical structures using fuzzy clustering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microelectrode recording (MER) is used to locate and classify target neural structure, then the procedure can be performed during surgery, but the technique is inexact and subject to variable interpretation by different personnel
Solution Approach 1:
The patent replaces the acoustic signal interpretation method with an automated visual display system. Instead of personnel listening to audio signals and subjectively determining target structure location, the system processes neural activity signals through algorithms and presents objective visual classifications, eliminating human interpretation variability and improving measurement precision and reliability
Solution Approach 2:
The system performs automated classification of neural structures by processing signals independently without requiring trained personnel to interpret acoustic signals. The automated algorithm extracts features from neural activity and classifies structures autonomously, reducing reliance on human expertise and eliminating inter-observer variability
2Measurement precision
If trained personnel are required to interpret the audio signal, then accurate classification may be achieved, but the complexity of the procedure increases and specialized expertise is required
Solution Approach 1:
The patent substitutes the human expert interpretation system with an automated computational system. The visual display system with automated feature extraction and classification algorithms replaces the need for trained personnel to interpret acoustic signals, reducing procedure complexity while maintaining or improving classification accuracy
Solution Approach 2:
The system creates an objective visual representation (copy) of the neural activity data that directly displays structural classifications. Instead of requiring personnel to interpret abstract acoustic signals, the system generates visual displays that objectively represent the classified neural structures, making the procedure accessible without specialized training
3Speed
If acoustic signal monitoring is used to determine target structure, then real-time classification during surgery is possible, but the technique is affected by uncontrollable factors in the operating room
Solution Approach 1:
The patent replaces acoustic signal monitoring with direct electrical signal processing and visual display. By processing neural activity signals electronically and presenting visual classifications in real-time, the system eliminates susceptibility to acoustic interference from operating room equipment while maintaining real-time classification capability
Solution Approach 2:
The system introduces an intermediary processing stage that filters and processes neural activity signals before classification. The automated feature extraction and classification algorithms act as intermediaries that isolate the neural signals from operating room interference, ensuring reliable real-time classification regardless of environmental acoustic conditions
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
This approach provides an objective and accurate method for classifying subcortical structures, reducing reliance on trained personnel and minimizing the impact of operating room variables, enabling precise targeting of neural structures during deep brain stimulation procedures.
Implementation Method 1
the electrode transduces neural activity into an acoustic signal
Data Source
AI summary
Subcortical neural structures are classified during a microelectrode recording (MER) procedure. As the electrode traverses subcortical structures toward a target neural structure, neural activity is analyzed. The neural activity is converted to electrical signals. Features pertaining to characteristics of the neural activity are extracted from the electrical signals. The features are synergistically combined using fuzzy clustering logic, for example. In an example embodiment, the combined results are provided in a form of a color coded map indicating the different structural regions traversed. Observation of the map provides an objective indication of the demarcations of the various structural regions traversed and an objective technique for classifying the structural regions.