Residual Neural Network With Attention For Microelectrode Recordings
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Solution Overview
Problem
Current methods for precise localization of the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery are limited by the inability to accurately discriminate the STN from surrounding brain tissue using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans, and are sensitive to artifacts in microelectrode recordings.
Innovation Solution
A method using a deep residual neural network with attention is employed to classify microelectrode recordings. This network receives signals from microelectrodes, converts them into spectrograms, and uses a self-attention mechanism to improve classification accuracy by mitigating the effects of artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CT and MRI scans are used to determine electrode positioning, then the approximate location of the STN can be identified, but accurate discrimination of the STN from surrounding brain tissue is not possible
Solution Approach 1:
The patent introduces microelectrode recordings as an intermediary tool between the imaging scans and the STN localization. These recordings capture electrical activity patterns that serve as a mediator to identify the STN's precise location, overcoming the limitation of imaging techniques alone.
Solution Approach 2:
The patent replaces the mechanical/imaging-based localization system (CT and MRI scans) with an electrophysiological measurement system (microelectrode recordings). This substitution enables accurate STN discrimination by using electrical activity patterns rather than structural imaging.
2Measurement precision
If microelectrode recordings are used for precise STN localization, then accurate discrimination of the STN is possible, but the recordings are sensitive to artifacts that reduce reliability
Solution Approach 1:
The patent implements a feedback mechanism where the neural network continuously analyzes microelectrode recordings and adjusts its classification based on learned patterns. The system uses the recorded electrical activity as feedback to identify STN location while compensating for artifacts through the trained model's ability to distinguish true STN signals from noise.
Solution Approach 2:
The patent transforms the raw microelectrode recording signals into spectrograms, changing the parameter representation from time-domain to frequency-time domain. This parameter transformation enables the neural network to better identify STN-specific patterns while being more robust to temporal artifacts in the original recordings.
3Measurement precision
If traditional classification methods are used for microelectrode recordings, then the process is simpler, but the accuracy of STN identification is reduced
Solution Approach 1:
The patent replaces traditional signal processing and classification methods with a deep learning-based neural network system. This substitution significantly improves STN identification accuracy by leveraging the network's ability to learn complex patterns from spectrogram representations of the recordings.
Solution Approach 2:
The patent adds a new dimension to the analysis by transforming 1D time-series recordings into 2D spectrograms (frequency vs. time). This dimensional transformation provides the neural network with richer feature representation, enabling more accurate classification despite the increased system complexity.
Data Source
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AI summary
A method of creating a computer implemented classification tool for classification of microelectrode recordings taken during a deep brain stimulation using deep residual neural network with attention comprising steps collecting a data set of recordings taken during a deep brain stimulation; splitting recordings into overlapping time chunks, and converting time chunks into spectrograms; dividing data set into a training set, a validation set, and a test set putting each spectrogram into a deep neural network of ResNet architecture augmented with a self-attention layer added after each of ResNet layers, with a head layer comprising a single 2D convolutional layer followed by batch normalization and ReLU activation function wherein the network is trained to return zero for time chunks taken from recordings made outside of the STN region of a brain and to return one for time chunks taken form recordings made within the STN region of a brain, fine tuning the network with the validation set, cross checking the network with the test set.