Opportunity
The accurate measurement and assessment of the depth of anesthesia (DOA) in animal subjects, particularly in veterinary medicine, remain a significant challenge. Conventional methods rely heavily on anesthesiologists' subjective judgment based on physiological parameters like blood pressure, heart rate, and respiratory patterns. These methods are prone to bias due to pre-existing conditions, surgical nature, or medication side effects. Additionally, existing EEG-based DOA assessment tools are primarily designed for humans, with limited applicability to animals. Current machine learning techniques for DOA classification, such as FPGA, SVM, or DNN, achieve accuracies between 79% and 93%, but lack hardware implementations for real-time animal use. There is a pressing need for a reliable, automated system that can objectively measure DOA in animals with high accuracy and low latency, ensuring safe and effective anesthesia during surgical procedures.
Technology
The patent introduces a hardware-optimized system that implements a logistic regression classification mechanism to measure and assess DOA in animal subjects using electroencephalography (EEG). The system comprises five key components:
- Signal Pre-processor: Filters and down-samples raw EEG signals to remove noise and reduce data size while preserving essential features. It includes a 50 Hz band-stop IIR filter to mitigate power-line interference and a low-pass FIR filter (cut-off: 250 Hz) to focus on low-frequency EEG regions relevant to anesthesia.
- Epoch Generator: Creates 1-second, 10% overlapping epochs using a sliding window technique, optimizing memory usage and processing efficiency.
- Feature Extractor: Derives two critical features from each epoch:
- Derivative: Measures the rate of change in neural activity (squared differences between consecutive samples).
- Variance: Quantifies signal variability using the square root of squared deviations from the mean.
- Classifier: Expands features via mapping, applies logistic regression, and uses a sigmoid function to classify epochs as "awake" (output <0.5) or="" "anesthetized"="" (output="" ≥0.5).="">
- Predictor: Accumulates classifier outputs to constrain the decision score between 0 (awake) and 1 (fully anesthetized), achieving 100% channel prediction accuracy after ~7 seconds.
- The system achieves 94% classification accuracy for 1-second epochs, with a 12 µs latency and low power consumption (0.446 W on a 28 nm CMOS chip).
Advantages
- High Accuracy: 94% classification accuracy for 1-second epochs, rising to 100% after 7 seconds.
- Real-Time Performance: Low system delay (12 µs) suitable for live monitoring.
- Hardware Efficiency: Optimized for FPGA with minimal resource usage (e.g., 34.82% LUTs, 24.55% DSP blocks).
- Animal-Specific: Tailored for veterinary use, addressing gaps in existing human-centric tools.
- Low Power: Consumes 0.446 W, enabling portable applications.
Applications
- Veterinary Surgery: Precise DOA monitoring for rodents, felines, canines, and bovines during procedures.
- Research: Studying anesthesia effects on neural activity in animal models.
- Drug Development: Evaluating anesthetic efficacy and safety in preclinical trials.
- Portable Devices: Integration into compact, low-power monitoring systems for field use.
