Distributed Neuromorphic Computing for Bioelectric Diagnostics

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Solution Overview

Problem

Current bioelectric diagnostics and therapy systems lack embedded computing capability for local interpretation and millisecond decision-making, which is essential for real-time life-saving therapy, particularly in addressing ventricular tachyarrhythmias and fibrillation.

Innovation Solution

A medical apparatus with a mechanically flexible substrate and distributed neuromorphic computing units that integrate sensors, processing structures, and actuators, enabling real-time analysis and localized therapy by sensing physiological parameters, processing signals, and applying stimuli to the organ.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If distributed neuromorphic computing units are integrated into the substrate, then real-time processing speed is improved, but device complexity increases

Engineering Contradiction:

Improvereal-time processing speedVSAvoiddevice complexity

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The computing system is divided into multiple distributed neuromorphic processing units, each capable of independent real-time signal processing. This segmentation enables parallel processing across the substrate, achieving millisecond decision-making speed while distributing the overall system complexity across multiple simple, identical units rather than one complex centralized processor

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Each neuromorphic processing unit contains integrated sensors, processing structures, and actuators that operate autonomously. The units self-organize into a distributed network where each unit processes local bioelectric signals independently, eliminating the need for complex inter-unit communication infrastructure and reducing overall system complexity

Inventive Principle:

Principle #25Self-service

2Measurement precision

If sensor density is increased for higher spatial resolution, then measurement precision is improved, but energy consumption increases

Engineering Contradiction:

Improvespatial resolutionVSAvoidenergy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The high-density sensor array is divided into multiple zones, each served by a local neuromorphic processing unit. This segmentation enables each sensor to process and transmit only relevant local information rather than all sensors communicating with a central processor, reducing total energy consumption while maintaining high spatial resolution through distributed parallel processing

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts and processes critical features locally at each neuromorphic unit before transmission. By taking out only the essential signal characteristics for further processing rather than transmitting raw high-resolution data from all sensors, energy consumption is significantly reduced while measurement precision is preserved through local feature extraction

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS11701002B2Distributed neuromorphic computing for high definition bioelectric diagnostics and therapy

Publication Date: 2023.07.18 GEORGE WASHINGTON UNIVERSITY

AI summary

A medical apparatus for an organ has a substrate that conforms to a shape of the organ, and a plurality of processing units connected to the substrate and distributed throughout the substrate. Each of the processing units has a sensor, processing device and actuator. The sensor senses a condition of the organ and provides a sensed signal. The processing device receives the sensed signal from said sensor, analyzes the sensed signal and provides a control signal. The actuator applies an output pulse to the organ in response to the control signal from the processing device.