Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Energy-Efficient Spiking Neural Network Architecture for Predictive Insulin Delivery

Sahil Shrivastava

Abstract

Diabetes mellitus affects over 537 million adults worldwide. Insulin-dependent patients require continuous glucose monitoring and precise dose calculation while operating under strict power budgets on wearable devices. This paper presents PDDS - an in-silico, software-complete research prototype of an event-driven computational pipeline for predictive insulin dose calculation. Motivated by neuromorphic computing principles for ultra-low-power wearable edge devices, the core contribution is a three-layer Leaky Integrate-and-Fire (LIF) Spiking Neural Network trained on 128,025 windows from OhioT1DM (66.5% real patients) and the FDA-accepted UVa/Padova physiological simulator (33.5%), achieving 85.90% validation accuracy. We present three rigorously honest evaluations: (1) a standard test-set comparison against ADA threshold rules, bidirectional LSTM (99.06% accuracy), and MLP (99.00%), where the SNN achieves 85.24% - we demonstrate this gap reflects the stochastic encoding trade-off, not architectural failure; (2) a temporal benchmark on 426 non-obvious clinician-annotated hypoglycemia windows where neither the SNN (9.2% recall) nor the ADA rule (16.7% recall) performs adequately, identifying the system's key limitation and the primary direction for future work; (3) a power-efficiency analysis showing the SNN requires 79,267x less energy per inference than the LSTM (1,551 Femtojoules vs. 122.9 nanojoules), justifying the SNN architecture for continuous wearable deployment. The system is not yet connected to physical hardware; it constitutes the computational middle layer of a five phase roadmap toward clinical validation. Keywords: spiking neural network, glucose severity classification, edge computing, hypoglycemia detection, event-driven architecture, LIF neuron, Poisson encoding, OhioT1DM, in-silico, neuromorphic, power efficiency.

An Energy-Efficient Spiking Neural Network Architecture for Predictive Insulin Delivery

Abstract

Diabetes mellitus affects over 537 million adults worldwide. Insulin-dependent patients require continuous glucose monitoring and precise dose calculation while operating under strict power budgets on wearable devices. This paper presents PDDS - an in-silico, software-complete research prototype of an event-driven computational pipeline for predictive insulin dose calculation. Motivated by neuromorphic computing principles for ultra-low-power wearable edge devices, the core contribution is a three-layer Leaky Integrate-and-Fire (LIF) Spiking Neural Network trained on 128,025 windows from OhioT1DM (66.5% real patients) and the FDA-accepted UVa/Padova physiological simulator (33.5%), achieving 85.90% validation accuracy. We present three rigorously honest evaluations: (1) a standard test-set comparison against ADA threshold rules, bidirectional LSTM (99.06% accuracy), and MLP (99.00%), where the SNN achieves 85.24% - we demonstrate this gap reflects the stochastic encoding trade-off, not architectural failure; (2) a temporal benchmark on 426 non-obvious clinician-annotated hypoglycemia windows where neither the SNN (9.2% recall) nor the ADA rule (16.7% recall) performs adequately, identifying the system's key limitation and the primary direction for future work; (3) a power-efficiency analysis showing the SNN requires 79,267x less energy per inference than the LSTM (1,551 Femtojoules vs. 122.9 nanojoules), justifying the SNN architecture for continuous wearable deployment. The system is not yet connected to physical hardware; it constitutes the computational middle layer of a five phase roadmap toward clinical validation. Keywords: spiking neural network, glucose severity classification, edge computing, hypoglycemia detection, event-driven architecture, LIF neuron, Poisson encoding, OhioT1DM, in-silico, neuromorphic, power efficiency.

Paper Structure

This paper contains 39 sections, 7 equations, 6 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Contributions
  3. Current Scope and Hardware Roadmap
  4. Related Work
  5. Closed-Loop Insulin Delivery
  6. Spiking Neural Networks in Medical Applications
  7. Machine Learning for Drug Delivery
  8. Physiological Glucose Simulation
  9. System Architecture
  10. VoltageBuffer
  11. EmergencyDetector with CGM Lag Compensation
  12. ThresholdBell---Edge-Triggered Comparator
  13. DoseCalculator---Sigmoidal Glucose-Responsive Formula
  14. SNN Severity Classifier
  15. Design Motivation: Why SNN Instead of a Rule-Based Threshold?
  16. ...and 24 more sections

Figures (6)

  • Figure 1: PDDS system architecture. The pipeline activates only on threshold crossings. The EmergencyDetector always runs first; its Path A (emergency) completely bypasses the injection pathway.
  • Figure 2: PDDSSpikingNet architecture. Input features are Poisson-encoded into binary spike trains over $T\!=\!50$ timesteps. Three LIF layers with decreasing $\beta$ values (0.95/0.90/0.80) create a hierarchy of temporal integration timescales. Classification is by spike-count argmax.
  • Figure 3: LIF neuron model. Membrane potential dynamics $V[t] = \beta V[t-1] + I[t]$, showing leakage, accumulation, and threshold crossing (spike). The surrogate gradient fast_sigmoid replaces the non-differentiable Heaviside step function during backpropagation.
  • Figure 4: PDDS Medallion data architecture. Bronze sources (OhioT1DM XML and simglucose CSV) are parsed into Silver per-patient CSV files, then feature-engineered into Gold NumPy arrays with ADA 2023 labels.
  • Figure 5: Sigmoidal dose-response curves for three severity levels. HIGH severity (dashed red) activates earliest---full dose engages at lower excess glucose. All curves are bounded by the 5.0 U safety cap. Inspired by Chou et al. chou2015 PBA glucose-responsive insulin.
  • ...and 1 more figures