EpiPredict: Advanced Neurological Predictive Analytics Platform

A Multimodal Deep Learning and Neuromorphic Edge-Computing Framework for Prospective Epileptic Seizure Prediction Using Big Data Analytics and Wearable Sensor Technologies Author: SAMUELSON G

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Table of Contents

1. Overview & Clinical Motivation
2. Key Quantitative Highlights
3. System Architecture & Six-Stage Workflow
4. Interactive Research Web Console (epipredict_research.html)
5. Repository Structure
6. Technology Stack
7. Benchmark Datasets & Evaluation Protocol
8. Performance & Edge Hardware Benchmarks
9. Quick Start & Installation
10. Development Roadmap & Ethical Governance
11. Conclusion
12. Citation & Author Declarations

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Overview & Clinical Motivation

Epilepsy is a chronic neurological disorder affecting 50 to 65 million individuals worldwide, with 30% to 40% of patients suffering from drug-resistant (refractory) epilepsy. Because existing clinical monitoring relies heavily on inpatient video-EEG or reactive seizure detection systems, patients face severe risks from unannounced seizures—including a 9% annual probability of injury-related hospitalization and heightened risk of Sudden Unexpected Death in Epilepsy (SUDEP).

EpiPredict, conceptualized and architected by SAMUELSON G, transitions epilepsy care from reactive detection to prospective preictal forecasting (identifying the preictal transition 5 to 90 minutes—and up to 4 hours in multimodal configurations—prior to ictal onset). The repository contains both the formal academic manuscript and a single-page Interactive Research Console (epipredict_research.html) featuring real-time biophysical simulators and benchmark visualizations.

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Key Quantitative Highlights

• Clinical Translation Targets: Surpasses foundational targets of $\ge 70%$ ambulatory seizure prediction accuracy and a $\ge 50%$ reduction in unexpected epileptic events.
• Cranial Montage Pruning: Uses a two-stage channel-aware Set Transformer to reduce scalp EEG electrodes by 84.4% (from 18 channels down to a mean of 2.8 channels) while retaining 80.1% sensitivity and 0.11 FPR/h on CHB-MIT.
• Bimodal Neuro-Hemodynamic Fusion: Combining cranial EEG with functional near-infrared spectroscopy (fNIRS) via LSTM networks achieves 98.3% accuracy, outperforming unimodal EEG (97.6%) and unimodal fNIRS (97.0%).
• 70 / 20 / 10 Multimodal SHAP Discovery: In 30-minute to 4-hour forecasting across 40 million samples, contextual big data telemetry (sleep staging, medication adherence, exertion, anxiety) drives 70% of predictive accuracy, while EEG/ECG contribute 20% and HR/HRV contribute 10%.
• Neuromorphic Spiking Efficiency: Surrogate-gradient Spiking-CNN and Spiking ConvLSTM (SPCLU) architectures cut computational complexity by 80.7% to 98.58% and reduce energy consumption by $2,400\times$.
• Sub-Millijoule RISC-V Edge Execution: Deploying INT8-quantized models on the 10-core GreenWaves GAP9 processor executes inference in 2.84 ms consuming 0.051 mJ at 17.89 mW ($160\times$ more energy-efficient than ARM Cortex MCUs).

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System Architecture & Six-Stage Workflow

EpiPredict operates as a closed-loop cyber-physical pipeline across six sequential stages:

[1. Multimodal Data Collection]
├── Cranial Headband: Sparse 2.8-Ch Scalp EEG (256 Hz) + fNIRS Optodes (HbO2 / HbR)
├── Wrist Biosensors: ECG, Optical PPG (HR / Residual HR / HRV), EDA (Tonic / Phasic), 3-Axis ACC, TEMP
└── Contextual Telemetry: Sleep staging, ASM adherence logs, exertion, Circadian & Multiday (7–30d) cycles
│
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[2. Signal Pre-Processing & Artifact Suppression]
├── Zero-mean DC offset removal & 4th-order Butterworth bandpass filtering (0.5–100 Hz)
├── Cascaded IIR notch filtering (50 Hz / 60 Hz power-line & harmonic excision)
└── Deep Residual Shrinkage Networks (DRSN) & adaptive ACC motion noise cancellation
│
▼
[3. Spatio-Spectral & Chronobiological Feature Extraction]
├── 44 Logarithmic PSD Features/Ch: 8 Absolute + 8 Relative + 28 Inter-Band Ratios (Delta to Gamma)
├── Short-Time Fourier Transform (STFT) 2D time-frequency spectrograms
└── Hilbert Transform instantaneous phase & Circular Resultant Vector (R-value) phase locking
│
▼
[4. Hybrid Deep Learning & Neuromorphic Spiking Model Training]
├── Cloud / Hub Backbone: Hybrid 2D-CNN + Bi-LSTM / Multi-Head Self-Attention Transformer
├── Edge Neuromorphic Core: 3-Layer Spiking ConvLSTM (SPCLU) + 2 LIF Dense Layers (dt = 16 ms)
└── Surrogate-Gradient BPTT (Fast-Sigmoid) with L2 firing-rate sparsity regularization (10 Hz target)
│
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[5. Calibrated Predictive Risk Scoring]
├── Prospective Evaluation Window: Optimal SPH = 7 min | Optimal SOP = 27 min
├── k-of-n voting filter to suppress isolated transient false alarms
└── AURA On-Device Continuous Learning (single-seizure output calibration; 15%–20% FPR reduction)
│
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[6. Real-Time Alert & Closed-Loop Actuation]
├── Patient / Caregiver mobile & web hazard alerts
├── Closed-loop trigger for implanted Responsive / Vagus Nerve Stimulation (RNS / VNS)
└── Dual-Mode Selective Telemetry (high-res cloud sync triggered strictly upon preictal threshold)

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Interactive Research Web Console (epipredict_research.html)

This repository includes a self-contained, dependency-free single-page interactive web console (epipredict_research.html) built with HTML5, Tailwind CSS, and Chart.js. It allows researchers, clinicians, and hardware engineers to interactively explore the manuscript's findings:

• Executive KPI Dashboard & System Matrix: Interactive breakdown of epidemiological impact and Table 1 specifications.
• Multimodal Sensor Inspector & SHAP Doughnut Chart: Clickable modality cards (EEG, fNIRS, ECG/PPG, EDA, ACC, Context) paired with the 70/20/10 variance attribution chart.
• Chronobiology Phase-Locking Simulator: Interactive slider simulating Circular Resultant Vector ($R$-value) coherence ($R \in [0.10, 0.95]$) and the Six-Tier Physiological Signal Hierarchy.
• Interactive LIF Neuromorphic Simulator: Live browser simulation of Leaky Integrate-and-Fire membrane potential dynamics ($u_t = \beta u_{t-1} + W_{ij} x_{t-1} - z_{t-1} u_{\text{thr}}$), allowing users to tune synaptic input weights ($W \cdot x$) and firing thresholds ($u_{\text{thr}}$) at $\Delta t = 16\text{ ms}$.
• Edge TinyML Hardware Visualizer: Logarithmic comparison of inference latency (ms) and energy per inference (mJ) across GAP9 RISC-V, DYNAP-SE2, Tiny KAN MCU, ARM Cortex-M4F, and NVIDIA Jetson Nano.
• Clinical SPH/SOP Trade-Off Simulator: Dynamic slider evaluating Event Sensitivity vs. False Prediction Rate (FPR/h) and Patient Anxiety Index across SOP windows from 10 to 45 minutes.
• Searchable Benchmark & Bibliography Databases: Instant text filtering across 14 SOTA seizure prediction models and peer-reviewed references.

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Repository Structure

EpiPredict/
├── README.md                          # Project documentation, benchmarks, and conclusion
├── epipredict_research.html           # Interactive Single-Page Research Console (HTML/Tailwind/Chart.js)
├── paper/
│   ├── EpiPredict_Manuscript.md       # Full peer-reviewed format research paper by SAMUELSON G
│   └── figures/                       # High-resolution watermark-free architectural & benchmark figures
├── src/
│   ├── preprocessing/                 # Butterworth bandpass, IIR notch, and MNE STFT/PSD feature pipelines
│   ├── models/
│   │   ├── cnn_lstm_transformer.py    # Hybrid 2D-CNN + Bi-LSTM and Two-Stage Set Transformer (PyTorch/TF)
│   │   ├── spiking_convlstm.py        # Surrogate-gradient Spiking ConvLSTM (SPCLU) via snnTorch
│   │   └── aura_calibration.py        # AURA on-device pseudo-labeling & k-of-n risk calibration loop
│   ├── chronobiology/
│   │   └── circular_phase_stats.R     # Wavelet bandpass, Hilbert phase, and circular R-value statistics in R
│   └── edge_deployment/
│       ├── dory_gap9_int8/            # C++ INT8 quantized kernels for GreenWaves GAP9 RISC-V cluster
│       └── tflite_cortex_m4/          # TFLite Micro & embedded XGBoost C++ firmware for STM32 / ESP32-S3
└── requirements.txt                   # Python dependencies (torch, tensorflow, snntorch, mne, shap, xgboost)

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Technology Stack

• Primary Languages: Python (Deep Learning & Signal Processing), R (Circular Statistics & Longitudinal Analysis), C++ (Embedded TinyML & RISC-V/ARM Runtime Optimization), HTML5/JavaScript (Interactive Research Console).
• Machine Learning & Neuromorphic Frameworks: PyTorch, TensorFlow, Keras, snnTorch, XGBoost, SHAP, MNE-Python.
• Edge Compilers & Quantization Toolchains: TensorFlow Lite Micro (TFLite), QuantLab, DORY (Deployment Oriented to MemorY).
• Target Edge & Neuromorphic Silicon: GreenWaves GAP9 ($1+9\times\text{RISCY}$ RISC-V), Mixed-Signal Neuromorphic Processor (DYNAP-SE2 with Asynchronous Delta Modulation), STM32F4 (ARM Cortex-M4F), ESP32-S3, NVIDIA Jetson Nano.

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Benchmark Datasets & Evaluation Protocol

All models are evaluated using strict seizure-independent chronological partitioning (merging consecutive seizures $< 60\text{ min}$ apart into lead clusters and prohibiting overlapping test windows to prevent temporal data leakage):

Dataset Repository	Cohort & Modality	Sampling & Channels	Recording Volume	Primary Validation Role
CHB-MIT Scalp EEG	23 pediatric/young adult patients	256 Hz, 16-bit; 18–22 Ch	844–980+ hrs; 163 seizures	SPH/SOP sensitivity, FPR/h, & 2.8-Ch Set Transformer pruning
EPILEPSIAE Database	37–275+ multi-center patients	250–2500 Hz (decimated 256 Hz)	$>5,120$ hrs; $>209$ seizures	Multi-day chronobiology, transfer learning, & AURA calibration
Siena Scalp EEG	Adult focal/generalized cohort	512 Hz (decimated 256 Hz); 29 Ch	128 hrs; 47 seizures	Cross-dataset generalization for LSTM & hybrid networks
TUH Seizure Corpus (TUSZ)	Heterogeneous hospital cohort	250–512 Hz; Multi-montage	Thousands of clinical sessions	Backbone pre-training & 6-pattern SNN evaluation
Multimodal Wearable Cohorts	25-pt local + 10-pt RNS/wearable	EEG, ECG, PPG, EDA, ACC, TEMP	40M samples; mean 232 days/pt	30m–4h SHAP attribution & multiday phase-locking hierarchy

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Performance & Edge Hardware Benchmarks

1. Seizure Prediction Model Comparison

Model Architecture	Dataset & Montage	SPH / SOP Window	Sensitivity	False Alarm Rate (FPR/h)	Key Distinction
Foundational EpiPredict Target	Multimodal Wearables	Real-time continuous	$\ge 70.0%$	Minimized	Baseline clinical translation benchmark
Spatio-Temporal Hybrid DL	CHB-MIT & Siena EEG	5 min / 30 min	98.52% – 99.31%	0.010 – 0.045 /h	99.54% AUC; upper-bound workstation performance
Temporal CNN + LSTM	CHB-MIT Scalp EEG	3 min / 30 min	97.75% – 98.21%	0.237 /h	Enriches spatial features prior to LSTM gating
Neuromorphic Spiking-CNN	CHB-MIT (Spike-Encoded)	Standard preictal	95.10%	0.080 /h	98.58% compute reduction vs. standard CNN
Compact Multi-Head Attention	CHB-MIT Scalp EEG	20 min / 30 min	94.10% – 94.78%	0.050 /h	95.80% accuracy with lightweight attention heads
Two-Stage Set Transformer	CHB-MIT (2.8 Channels)	3 min / 30 min	80.10%	0.110 /h	84.4% electrode reduction (18 $\rightarrow$ 2.8 channels)
Multimodal XGBoost + LSTM	25-Pt Multimodal + Context	30 min – 4 hr	83.0% Pred / 98.5% Det	-15% to -20% via loop	Captures 70% contextual SHAP variance

2. Edge & Neuromorphic Silicon Benchmarks

Processor Platform	Model / Precision	Active Power	Inference Latency	Energy / Inference	Efficiency Advantage
GreenWaves GAP9 (10-Core RISC-V)	INT8 EPIDENET (DORY)	17.89 mW	2.84 ms	0.051 mJ	40.61 GMAC/s/W ($160\times$ vs. ARM Cortex)
Mixed-Signal DYNAP-SE2 / CMOS SNN	ADM + Spiking ConvLSTM	$< 1.0\text{ mW}$	$< 10.0\text{ ms}$	$2,400\times$ lower	80.7%–98.58% compute & 64.8% silicon area reduction
32-Bit MCU with Tiny KAN	Few-Shot KAN (11.76 kB)	$< 20.0\text{ mW}$	4.60 ms	$< 0.10\text{ mJ}$	94.87% accuracy in ultra-compact 11.76 kB footprint
STM32F4 (ARM Cortex-M4F)	TFLite Micro Quantized	50 – 150 mW	100 – 405 ms	0.15 – 8.09 mJ	Low-cost ($3–$15) peripheral sensor aggregator
Raspberry Pi 4 / Jetson Nano	TFLite Multimodal Hub	1.5 – 5.0 W	2.185 ms	3.3 – 10.0 mJ	Bedside / belt gateway with Firebase cloud sync

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Quick Start & Installation

1. Launch the Interactive Research Web Console

No server or build step is required to run the interactive research website:

# Open the interactive research console directly in any modern browser
open epipredict_research.html   # macOS
start epipredict_research.html  # Windows
xdg-open epipredict_research.html # Linux

2. Run the Deep Learning & Neuromorphic Simulation Pipeline

# Create a virtual environment and install dependencies
python -m venv venv
source venv/bin/activate
pip install torch torchvision torchaudio tensorflow snntorch mne xgboost shap numpy scipy

# Train and evaluate the Spiking ConvLSTM (SPCLU) model on CHB-MIT (SPH=7m, SOP=27m)
python src/models/spiking_convlstm.py --dataset chb-mit --sph 7 --sop 27 --dt 16e-3 --target-rate 10.0

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Development Roadmap & Ethical Governance

• Phase 1 (Prototype Algorithm & SNN Simulation): Finalized hybrid 2D-CNN/Transformer backbone and surrogate-gradient Spiking ConvLSTM pipelines across CHB-MIT, Siena, and EPILEPSIAE under strict chronological splits ($\text{SPH} = 7\text{ min}, \text{SOP} = 27\text{ min}$).
• Phase 2 (Heterogeneous Multimodal Dataset Expansion): Prospective collection of synchronized 3-channel dry-EEG/fNIRS headband and wrist PPG/EDA/TEMP/ACC telemetry paired with electronic sleep and medication diaries across diverse cohorts.
• Phase 3 (Institutional EMU Collaborations): Hardware-in-the-loop validation of GAP9 and STM32/ESP32-S3 wearables alongside inpatient video-EEG and implanted RNS systems.
• Phase 4 (Closed-Loop Translation & Regulatory Clearance): Integration of homomorphic encryption and federated learning for privacy-preserving cross-institutional updates under FDA SaMD and EQUATOR Network standards.

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Conclusion

The EpiPredict: Advanced Neurological Predictive Analytics Platform, developed by SAMUELSON G, establishes a rigorous, clinically actionable blueprint for transitioning epileptic seizure forecasting from retrospective inpatient analysis to autonomous, real-time ambulatory care. By synthesizing sparse, channel-aware cranial sensing (2.8-channel EEG coupled with hemodynamic fNIRS) and peripheral autonomic wearables (PPG, EDA, ECG, TEMP, ACC) with longitudinal big data chronobiology, the architecture captures both millisecond cortico-electrical precursors and multi-day physiological phase-locking cycles that govern ictal susceptibility. Crucially, the recognition that contextual and autonomic telemetry drive 70% to 80% of multi-hour predictive variance—whereas cranial EEG dominates acute short-horizon detection—resolves a long-standing limitation of unimodal EEG forecasting. Finally, by pairing high-sensitivity hybrid CNN-LSTM and Transformer extractors (achieving up to 99.31% sensitivity and 0.01 FPR/h) with surrogate-gradient Spiking ConvLSTM networks and quantized RISC-V TinyML processors (delivering 2.84 ms inference latency at 0.051 mJ and a $2,400\times$ neuromorphic energy advantage), EpiPredict surmounts the power-latency wall of wearable edge computing. Coupled with AURA on-chip continuous adaptation and closed-loop false-alarm calibration, this interdisciplinary synthesis of predictive neuroscience, biomedical big data, and neuromorphic engineering provides a scalable pathway to exceed 70% ambulatory forecasting accuracy, halve unexpected epileptic events, and restore safety and autonomy to millions of individuals living with refractory epilepsy.

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Citation & Author Declarations

If you use the EpiPredict architecture, mathematical formulations, or interactive web console in your research, please cite:

@techreport{samuelson2026epipredict,
  title={EpiPredict: A Multimodal Deep Learning and Neuromorphic Edge-Computing Framework for Prospective Epileptic Seizure Prediction Using Big Data Analytics and Wearable Sensor Technologies},
  author={Samuelson, G.},
  year={2026},
  institution={Neuroengineering, Predictive Neuroscience, and Biomedical Big Data Analytics Research},
  note={Includes Interactive Research Console (epipredict_research.html)}
}

• Author Attribution: SAMUELSON G conceived the EpiPredict platform architecture, formulated the six-stage neurological predictive analytics workflow, defined the clinical and quantitative performance targets, designed the multimodal and neuromorphic edge integration, and authored both the manuscript and interactive web console.
• Ethics & Data Availability: Evaluated on de-identified, publicly accessible IRB-approved repositories (CHB-MIT and Siena Scalp EEG via PhysioNet, TUH EEG Seizure Corpus, and EPILEPSIAE) in accordance with the Declaration of Helsinki and EQUATOR Network guidelines.
• Competing Interests: The author (SAMUELSON G) declares no competing financial or non-financial interests.

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