EpiPredict: Neurological Predictive Analytics Platform

Section Overview: This opening section introduces the core clinical challenge of refractory epilepsy and summarizes the foundational specifications of the EpiPredict platform conceptualized by Samuelson G. Users can review key epidemiological metrics, explore the foundational system architecture mapping, and inspect the high-level quantitative breakthroughs achieved across multimodal sensing and neuromorphic edge processing.

Neuroengineering Predictive Neuroscience Biomedical Big Data

EpiPredict: A Multimodal Deep Learning and Neuromorphic Edge-Computing Framework for Prospective Epileptic Seizure Prediction

An end-to-end neurological predictive analytics platform by SAMUELSON G synthesizing channel-aware sparse scalp EEG/fNIRS, peripheral autonomic wrist sensing, multi-day chronobiological big data, hybrid 2D-CNN + Transformers, and sub-milliwatt neuromorphic Spiking Neural Networks (SPCLU) deployed on multi-core RISC-V edge processors.

Author: SAMUELSON G

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Affiliation: Neuroengineering & Predictive Neuroscience Research

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Target Accuracy: ≥ 70% Ambulatory Seizure Prediction

Global Impact

65M

People with epilepsy worldwide; 30-40% drug-resistant

Trauma Reduction

≥ 50%

Target reduction in unexpected epileptic events

Peak Sensitivity

99.31%

Achieved in spatio-temporal deep learning benchmark

Neuromorphic Gain

2,400×

SNN energy reduction vs standard ConvLSTM

Edge Latency

2.84 ms

GAP9 RISC-V inference (0.051 mJ/window)

Table 1: Structural Mapping of Foundational EpiPredict Specifications

Mapping clinical problem requirements to integrated multimodal & neuromorphic implementations

System Mapping Matrix

System Dimension	Baseline Clinical Specification	Advanced Architecture Implementation	Target Clinical & Operational Impact
Core Clinical Objective	Overcome unpredictable ictal onset in 30-40% drug-resistant population	Multi-horizon preictal state identification (3-30 min acute SPH/SOP; up to 4 hr forecasting)	≥ 70% ambulatory prediction accuracy; ≥ 50% event reduction
Sensing & Big Data Layer	Wearable EEG monitoring & neurological signal archives	Channel-aware sparse EEG (2.8 channels) + fNIRS + wrist sensors (PPG, EDA, ECG, TEMP, ACC)	84.4% channel pruning; 70% long-horizon variance captured via context
Software & Algorithmic Stack	Python, R, C++; TensorFlow, PyTorch, Keras	Hybrid 2D-CNN + Bi-LSTM / Set Transformer; Surrogate-Gradient Spiking ConvLSTM (SPCLU)	91.5% to 99.31% sensitivity; 0.01-0.145 FPR/h; 15-20% false-alarm reduction
Edge & Neuromorphic Hardware	Edge computing devices, neuromorphic sensors, low-latency units	Mixed-signal ADM (DYNAP-SE2) & multi-core RISC-V (GAP9) / ARM TinyML processors	80.7%-98.58% compute reduction; 2,400× SNN energy gain; 2.84 ms latency
Personalization & Adaptation	Adaptive machine learning models for brain pattern variations	AURA on-device continuous learning (detector pseudo-labels train plastic SNN on-chip)	Single-seizure calibration; zero cloud dependency; full patient data privacy

Section Overview: This section details the multi-sensor physical architecture and presents the crucial analytical discovery regarding predictive variance distribution. Users can interactively explore how cranial scalp EEG/fNIRS combine with peripheral wrist sensors (PPG, EDA, ECG, ACC, TEMP) and contextual big data telemetry. The chart below illustrates the explainable AI (SHAP) finding that contextual and autonomic telemetry drive 90% of multi-hour forecasting variance.

Multimodal Sensor Suite & Cranial Pruning

Standard clinical monitoring requires 18 to 22 wet scalp EEG electrodes, which are socially stigmatizing and uncomfortable. EpiPredict introduces a two-stage channel-aware Set Transformer that prunes the cranial montage down to a mean of 2.8 channels per patient while maintaining 80.1% sensitivity and 0.11 FPR/h. To compensate for scalp EEG motion vulnerability, discrete fNIRS optodes measure local preictal surges in oxyhemoglobin (HbO_2) and deoxyhemoglobin (HbR).

Inspect Sensor Modality Details:

Sparse Scalp EEG (2.8 Channels)

Captures cortical electrical activity at 256 Hz. Pruned using Set Transformer from 18 channels down to 2.8 channels without loss of predictive power.

Role: Acute Detection & Spike Micro-Morphology

Predictive Variance Attribution (SHAP Analysis)

Contribution to 30-min to 4-hour advance seizure forecasting

25 Patients / 40M Samples

Key Analytical Finding: While scalp EEG dominates acute ictal detection (98.5% accuracy), contextual big data telemetry (70%)—sleep staging, medication logs, physical exertion, and stress—dominates long-horizon 30m-4h prediction.

Section Overview: Continuous longitudinal monitoring reveals that seizures are tightly bound to endogenous physiological rhythms. This section allows users to inspect circadian (~24h) and multiday (~7d, ~14d, ~28d) phase-locking phenomena, calculate resultant circular phase vector (R-values), and evaluate the six-tier hierarchy of physiological signals for seizure risk discrimination.

Big Data Chronobiology & Multiday Phase Locking

Wavelet phase extraction and circular resultant vector (R-value) quantification across 232-day patient recordings

Hilbert Transform: z(t) = x_{bp}(t) + j \mathcal{H}\{x_{bp}(t)\}

Circular Resultant Vector Strength (R-value)

Phase locking measures the clustering of seizure onsets at specific preferred phases \phi_k of a physiological cycle. An R-value close to 1.0 indicates tight phase synchronization, whereas R to 0 indicates random timing.

Simulated Phase Coherence (R): 0.78

0.10 (Random Poisson) 0.50 (Moderate) 0.95 (Tight Phase Lock)

High Phase Coherence (R = 0.78): Seizures strongly cluster at the peak of the multiday autonomic cycle. Risk scoring multiplies acute EEG probability by 1.85\times during peak phase window.

Six-Tier Physiological Signal Discrimination Hierarchy

Tier 1 • Gold Standard

IEA Potentials

Intracranial Electrographic Activity

Tier 2 • Primary Autonomic

Residual HR

Circadian-adjusted resting heart rate

Tier 3 • Thermal Shift

Skin Temp (TEMP)

Peripheral vasoconstriction cycles

Tier 4 • Sudomotor

HR & Phasic EDA

Sympathetic arousal bursts

Tier 5 • Actigraphy

3-Axis ACC

Motor restlessness & posture

Tier 6 • Autonomic Baseline

Tonic EDA & HRV

Slow-varying sudomotor baseline

Section Overview: This section details the mathematical formulations powering both high-capacity cloud models (Hybrid 2D-CNN + Transformers) and edge neuromorphic models (Spiking ConvLSTM / SPCLU). Users can interactively test Leaky Integrate-and-Fire (LIF) membrane dynamics, review fast-sigmoid surrogate gradients, and explore the AURA on-device continuous learning workflow.

Spiking Neural Network (SPCLU) & LIF Dynamics

Event-driven neuromorphic membrane updates discretized via forward Euler (\Delta t = 16ms, \beta \approx 0.45)

// LIF Membrane Potential Update

u[t] = β * u[t-1] + W * x[t-1] - z[t-1] * u_thr

// Heaviside Spike Generation

z[t] = Θ(u[t] - u_thr) = 1 if u[t] ≥ u_thr else 0

// Fast-Sigmoid Surrogate Gradient (BPTT)

&partial;S / &partial;V ≈ 1 / (1 + β_sg * |V[t] - u_thr|)²

// L2 Firing-Rate Sparsity Penalty

L_total = L_CE + λ * ∑ (½ ∑ z[t] - r_target)²

Surrogate Gradient Learning: Because the true derivative of the Heaviside step function is zero everywhere (except at threshold where it is infinite), standard backpropagation fails. Substituting the fast-sigmoid surrogate during backward passes enables end-to-end AdamW optimization while maintaining binary event-driven spike execution during forward passes.

Interactive LIF Neuron Simulator

Inject synaptic current pulses to observe sub-threshold integration, threshold reset, and refractory dynamics.

Synaptic Input Weight (W \cdot x): 12.5 mV

Membrane Firing Threshold (u_{\text{thr}}): 20.0 mV

MEMBRANE POTENTIAL u(t) vs TIMESTEPS (dt=16ms)

Spikes Emitted: 3 | Average Firing Rate: 9.38 Hz (Target: 10 Hz)

// 2D Spectral Power Density Formulations

P_abs(i) = log( ∑ PSD(ω) ) for ω in Band_i

P_rel(i) = log( ∑ PSD(ω_i) / ∑ PSD(ω_total) )

R_ij = P_abs(i) - P_abs(j) (28 Pairwise Combinations)

// Multi-Head Scaled Dot-Product Attention

Attention(Q, K, V) = softmax( (Q * K^T) / √d_k ) * V

AURA On-Device Continuous Personalization Loop

Because preictal signatures vary between individuals, EpiPredict incorporates the AURA (Adaptive Unlabeled Real-time Approximate-learning) framework. When an embedded high-specificity detector identifies an active seizure onset, it retroactively labels the buffered preictal window and fine-tunes the plastic output layer on-chip using a single recorded seizure.

Feedback Calibration Gain: Reduces false alarm rate by 15% - 20% over 30 days without raw data cloud transmission.

Section Overview: Deploying deep neural networks onto wearable microcontrollers requires bridging a four-order-of-magnitude gap in compute and thermal budgets. This section presents empirical hardware benchmarks comparing multi-core RISC-V processors (GAP9), mixed-signal neuromorphic silicon (DYNAP-SE2), ARM Cortex microcontrollers, and edge gateways (Jetson Nano).

Hardware Execution Benchmarks

Inference Latency (ms) vs. Energy per Inference (mJ)

INT8 & SNN Acceleration

Silicon Winner: GAP9 RISC-V cluster executes INT8 EPIDENET in 2.84 ms consuming 0.051 mJ at 17.89 mW—delivering a 160× energy efficiency gain (40.61 GMAC/s/W) over ARM Cortex-M4.

Table 4: Edge Hardware Matrix

Processor	Power	Latency	Energy
GAP9 RISC-V	17.89 mW	2.84 ms	0.051 mJ
DYNAP-SE2 SNN	< 1.0 mW	< 10.0 ms	2400× lower
Tiny KAN MCU	< 20.0 mW	4.60 ms	< 0.10 mJ
Cortex-M4F	50-150 mW	100-405 ms	0.15-8.09 mJ
Jetson Nano	1.5-5.0 W	2.18 ms	3.3-10 mJ

Dual-Mode Selective Telemetry: Normal interictal data is discarded after local inference. High-resolution cloud streaming is activated only when k-of-n risk scores cross the warning threshold.

Section Overview: Evaluating seizure prediction requires rigorous event-level temporal definitions. This section allows users to dynamically simulate the neuro-ergonomic trade-off between the Seizure Prediction Horizon (SPH) and Seizure Occurrence Period (SOP), and provides a filterable benchmark comparison table across five foundational big data repositories.

Clinical Evaluation Protocol & SPH/SOP Optimization

Rigorous non-overlapping event-level testing (t_{\text{alarm}} + \text{SPH} \le t_{\text{onset}} \le t_{\text{alarm}} + \text{SPH} + \text{SOP})

Mathematically Optimal: SPH = 7 min | SOP = 27 min

SPH / SOP Trade-Off Simulator

Adjust the Seizure Occurrence Period (SOP) slider to see how window length impacts Event Sensitivity vs. False Prediction Rate per Hour (FPR/h).

Seizure Occurrence Period (SOP): 27 Minutes

10 min (Narrow SOP) 27 min (Optimum) 45 min (Wide SOP)

Simulated Clinical Metrics at SOP = 27 min:

Event-Level Sensitivity: 94.5%

False Prediction Rate: 0.045 FPR/h

Patient Anxiety Index: Low / Optimal

Table 3: Comprehensive Benchmark Dataset Comparison Matrix

Filter results across algorithm families and datasets

Model Architecture & Study	Dataset & Modality	SPH / SOP	Sensitivity	FPR / Hour	Accuracy / AUC
Foundational EpiPredict Target (Samuelson G)	Multimodal Wearables	Real-time	≥ 70.0%	Minimized	≥ 70.0% Target
Spatio-Temporal Hybrid DL	CHB-MIT / Siena EEG	5m / 30m	98.52% - 99.31%	0.010 - 0.045 /h	99.54% AUC
Temporal CNN + LSTM	CHB-MIT Scalp EEG	3m / 30m	97.75% - 98.21%	0.237 /h	98.10% Acc
Neuromorphic Spiking-CNN	CHB-MIT (Spike-Encoded)	Standard	95.10%	0.080 /h	99.20% Spec
Compact Multi-Head Attention	CHB-MIT Scalp EEG	20m / 30m	94.10% - 94.78%	0.050 /h	95.80% Acc
Transformer-Guided CNN	CHB-MIT (18-Ch STFT)	Standard	91.50% - 92.11%	0.145 /h	93.50% AUC
Patient-Specific Ensemble	EPILEPSIAE Database	5m / 30m	89.31%	0.030 /h	High Stability
Two-Stage Set Transformer	CHB-MIT (2.8 Channels)	3m / 30m	80.10% Mean	0.110 /h	84.4% Channel Pruning
Multimodal XGBoost + LSTM	25-Patient Multi-Sensor	30m - 4h	83.0% Pred / 98.5% Det	-15% to -20% Loop	99.0% Spec
Bimodal EEG + fNIRS LSTM	Neuro-Hemodynamic	Preictal	> 98.00%	Minimal	98.30% Acc

Section Overview: This final section outlines the four-phase development roadmap, details ethical governance and patient biometric privacy protections, records mandatory author declarations for **SAMUELSON G**, and provides a searchable bibliography of peer-reviewed references.

Four-Phase Clinical & Engineering Roadmap

Phase 1 • Complete

Algorithm & SNN Design

Finalize hybrid 2D-CNN/Transformer backbone and surrogate-gradient Spiking ConvLSTM on CHB-MIT, Siena, and EPILEPSIAE benchmarks.

Phase 2 • Current

Dataset Expansion

Multi-center collection of 3-channel dry EEG/fNIRS headbands & wrist telemetry across diverse demographic cohorts.

Phase 3 • Planned

EMU Collaborations

Deploy GAP9 and STM32 hardware prototypes alongside gold-standard inpatient video-EEG & implanted RNS units.

Phase 4 • Future

Closed-Loop Translation

Federated learning, homomorphic encryption, and prospective randomized clinical trials for FDA SaMD clearance.

Ethical Governance & Biometric Privacy

Continuous neuro-surveillance requires strict biometric protections. EpiPredict processes continuous EEG/autonomic streams directly on-device via TinyML edge engines, preserving edge data sovereignty. When cloud synchronization is required for clinician review, data are secured via homomorphic encryption and differential privacy.

Author Declarations & Institutional Compliance

Author Attribution: SAMUELSON G conceived the platform architecture, formulated the analytics workflow, and authored this specification.

Ethics Approval: Benchmarked on de-identified IRB-approved repositories (CHB-MIT, Siena, EPILEPSIAE, TUSZ) under the Declaration of Helsinki.

Competing Interests: The author declares no competing financial interests.

Publication References & Literature Citations

Peer-reviewed sources supporting the EpiPredict framework

Samuelson, G. EpiPredict: Advanced Neurological Predictive Analytics Platform. Technical Specification & Neuroengineering Report, 2026.
World Health Organization (WHO). Epilepsy and Neurological Disorder Global Epidemiological Reports. Geneva: WHO, 2024.
Stirling, R. E., et al. "Long-timescale cyclical changes in wearable and brain recordings and seizure phase locking in epilepsy." Epilepsia, doi:10.1101/2022.07.10.22277412.
Truong, N. D., et al. "Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram." Neural Networks, vol. 105, 2018.
Maiwald, T., et al. "Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic." Physica D, 2004.
Alaei, H. K., et al. "Optimal Seizure Prediction Horizon (SPH) and Seizure Occurrence Period (SOP) duration analysis." Biomedical Signal Processing.
Zambrana-Vinaroz, D., et al. "Wearable Epileptic Seizure Prediction System Using ECG, PPG and EEG Signals." Sensors, vol. 22, p. 9372, 2022.
Meisel, C., et al. "Machine Learning from Wristband Sensor Data for Wearable, Noninvasive Seizure Forecasting." Epilepsia, vol. 61, 2020.
Yang, Y., et al. "Neuromorphic deep spiking neural networks for seizure detection." Neuromorphic Computing & Engineering, vol. 3, 2023.
Tian, C., et al. "A neuromorphic computing approach for epileptic seizure prediction using Spiking CNNs." Frontiers in Neuroscience, vol. 17, 2023.
Herbozo Contreras, L. F., et al. "Towards Personalized Edge-AI for Medicine: Efficient Neuromorphic Frameworks for Seizure Forecasting (AURA Platform)." medRxiv, 2025.
Sipal, I., et al. "Multimodal fNIRS and EEG Fusion for Epileptic Seizure Prediction Using CNN, LSTM, and Transformers." arXiv:2601.05095, 2026.
ETH Zurich / GreenWaves GAP9 Group. "Energy-Efficient Quantized Seizure Detection (EPIDENET) on Multi-Core RISC-V." ETH Research Collection, 2024.
BioCAS Neuromorphic Group. "Event Driven Neural Network on a Mixed Signal Neuromorphic Processor (DYNAP-SE2) for EEG." IEEE BioCAS, 2024.
Set Transformer Group. "Two-Stage Channel-Aware Set Transformer Network for Wearable Seizure Prediction on CHB-MIT." arXiv:2507.15364, 2025.