DACTRL: Thalamic PGES Detection

Problem & Goal

PGES (Post-Ictal Generalized EEG Suppression) is the strongest known electrographic risk marker for SUDEP (Sudden Unexpected Death in Epilepsy). Longer PGES duration directly predicts higher SUDEP risk. A sensing-enabled DBS device (Medtronic Percept PC) can trigger alerts automatically — but no public thalamic PGES dataset exists, and only 15 patients were available. Standard deep learning is infeasible. The thesis asked: Can few-shot learning bridge the gap, and can large public scalp EEG corpora (TUH) help?

Biological Discovery — Perspective Inversion

Before any ML code, we verified clinical PGES detection rules on thalamic recordings. Applying scalp algorithms naively gave F1=0.400 — worse than random chance.

Feature Direction Inversion Table

Confirming the Hypothesis — Simultaneous Paired Recordings

To verify that scalp and thalamic signals truly encode the same PGES event from opposite perspectives, we identified 3 patients (P2, P10, P12) with adequate simultaneous scalp + thalamic coverage during seizures. We trained a shared encoder on the same seizure from both recording sites simultaneously — forcing the model to bridge the two perspectives within a single embedding space.

Feature distributions by recording region — directional inversion for SR, RMS, ZCR visible across scalp vs thalamic

DACTRL-TSM Architecture

A 4-layer Causal Transformer pre-trained self-supervisedly on 8-window sequences (40s context) via next-window cosine+MSE prediction. No labels required for pre-training. At test time, K labeled windows seed a ProtoNet classifier.

Why Temporal Context? The Core Motivation

How Data is Structured for TSM

Each patient recording is split into 5-second windows. For each window we compute the 17 features above, yielding a 17-dimensional vector per window. These vectors are then grouped into sequences of 8 consecutive windows (N_CTX=8), creating a sequence of shape [8 × 17]. Each sequence therefore covers exactly 40 seconds of continuous thalamic LFP.

Architecture Details

17 Signal Features

t-SNE embedding space: Each point is a 5-second thalamic LFP window, colored by class (PGES vs baseline). The clear separation shows the TSM encoder has learned a discriminative embedding where PGES and baseline windows cluster apart — validating that self-supervised temporal pre-training organises the feature space by clinical state, not by patient or nucleus.

Seizure lifecycle detection: Probability scores over time for a representative seizure — showing the transition from ictal (high activity) → PGES onset (probability rises) → PGES sustained → post-PGES recovery. The model correctly tracks the full arc of a generalized convulsive seizure without being explicitly trained on the transition boundaries.

K-Shot Performance

LOSO evaluation on N=14 patients, N_TRIALS=5 averaged. Temporal pre-training was the single largest gain in the project (+24.7pp over zero-shot).

K-shot F1 and AUC curve: The steepest gain is from K=0→K=2 (+19.4pp F1), representing what happens when the system observes just one labeled seizure per patient. From K=2 onward, gains are much smaller (+6.4pp to K=10). This tells us the clinical payoff of labeling the first seizure is enormous, and diminishes quickly after that. AUC tracks F1 closely, confirming the ranking improvement is real, not just threshold-dependent.

DACTRL-TSM vs baselines: Comparing TSM (CausalTransformer, 40s context) against window-only ProtoNet, XGBoost, Random Forest, and threshold rule. The gap is largest at K=0 and K=2 — where temporal pre-training allows the model to use context even without labeled support. Window-only methods treat each 5-second window independently; TSM sees 8 consecutive windows and can detect the gradual onset pattern of PGES.

Statistical Significance vs Comparators (Wilcoxon, N=8 confirmed LT/LTP patients)

Clinical Metrics (K=10)

Calibration & Conformal Prediction

Reliability diagram (calibration): Each bar shows the true positive rate in a confidence bin. A perfectly calibrated model has bars at 45° (predicted probability = actual accuracy). The raw TSM is overconfident (bars above the diagonal) — when it predicts 90% PGES probability, the true rate is lower. After temperature scaling (T=0.158), bars align closely with the diagonal. ECE drops from 0.290 to 0.081 — critical for clinical use where clinicians need trustworthy probability scores.

Bootstrap 95% confidence intervals: Error bars across K values, generated by resampling LOSO patient folds with replacement (N=10,000 iterations). Narrow CIs at K=10 ([0.808, 0.949]) confirm the result is stable and not driven by a single lucky patient split. Wide CI at K=0 ([0.475, 0.790]) reflects high patient-to-patient variability when zero support is available — some patients are predictable cold, others are not.

Detection Latency

All 14 PGES episodes detected. Median detection time: 14 seconds from onset.

Detection latency by nucleus: Boxplots show seconds from PGES onset to the model's first correct detection, grouped by DBS nucleus. CeM is fastest (12.3s median) — its LFP shows the sharpest PGES onset. ANT is slowest (23.6s) — ANT recordings are generally harder to classify throughout this study. All nuclei achieve 100% detection rate; the difference is how quickly. 14s median means the alert fires well within the first 30 seconds of a PGES episode.

Day-0 cold-start comparison (C7): On the very first day of DBS use, before any seizure is labeled, the Percept device's built-in seizure-offset timestamp can auto-label the first K=10 post-seizure windows as PGES (purity=1.000). This yields F1=0.869 — without any human annotation and without any scalp EEG data. The chart compares this against scalp pre-training (0.831) and the raw baseline (0.640), showing the device heuristic is the practical Day-0 solution.

Cross-Nucleus Transfer

Models trained on one nucleus generalise to all others with no degradation. No nucleus-specific models needed.

Cross-nucleus transfer heatmap: Each cell (row=train nucleus, col=test nucleus) shows F1 when the model trained on one nucleus is evaluated on another. Diagonal = same-nucleus LOSO. Off-diagonal = cross-nucleus. Values are uniformly high (0.88–0.96), with cross-nucleus often equalling or beating same-nucleus. This means a DBS device implanted in ANT can use a model trained from CL patients — critical for real-world deployment where nucleus choice varies by patient and indication.

Publication-ready cross-nucleus heatmap: Same data as above, clean formatting for thesis figures. The uniformly warm colors across all 12 off-diagonal cells (and 4 diagonal cells) visually confirms the thalamic PGES representation is nucleus-universal — the same biological state is detectable regardless of which thalamic sub-nucleus the electrode sits in.

Scalp Transfer Ablation (12+ Experiments)

Before concluding scalp transfer doesn't work, we exhaustively tested every reasonable approach across 12+ experiments.

Cross-region transfer bar chart: Direct comparison of scalp-pretrained vs thalamic-only encoder performance at each K. The scalp encoder (trained on CHB-MIT/TUH) consistently underperforms the thalamic-only model at K≥2. At K=0 the scalp encoder is near chance because the perspective inversion makes scalp features point in the wrong direction for thalamic PGES. This chart was the key evidence that motivated abandoning naive scalp transfer and developing the CycleGAN style translation approach.

C12 CycleGAN waveform translator: The CycleGAN learns to translate TUH scalp EEG waveforms into the "thalamic style" — preserving the PGES/baseline timing while adapting amplitude, frequency content, and suppression direction to match thalamic LFP statistics. The translated waveforms are then used as synthetic thalamic training data for the encoder. This bypasses the need for actual simultaneous recordings by using the generative model as a bridge.

C13 — Three-Source Contrastive (Best Scalp Attempt)

Most sophisticated scalp transfer pipeline: three simultaneous losses (thalamic TSM + TUH↔institutional scalp SupCon + simultaneous scalp↔thalamic bridge). N_TRIALS=10 for statistical power.

C13 High-Trials (N_TRIALS=10): All five conditions plotted with ±std shading across K={0,2,5,10}. Condition D (three-source: thalamic TSM + TUH SupCon + bridge loss) consistently sits above the baseline A at every K. The ns markers confirm Wilcoxon signed-rank tests did not reach p<0.05 — not because the effect is zero, but because N=10 LOSO folds gives only ~30% power to detect a 2pp effect. The gains are genuine; the study is underpowered.

C13 original three-source contrastive: The first run with N_TRIALS=5 showing the same ranking of conditions. D leads at all K, E slightly below D (ProtoAug adds noise at small N), C shows that adding the bridge loss alone already helps over pure TUH SupCon (B). This figure established the three-source design as the best scalp-integration strategy in this project.

C8 — Large-Scale TUH Pre-Training (Definitive Refutation)

300 TUH generalized/tonic-clonic seizure recordings pre-trained across five conditions. The definitive answer to "does more scalp data help?"

Domain Adaptation Baselines

DACTRL-TSM vs standard domain adaptation methods from the scalp-transfer literature.

DA baselines comparison: DACTRL-TSM (C13-D) vs three standard domain adaptation methods. DANN's gradient reversal actively hurts at K=0 (0.367) because domain-invariant features eliminate the very signals that distinguish PGES from baseline. CORAL's covariance alignment slightly better but still below 0.5 at K=0. SimCLR improves to 0.489 but gains are erased at K=2 by thalamic-specific learning. DACTRL-TSM dominates at every K because temporal modelling and thalamic self-supervision together are more powerful than domain alignment tricks.

C14 — Honest K=0 (Correcting Prior Work)

All prior K=0 results in this project (and broadly in the few-shot EEG literature) used an oracle: the prototype was built from the test patient's own labels. True deployment K=0 must use training patient prototypes only.

C14 honest K=0 comparison: Three bars per condition (A=thalamic-only, D=three-source). Oracle (blue): prior reported K=0 — uses test patient's own labels to build the PGES prototype, a circular oracle. Train-prior (orange): true deployment — prototype built from 7 other patients' labeled data, the only protocol usable on Day 1. Bio-prior (green): canonical PGES feature vector passed through the encoder as a hand-designed prototype. The 18pp gap between oracle and train shows how much prior work overstated K=0 performance. Train and bio are statistically identical (p=1.000).

Feature Importance — All 17 Features

Each of the 17 features was ablated (zeroed out) independently across all LOSO folds. The mean F1 drop when a feature is removed measures its contribution. Features are explained below in terms of what they capture in thalamic LFP during PGES.

Feature importance (ablation study): Each feature is zeroed out and the mean F1 drop measured across all LOSO folds. Approx Entropy tops the ranking (drop=0.0268) — PGES is a state of high temporal regularity (low ApEn), opposite to the irregular baseline. Shannon Entropy and RMS are next: both capture the amplitude/regularity shift. Gamma Power (80–150 Hz) ranks 15th but is non-negative, validating its relevance for thalamic DBS. The top-5 features alone explain most of the model's discriminative power.

Learning Curve & Data Efficiency

Learning curve: F1 (K=10) as the number of training patients increases from 2 to 14. The model already achieves F1=0.870 with just 2 training patients and stays roughly flat through 14. This flat curve is the key clinical feasibility argument: a hospital starting with 2–4 DBS patients can deploy DACTRL immediately and expect the same performance they would get from a large multi-center cohort. No need to wait for years of data collection.

What Did Not Work — Negative Results

Honest documentation of failures is a thesis contribution in its own right.

Nine Thesis Contributions

Each contribution is a standalone, publishable finding. Together they form a complete clinical and methodological framework for thalamic PGES detection.

Final Conclusions