# Multi-label neonatal EEG segmentation enriched with semi-synthetic data

Vivien Kraus, Guillaume Dollé, Alexandra Givernaud, Jonathan Beck, Nathalie Bednarek, G. Loron, François Rousseau, Nicolas Passat

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Vivien Kraus, Guillaume Dollé, Alexandra Givernaud, Jonathan Beck, Nathalie Bednarek, et al. Multi-label neonatal EEG segmentation enriched with semi-synthetic data. Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM), Mar 2025, Nice, France. hal-04896982v2

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Submitted on 19 Mar 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Multi-label neonatal EEG segmentation enriched with semi-synthetic data

V. Kraus¹, G. Dollé², A. Givernaud³, J. Beck³, N. Bednarek¹,³, G. Loron¹,³, F. Rousseau⁴, N. Passat¹

¹ Université de Reims Champagne Ardenne, CRESTIC, Reims, France
² Université de Reims Champagne Ardenne, CNRS, LMR, UMR 9008, Reims, France
³ Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France
⁴ IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France

## Neonatal EEG reading and interpretation

* ElectroEncephaloGraphy (EEG) assesses severity of Hypoxic-Ischemic Encephalopathy, a **frequent** (1/1000 births) and **deadly** (up to 20%) condition affecting **neonates**.
* Neonatal EEG is difficult to interpret: **experts required**.
* Required **immediately** (first 6 hours of life for treatment).
* Required **constantly** (continuous monitoring under treatment, 72h).
* To interpret EEG: **semi-automatic tools required!**

<figure><img src="image_2.png" /><figcaption>Figure 1. Neonates (patients) wear electrodes on their head to record **electrical activity**. The signal is then interpreted.</figcaption></figure>

## Semi-synthetic data

Multi-centric Lytonepal cohort includes EEG records. Annotate Front Sharp Transients (FSP), Anterior Slow Dysrythmia (ASD), Spikes: difficult task, **sparse, insufficient data**. Cut 10-second windows, filter 0.3s, < 30 Hz.

The model must learn the most **basic facts** about how **humans** interpret neonatal EEG:

* The **EEG montage** is significant: activity is different in different parts of the brain.
* Activity recognition should primarily be based on **amplitude, frequency** ($δ, θ, α, β, γ$), **location, duration**.

Semi-synthetic data approach: pick the most precisely described figures from literature, generate waves, add them to the signal.

<figure><img src="image_3.png" /><figcaption>Figure 2. Front Sharp Transients are observed on **frontal zones, not always both sides** (i.e. left/right) at once.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 3. Instances of synthetic figures</figcaption></figure>

**Multi-label:** these 5 synthetic figure types are kept **separate** from the 3 real ones, but the network is free to correlate synthetic labels with real labels.

## EEG segmentation

**Simultaneously** learn to recognize real annotations and synthetic ones. Loss **ignores** parts of the signal with no real annotations.

<figure><img src="image_5.png" /><figcaption>Figure 4. Simple CNN-based U-Net for simultaneous learning of 5 synthetic and 3 real labels.</figcaption></figure>

## Experimental results

* Find best **learning rate**: maximize the F-score (Dice) for worst **synthetic** label (ignoring real labels).
* Too many synthetic annotations **overlap** real figures, **distract** the model. **Reduce** from maximum (around 1 per window) to 0.

<figure><img src="image_6.png" /><figcaption>Figure 5. Varying the synthetic data frequency</figcaption></figure>

* ASD: inconclusive (too few data points).
* FSP: better with few instances, difficult (complex shape).
* Spike: more help, until it floods the signal.

## Conclusion & future works

* Introducing semi-synthetic data helps recognize figures for EEG interpretation.
* Make the figures more physiological. Check output correlations. Improve overall performance. More real data.

**Reference for EEG reading and interpretation:** Lamblin et al, "Électroencéphalographie du nouveau-né prématuré et à terme. Aspects maturatifs et glossaire", *Clinical Neurophysiology*, 29:123-219, 1999

Work supported by the ANR (grants ANR-19-CHIA-0015, ANR-22-CE45-0034) and by the American Memorial Hospital Foundation (AMHF).