BioSerenity-E1: a self-supervised EEG model for medical applications

• URL: http://arxiv.org/abs/2503.10362v1
• Date: Thu, 13 Mar 2025 13:42:46 GMT
• Title: BioSerenity-E1: a self-supervised EEG model for medical applications
• Authors: Ruggero G. Bettinardi, Mohamed Rahmouni, Ulysse Gimenez,
• Abstract summary: BioSerenity-E1 is a family of self-supervised foundation models for clinical EEG applications.<n>It combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive process that demands highly specialized expertise, which remains relatively scarce and not consistently accessible. To address these limitations, the implementation of automated pre-screening and analysis systems for EEG data holds considerable promise. Advances in self-supervised learning made it possible to pre-train complex deep learning architectures on large volumes of unlabeled EEG data to learn generalizable representations, that can later be used to enhance performance on multiple tasks while needing less downstream data. In the present paper, we introduce BioSerenity-E1, the first of a family of self-supervised foundation models for clinical EEG applications that combines spectral tokenization with masked prediction to achieve state-of-the-art performance across relevant diagnostic tasks. The two-phase self-supervised pretraining framework initially acquires compressed EEG representations via a transformer-based VQ-VAE architecture designed to reconstruct log-multitaper spectral projections, then implements extensive (70% block) masked token prediction to force the model to learn complex spatiotemporal dependencies in EEG signals. BioSerenity-E1 achieves strong performance across three clinical tasks, either in line or above state-of-the-art methods: seizure detection (AUROC = 0.926, Sensitivity = 0.909), normal/abnormal classification (AUPRC = 0.970 on proprietary data; 0.910 on TUH-Abnormal), and multiclass pathology differentiation on unbalanced data (Weighted F1 = 0.730). The utility of BioSerenity-E1 is further confirmed in low-data regimes scenarios, showing clear improvements in AUPRC (from +2% to 17%) when trained on less than 10% of the available data.

Related papers

• rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics [0.56337958460022]
  This study uses MIT-BIH Arrhythmia dataset to evaluate the efficiency of rECGnition_v2.0 for various classes of arrhythmias.<n>The compact architectural footprint of the rECGnition_v2.0, characterized by its lesser trainable parameters, unfurled several advantages including interpretability and scalability.
  arXiv  Detail & Related papers  (2025-02-22T15:16:46Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring.<n>Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors.<n>In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type.<n>Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model [19.91895489891802]
  We present FEMBA (Foundational EEG Mamba + Bidirectional Architecture), a novel self-supervised framework for EEG analysis.<n>Unlike Transformer-based models, which incur quadratic time and memory complexity, FEMBA scales linearly with sequence length.<n>It achieves competitive performance in comparison with transformer models, with significantly lower computational cost.
  arXiv  Detail & Related papers  (2025-02-10T13:15:52Z)
• CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention [53.539020807256904]
  We introduce a Compact for Representations of Brain Oscillations using alternating attention (CEReBrO)<n>Our tokenization scheme represents EEG signals at a per-channel patch.<n>We propose an alternating attention mechanism that jointly models intra-channel temporal dynamics and inter-channel spatial correlations, achieving 2x speed improvement with 6x less memory required compared to standard self-attention.
  arXiv  Detail & Related papers  (2025-01-18T21:44:38Z)
• Enhancing Glucose Level Prediction of ICU Patients through Hierarchical Modeling of Irregular Time-Series [4.101915841246237]
  Multi-source Irregular Time-Series Transformer (MITST) designed to predict blood glucose (BG) levels in ICU patients.<n>MITST employs hierarchical architecture of Transformers, comprising feature-level, timestamp, and source-level components.
  arXiv  Detail & Related papers  (2024-11-03T03:03:11Z)
• Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance [0.0]
  We create a large unsupervised pre-training dataset by combining ten public ECG databases. We pre-train Vision Transformers using JEPA on this dataset and fine-tune them on various PTB-XL benchmarks.
  arXiv  Detail & Related papers  (2024-10-02T08:25:57Z)
• BISeizuRe: BERT-Inspired Seizure Data Representation to Improve Epilepsy Monitoring [13.35453284825286]
  This study presents a novel approach for EEG-based seizure detection leveraging a BERT-based model. The model, BENDR, undergoes a two-phase training process, pre-training and fine-tuning. The optimized model demonstrates substantial performance enhancements, achieving as low as 0.23 FP/h, 2.5$times$ lower than the baseline model, with a lower but still acceptable sensitivity rate.
  arXiv  Detail & Related papers  (2024-06-27T14:09:10Z)
• Improving Clinical Decision Support through Interpretable Machine Learning and Error Handling in Electronic Health Records [6.594072648536156]
  Trust-MAPS translates clinical domain knowledge into high-dimensional, mixed-integer programming models. Trust-scores emerge as clinically meaningful features that not only boost predictive performance for clinical decision support tasks, but also lend interpretability to ML models.
  arXiv  Detail & Related papers  (2023-08-21T15:14:49Z)
• Robust and Efficient Medical Imaging with Self-Supervision [80.62711706785834]
  We present REMEDIS, a unified representation learning strategy to improve robustness and data-efficiency of medical imaging AI. We study a diverse range of medical imaging tasks and simulate three realistic application scenarios using retrospective data.
  arXiv  Detail & Related papers  (2022-05-19T17:34:18Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.