EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection

• URL: http://arxiv.org/abs/2607.04558v1
• Date: Mon, 06 Jul 2026 00:19:41 GMT
• Title: EEG-SpikeAgent: Agentic Closed-Loop Program Synthesis for Automated EEG Spike Detection
• Abstract summary: We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG.<n>Across iterations, EEG-SpikeAgent proposes and refines candidate signal features and decision rules informed by model performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automated detection of interictal epileptiform discharges in scalp electroencephalography (EEG) is clinically important, but recent high-performing deep-learning models often trade interpretability for accuracy. We introduce EEG-SpikeAgent, a closed-loop program-synthesis framework that uses a large language model (LLM) agentic system to generate signal-processing features for spike detection in scalp EEG. The system iteratively proposes one deterministic EEG feature module at a time, executes the resulting code on EEG to generate tabular features, evaluates performance via a tabular classifier, summarizes run-level metrics, and feeds structured diagnostics back to the model for refinement. Across iterations, EEG-SpikeAgent proposes and refines candidate signal features and decision rules informed by model performance. We evaluated EEG-SpikeAgent on VEPISET, a public 29-channel dataset of 4-second epochs containing 2,516 discharge-containing and 22,933 non-discharge epochs. Across five-fold cross-validation with a gradient-boosted tree classifier, agent-generated features achieved an area under the receiver operating characteristic curve of 0.935, balanced accuracy of 0.699, F1 score of 0.557, sensitivity of 0.401, and specificity of 0.996 at the default operating point. At an operating point with sensitivity 0.80, mean precision was 0.470 and mean specificity was 0.900. Artifact-aware feature generation improved balanced accuracy and F1 score over spike-only feature search. These results indicate that LLM-based program synthesis can automate EEG feature engineering in auditable and inspectable code-driven manner for clinical and methodological review.

Related papers

• Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder [0.3262230127283452]
  This study presents an offline anomaly-detection framework for EHA sensor signals, focusing on temperature and pressure data collected from a controlled test bench.<n>The method employs a reconstruction-based Long Short-Term Memory (LSTM) autoencoder, calibrated and evaluated using validation-set reconstruction-error distributions.<n>The LSTM autoencoder achieved an average accuracy of 99.0%, precision up to 100%, recall between 90.2% and 99.6%, and F1-scores from 93.1% to 99.8%, demonstrating high detection sensitivity and a very low false-
  arXiv  Detail & Related papers  (2026-06-03T17:37:21Z)
• Organ-Agents: Virtual Human Physiology Simulator via LLMs [66.40796430669158]
  Organ-Agents is a multi-agent framework that simulates human physiology via LLM-driven agents.<n>We curated data from 7,134 sepsis patients and 7,895 controls, generating high-resolution trajectories across 9 systems and 125 variables.<n>Organ-Agents achieved high simulation accuracy on 4,509 held-out patients, with per-system MSEs 0.16 and robustness across SOFA-based severity strata.
  arXiv  Detail & Related papers  (2025-08-20T01:58:45Z)
• PySeizure: A single machine learning classifier framework to detect seizures in diverse datasets [0.0]
  We introduce an innovative, open-source machine-learning framework that enables robust seizure detection across varied clinical datasets.<n>To enhance robustness, the framework incorporates an automated pre-processing pipeline to standardise data and a majority voting mechanism.<n>We train, tune, and evaluate models within each dataset, assessing their cross-dataset transferability.
  arXiv  Detail & Related papers  (2025-08-10T09:12:29Z)
• BioSerenity-E1: a self-supervised EEG model for medical applications [0.0]
  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.
  arXiv  Detail & Related papers  (2025-03-13T13:42:46Z)
• CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality Detection [0.4448543797168715]
  CwA-T is a novel framework that combines a channelwise CNN-based autoencoder with a single-head transformer classifier for efficient EEG abnormality detection.<n> evaluated on the TUH Abnormal EEG Corpus, the proposed model achieves 85.0% accuracy, 76.2% sensitivity, and 91.2% specificity at the per-case level.<n>The framework retains interpretability through its channelwise design, demonstrating great potential for future applications in neuroscience research and clinical practice.
  arXiv  Detail & Related papers  (2024-12-19T04:38:34Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection [49.196182908826565]
  Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input.
  arXiv  Detail & Related papers  (2023-09-07T13:43:46Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• 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)
• Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble [64.29529357862955]
  We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
  arXiv  Detail & Related papers  (2020-10-21T18:11:36Z)
• Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network [0.19686770963118383]
  The study presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from single-channel electrocardiogram (ECG) signals. The model is constructed using convolutional, max pooling layers and a fully connected Multilayer Perceptron (MLP) consisting of a hidden layer and SoftMax output for classification. This demonstrates the model can identify the presence of Apnoea with a high degree of accuracy.
  arXiv  Detail & Related papers  (2020-02-03T15:47:00Z)

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.