arXiv subjects

Nicolas Weeger

Publications and source records attributed to Nicolas Weeger.

5 recordsLinked to original sources

arXiv2026

Comparison of Dimension Reduction Methods for EEG Seizure Detection Using Autonomous AI-Driven Optimization

Automated epileptic seizure detection from multichannel electroencephalography (EEG) benefits from dimension reduction to obtain compact, discriminative representations. We compare four signal-space dimension reduction methods, Principal Component Analysis (PCA), Dynamical Component Analysis (DyCA), Dynamic Mode Decomposition (DMD), and Average Volatility Dimensioning (AVD), for deep learning-based seizure detection on the Temple University Hospital Seizure Corpus (TUSZ v2.0.3). To enable a comparison of optimal combinations of representation and classifier, an autonomous AI-driven research framework independently optimizes architecture and hyperparameters for each representation. Measured by test ROC-AUC, the variance-based methods AVD (88.28%) and PCA (85.98%) paired with their respective optimal classifiers outperform the dynamics-based methods DMD (74.56%) and DyCA (74.85%) by over 10%, with AVD also showing the smallest validation-to-test gap. The best-performing classifier architecture differs across representations, indicating that representation and classifier should be optimized jointly. Our results highlight the importance of the input representation for EEG seizure detection and indicate the viability of autonomous AI-driven experimentation in biomedical signal processing.

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arXiv2026

Low-Density EEG for Seizure Detection: Evaluating CNN-RNN Architectures on a Behind-the-Ear Montage Setup

Epilepsy affects over 50 million individuals globally, underscoring the need for automated seizure detection systems that can alleviate clinicians workload and enhance the accuracy of patient seizure diaries. In wearable EEG applications, however, reliable detection remains challenging due to the limited spatial resolution of low-density electrode configurations, reduced signal-to-noise ratios, and the scarcity of diverse, publicly available training datasets. This study investigates the efficacy of hybrid deep learning architectures for automated seizure detection using a simulated behind-the-ear montage derived from the Temple University Seizure Corpus (TUSZ, v2.0.3). We conduct a systematic comparison of several CNN-RNN models, including LSTM- and GRU-based variants, across multiple EEG montages to evaluate their capacity to compensate for the loss of spatial information inherent to reduced electrode configurations. The proposed CNN-Merged model, which integrates temporal and spectral feature representations, demonstrates superior performance, achieving a ROC AUC of 85.89% and a balanced accuracy of 79.11% on the held-out test set. Furthermore, the model exhibits strong robustness across different reference montages, effectively bridging the performance gap between conventional full-scalp recordings and resource-constrained wearable systems. These findings substantiate the potential of hybrid deep learning models as a promising avenue toward robust, patient-independent seizure detection in low-density EEG applications.

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arXiv2026

AI Engineering Blueprint for On-Premises Retrieval-Augmented Generation Systems

Retrieval-augmented generation (RAG) systems are gaining traction in enterprise settings, yet stringent data protection regulations prevent many organizations from using cloud-based services, necessitating on-premises deployments. While existing blueprints and reference architectures focus on cloud deployments and lack enterprise-grade components, comprehensive on-premises implementation frameworks remain scarce. This paper aims to address this gap by presenting a comprehensive AI engineering blueprint for scalable on-premises enterprise RAG solutions. It is designed to address common challenges and streamline the integration of RAG into existing enterprise infrastructure. The blueprint provides: (1) an end-to-end reference architecture described using the 4+1 view model, (2) a reference application for on-premises deployment, and (3) best practices for tooling, development, and CI/CD pipelines, all publicly available on GitHub. Ongoing case studies and expert interviews with industry partners will assess its practical benefits.

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arXiv2025

Towards Automated EEG-Based Epilepsy Detection Using Deep Convolutional Autoencoders

Epilepsy is one of the most common neurological disorders. This disease requires reliable and efficient seizure detection methods. Electroencephalography (EEG) is the gold standard for seizure monitoring, but its manual analysis is a time-consuming task that requires expert knowledge. In addition, there are no well-defined features that allow fully automated analysis. Existing deep learning-based approaches struggle to achieve high sensitivity while maintaining a low false alarm rate per hour (FAR/h) and lack consistency in the optimal EEG input representation, whether in the time or frequency domain. To address these issues, we propose a Deep Convolutional Autoencoder (DCAE) to extract low-dimensional latent representations that preserve essential EEG signal features. The ability of the model to preserve relevant information was evaluated by comparing reconstruction errors based on both time series and frequency-domain representations. Several autoencoders with different loss functions based on time and frequency were trained and evaluated to determine their effectiveness in reconstructing EEG features. Our results show that the DCAE model taking both time series and frequency losses into account achieved the best reconstruction performance. This indicates that Deep Neural Networks with a single representation might not preserve the relevant signal properties. This work provides insight into how deep learning models process EEG data and examines whether frequency information is captured when time series signals are used as input.

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arXiv2025

Towards practicable Machine Learning development using AI Engineering Blueprints

The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers.

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