Ekaterina Kutafina
Dr. Dr. rer. medic.
Biosignale
Biosignale wie EKG, EEG oder Sensordaten von Wearables bieten einen einzigartigen Einblick in die menschliche Physiologie und Gesundheit. Wir entwickeln Methoden zur intelligenten Verarbeitung und Interpretation dieser Signale, wobei wir Signalverarbeitung, maschinelles Lernen und multimodale Integration kombinieren. Unser Ziel ist es, die Diagnose und Überwachung zu verbessern, personalisierte Interventionen zu ermöglichen und neue Wege zum Verständnis komplexer physiologischer Prozesse zu eröffnen – immer mit dem Fokus auf der klinischen Anwendbarkeit.
Tools & Services
Publikationen
Supervised spike sorting feasibility of noisy single-electrode extracellular recordings: Systematic study of human C-nociceptors recorded via microneurography
Sorting spikes from noisy single-channel in-vivo extracellular recordings is challenging, particularly due to the lack of ground truth data. Microneurography, an electrophysiological technique for studying peripheral sensory systems, employs experimental protocols that time-lock a subset of spikes. Stable propagation speed of nerve signals enables reliable sorting of these spikes. Leveraging this property, we established ground truth labels for data collected in two European laboratories and designed a proof-of-concept open-source pipeline to process data across diverse hardware and software systems. Using the labels derived from the time-locked spikes, we employed a supervised approach instead of the unsupervised methods typically used in spike sorting. We evaluated multiple low-dimensional representations of spikes and found that raw signal features outperformed more complex approaches, which are effective in brain recordings. However, the choice of the optimal features remained dataset-specific, influenced by the similarity of average spike shapes and the number of fibers contributing to the signal. Based on our findings, we recommend tailoring lightweight algorithms to individual recordings and assessing the “sortability feasibility” based on achieved accuracy and the research question before proceeding with sorting of non-time-locked spikes in future projects.
Spectral changes in electroencephalography linked to neuroactive medications: A computational pipeline for data mining and analysis
Harmonizing Microneurography Metadata with Local Data Hubs: A Concept
This work aims to improve FAIR-ness of the microneurography research by integrating the local (meta)data to existing research data infrastructures. In the previous work, we developed an odML based solution for local metadata storage of microneurography data. However, this solution is limited to a narrow community. As a next step, we propose the integration into the Local Data Hubs, data-sharing services within NFDI4Health infrastructure. We outline a first concept, that streams chosen data from the established odMLtables GUI.
Abschlussarbeiten
Leveraging data integration architectures for patient care: case of multimodal sensor data
Integrating Computational Biosignal Analytics into Data-Driven Multimodal Approaches in Modern Cristical Care in Cardiac Surgery
Kurse
Einführung in die computergestützte medizinische Signal Analyse
Bachelor Studium
–
Klinik
–
Kliniker
–
Promotionsstudium
–
Master Studium
–
PostDoc
–
Vorklinik
–
WiSe
Der menschliche Körper sendet kontinuierlich Biosignale aus, die wertvolle Einblicke in physiologische Prozesse liefern. In der Medizin werden diese Signale sowohl zu Forschungszwecken genutzt als auch um Diagnose und Monitoring von Krankheiten und Patienten zu unterstützen. Diese Veranstaltung bietet eine praxisnahe Einführung in die computergestützte Biosignalanalyse. Nach einer kurzen theoretischen Einführung zu den Grundlagen der Signalverarbeitung, einschließlich Definition, Erfassung und Anwendungsmöglichkeiten, erfolgt eine praktische Einführung in die Datenanalyse. Anhand eines realistischen Beispiels aus dem Patientenmonitoring im Intensivmedizin-Setting werden essentielle Schritte vermittelt: Daten-Vorbereitung, Feature-Engineering und die Vorhersage des Signals mit modernen Machine Learning Methoden durchgeführt. Es wird von Teilnehmenden der Besuch der vorangegangenen Veranstaltung ¿Coding Basics¿ oder ein äquivalentes Vorwissen in der Programmierung in Python vorausgesetzt.
In KLIPS anzeigenMedical AI - From basics to pro: Heart Rate Variability & AI symbiosis in personalized medicine
Klinik
–
Kliniker
–
Promotionsstudium
–
PostDoc
–
Vorklinik
–
WiSe + SoSe
[Diese Lehrveranstaltung wird nur auf Englisch angeboten] Heart rate variability (HRV) is widely used in clinical settings as a non-invasive autonomic nervous system function marker. It helps assess cardiovascular health, stress levels, and overall well-being. Clinicians use HRV to monitor conditions like heart disease, hypertension, and diabetes, as well as to evaluate recovery in post-surgical and critically ill patients. HRV also plays a role in mental health, aiding in the diagnosis and management of anxiety, depression, or PTSD. Additionally, it is used in sports medicine and rehabilitation to track recovery and optimize training. Its broad applications make it a valuable tool in personalized medicine. The development of AI methods allows to make more complex predictions using multiple HRV parameters simultaneously. This complexity enabled successful decision support in the domains where HRV was not previously prominent for clinical use, such as epileptology. In this lecture block, we will discuss the technical aspects of HRV assessment, such as different sensors, data quality control or different HRV measures. We will review various types of clinical applications, but also the ¿citizen science¿ approach and sports coaching. For the practical part we take a dataset with precomputed R-to-R intervals and different labels (e.g. RR Interval Time Series Modeling: The PhysioNet/Computing in Cardiology Challenge 2002 v1.0.0 ). We will test different machine learning approaches to classify the data, e.g. to detect whether the data was recorded in a stressed or relaxed phase. This block lecture does not require any previous coding experience, we will use the graphical low-code platform KNIME. Students are required to bring their own laptop.
In KLIPS anzeigen