Sleep Disorders Prediction from Biosignals and Clinical Data
Key Expertise
Experience
8+ years
Timezone
CET (UTC +1)
Skills
AI / ML
Languages
Databases
Infrastructure
Frameworks
Overview
The project involved leading a scientific data science initiative focused on early detection of sleep disorders using real-time biosignals and clinical data. The solution was designed to support improved diagnostic insights by combining advanced healthcare analytics, signal processing, and machine learning with clinically relevant interpretation. The work integrated multiple biomedical data sources to identify meaningful patterns associated with sleep disorders.
Achievements
Led an end-to-end research initiative in sleep disorder detection using biosignals and clinical inputs from scratch. Improved analytical depth through integration of multiple physiological data streams, including PPG, EEG, ECG, biomarkers, and EHR data. Delivered scientific analyses, visualizations, and stakeholder-facing outputs to support research and clinical collaboration. Independently planning, executing and managing research activities, identifying key insights and resolving data discrepancies.
Responsibilities
- Planned and executed and led advanced scientific biosignal and clinical data research for early detection of sleep disorders.
- Analyzed real-time physiological and clinical data using time series methods, signal processing, and anomaly detection.
- Analysing and interpreting EHR data to support clinical hypotheses and diagnostic models.
- Conducted advanced healthcare data analytics
- Worked with PPG, EEG, ECG, biomarkers, vital signs, and electronic health records.
- Interpreted analytical findings to support clinical hypotheses and model development.
- Created high-quality visualizations and scientific reports for research and stakeholder communication.
Technologies Used
Key Expertise
Experience
8+ years
Timezone
CET (UTC +1)
Skills
AI / ML
Languages
Databases
Infrastructure
Frameworks
This project was delivered by
Oleksandra K.
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