AI Research Group
A multidisciplinary R&D unit that aims to analyze physiological signals from the human body (ECG, EEG, EMG, respiration, blood pressure, oxygen saturation, etc.) using artificial intelligence methods and integrate them into clinical decision support systems.
Biomedical signals can be thought of as a digital language that reflects the instantaneous state of our body. Physiological signals such as heartbeats, brain waves, muscle activity, and breathing rhythm provide continuous information flow about our health status.
Artificial intelligence technologies can understand these complex signal data and detect deviations from normal patterns. This process is like an experienced musician instantly noticing a wrong-playing instrument in an orchestra.
The main goal of our group is to develop continuous patient monitoring, early warning systems, and signal-based disease prediction models using time series analysis, signal processing, and machine learning techniques.
Main application areas where we develop AI-supported solutions in the field of biomedical signal processing
Heart rhythm analysis and arrhythmia detection algorithms
Early warning systems through EEG analysis
Respiratory failure and apnea detection algorithms
Hypertensive crisis prediction and change analysis
Hypoxia alerts and oxygen deficiency detection
Muscle activity and fatigue analysis
AI-supported signal analysis systems used and tested in real clinical environments
Analyzes continuous ECG signals to detect atrial fibrillation, ventricular tachycardia, and other arrhythmias in real-time and provides emergency intervention alerts.
Analyzes preictal changes in EEG signals to predict epileptic seizures an average of 23 minutes in advance with 89% sensitivity.
Integrates respiratory rate, tidal volume, and oxygen saturation data to predict respiratory failure development 2 hours in advance.
Simultaneously analyzes heart rate, blood pressure, respiratory rate, and body temperature signals to detect critical changes.
With our multidisciplinary approach, we collaborate with experts from health and engineering fields
Main research topics we focus on for the development of artificial intelligence technologies in the field of biomedical signal processing
Understanding time-dependent changes in physiological signals and predicting future states is critically important. It helps predict disease progression by detecting periodic changes, trend analysis, and seasonal fluctuations in signals.
Advanced filtering algorithms to improve the quality of raw biomedical signals and extract clinically meaningful information. Systems that minimize electrical noise, motion artifacts, and other disruptive factors.
Systems that automatically detect deviations from normal physiological signals. Algorithms that learn each patient's unique signal patterns and perform personalized anomaly detection accordingly.
Combining information from different physiological parameters for more comprehensive health status assessment. A holistic approach is achieved through integration of ECG, respiratory signals, and blood pressure data.
Classifying disease subtypes and patient groups according to physiological signal characteristics. Used in personalized medicine applications to predict disease progression and anticipate treatment response.
Since physiological signals are sensitive personal data, secure processing, storage, and sharing protocols are essential. Designing systems that provide clinical benefit while protecting patient privacy.
Other specialized signal analysis systems and application areas we have developed
Analyzes EEG and EMG signals to continuously calculate surgical anesthesia depth and recommends optimal dosage.
Detects obstructive sleep apnea episodes through respiratory signal and oxygen saturation analysis.
Objectively measures muscle fatigue by analyzing frequency spectrum changes in EMG signals.
Concrete achievements and clinical impacts our Research Group has achieved in the field of biomedical signal processing
Special patient groups and application environments where biomedical signal processing technologies are used
Analyzes heart rate, respiration, and oxygen saturation signals in premature babies to detect bradycardia and apnea episodes in advance.
Remote patient monitoring systems with continuous vital sign tracking and early warning systems in home care environments.
Fall risk assessment, activity analysis, and chronic disease progression monitoring systems in elderly patients.
Would you like to collaborate with us to develop artificial intelligence applications in the field of biomedical signal processing?
System development with real signal data in hospitals and intensive care units
Research projects in signal processing and machine learning fields
Signal analysis algorithm integration with medical device manufacturers