WP1 (Work Package 1): Project management.
Work package leader: Assoc. Prof. Dr. G. Georgieva-Tsaneva.
The activities under this work package will monitor the timely achievement of the goals and the quality of the results throughout the project.
WP2: Research activities related to the study of the fundamental characteristics of HRV and the development of a methodological framework
Work package leader: Assist. Prof. Dr. Krasimir Cheshmedzhiev
- 2.1 Research activities related to the use of classical and innovative methods for the analysis of HRV and HRT.
- 2.2 Research of methods and algorithms for pre-processing data.
- 2.3 Complex analysis of HRV methods.
- 2.4 Analysis of the studied methods, reduction and optimization of indicators for the purpose of optimal study of HRV in various physiological and pre-risk conditions.
- 2.5 Development of a methodological framework for effective study of HRV.
WP3: Experimental study of hybrid algorithms for generating synthetic ECG/PPG/HRV data.
Work package leader: Assoc. Prof. Dr. Evgeniya Gospodinova
- 3.1 Creation and study of methods and algorithms for generating synthetic cardiological data by applying classical stochastic methods.
- 3.2 Generation, study and experimentation with synthetic cardiological data by applying generative adversarial networks.
- 3.3 Development and testing of a new hybrid approach for simulating cardiological data, combining stochastic modeling and generative adversarial networks.
- 3.4 Comparative analysis and verification of the generated cardiological data in terms of accuracy and speed.
WP4: Study of the dynamics of cardiological time series by creating new hybrid AI-based algorithms for analysis and prediction.
Work package leader: Assoc. Prof. Dr. Galya Georgieva-Tsaneva
- 4.1 Study and critical analysis of modern AI architectures for time series – RNN, LSTM, GRU, CNN and Transformers – and analysis of their capabilities for analysis and prediction of cardiological series.
- 4.2 Study of the short-term dynamics of ECG, PPG and HRV by applying and comparing hybrid neural models to classical statistical, fractal and entropy methods.
- 4.3 Development of AI-based algorithms for predicting short-term and long-term changes in HRV and detecting deviations associated with stress, fatigue and increased risk.
- 4.4 Systematic analysis and search for new dependencies between HRV parameters and different physiological states (rest, cognitive load, stress, pre-risk).
- 4.5 Validation of the effectiveness of the developed algorithms through experiments on real and synthetic data (generated in WP3) using standardized evaluation metrics.
WP5: Study of HRV in different psychophysiological states and validation of AI algorithms by creating VR stimuli.
Work package leader: Assist. Prof. Dr. Penio Lebamovski
- 5.1 Conducting experiments to study the dynamics of heart rate in different states: rest, relaxation, cognitive load and stress (caused by physical/mental load, stressful situations, etc.).
- 5.2 Study of HRV by creating a multimodal interface for sensory control, aimed at analyzing differences in autonomic nervous regulation when using gestures, eye tracking, head movements and a joystick.
- 5.3 Validation of the developed AI algorithms (from WP2–WP4) in real experimental conditions by comparing their effectiveness in distinguishing induced states. Study of individual differences in physiological reactions to VR induced states.
- 5.4 Assessment of the reliability and applicability of VR experiments as a scientific tool.
- 5.5 Research and assessment of the body's resistance to stress and its ability to recover through HRV dynamics.
- 5.6 Analysis of the scientific and applied potential of the VR/MR environment for generating controlled stimuli.
WP6: Development and research of the concept of a personalized signal-driven AI- and IoT-based digital twin.
Work package leader: Assoc. Prof. Dr. Galya Georgieva-Tsaneva
- 6.1 Research and analysis of the methodological framework from WP2 with the aim of integrating it as a basic tool for defining a minimum informative set of HRV parameters in the digital twin.
- 6.2 Evaluation and validation of hybrid methods for generating synthetic cardio data from WP3, by comparing their statistical and physiological reliability with real records.
- 6.3 Integration and validation of AI-based algorithms from WP4 as the analytical core of the digital twin.
- 6.4 Integration of the digital twin in an IoT environment and validation through VR experiments.
- 6.5 Integration and experiments with the personalized signal-driven digital twin.
- 6.6 Integration and study of real clinically labeled data in the digital twin.
WP7: Dissemination, promotion and multiplication of the results of the scientific project.
Work package leader: Assoc. Prof. Dr. Galya Georgieva-Tsaneva
- 7.1 Development, periodic updating and maintenance of the project website.
- 7.2 Preparation for publications and promotion of the results.
- 7.3 Participation of the project team in scientific seminars, in order to promote the results obtained during the implementation of the scientific project.
- 7.4 Preparation, creation and publication of a monograph on the project.
- 7.5 Printing of two types of brochures and printed information materials in various public media.
- 7.6 Approaches to creating educational content in order to promote the results of the project among teachers and students of Medicine and Health Care.
- 7.7 Preparation of recommendations for the application of the project results in healthcare and prevention.
