| Title |
Dirbtiniu intelektu grįstas kognityvinių funkcijų sutrikimo prognozavimas širdies chirurgijos metu |
| Translation of Title |
Prediction of cognitive impairment in cardiac surgery using artificial intelligence. |
| Authors |
Krevnevičiūtė, Justina |
| Full Text |
|
| Pages |
57 |
| Keywords [eng] |
biological signals ; classification ; signal preprocessing ; postoperative cognitive impairment |
| Abstract [eng] |
The master's thesis presents methods for classifying physiological time series data – arterial blood pressure (ABP) and transcranial Doppler (TCD) signals. Signal processing methods (noise removal, segmentation, feature engineering) are reviewed. The most significant ABP and TCD features are identified in predicting postoperative cognitive impairment. During the study, various artificial intelligence models are implemented and compared - from statistical to hybrid and transformer architectures, using spectrogram and recurrence matrices and feature vectors. The accuracy, fit and F1 score of the models are compared. The implemented models and data preparation methods showed that for a small dataset consisting of biological signals of different lengths, the best results were achieved using machine learning models with feature vector input. |
| Dissertation Institution |
Kauno technologijos universitetas. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2025 |
