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Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

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posted on 24.05.2021, 18:55 by Hedieh Montazeri
In this thesis, we propose and implement a new hybrid approach using fractal analysis, statistical analysis and neural network computation to build a model for prediction the number of ischemia occurrence based on ECG recordings. The main advantage of the proposed approach over similar earlier related works is that first useful parameters from fractal analysis of the signal are extracted to build a model that includes both clinical characteristics and signal attributes. Statistical analysis such as binary logistic regression and multivariate linear regression are then used to further explore the relation of parameters in order to obtain a more accurate model. We show that the results compare well with those of earlier work and clearly indicate that the augmentation of the above mentioned approaches improves the prediction accuracy.





Master of Spatial Analysis


Computer Science

Granting Institution

Ryerson University

LAC Thesis Type