| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 534.31 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Atrial fibrillation (AF) is the most common cardiac anomaly and one that potentially threatens human life. Due to its relation to a
variation in cardiac rhythm during indeterminate periods, long-term observations are necessary for its diagnosis. With the increase
in data volume, fatigue and the complexity of long-term features make analysis an increasingly impractical process. Most medical
diagnostic aid systems based on machine learning, are designed to automatically detect, classify or predict certain behaviors. In
this work, using the PhysioNet MIT-BIH Atrial Fibrillation database, a system based on MLP artificial neural network is proposed
to differentiate, between AF and non-AF, segments and ECG’s features, obtaining average accuracy of 80.67% in test set, for the
10-fold cross-validation method. As a highlight, the extraction of jitter and shimmer parameters from ECG windows is presented
to compose the network input sets, indicating a slight improvement in the model's performance. Added to these, Shannon's and
logarithmic energy entropies are determined, also indicating an improvement in performance related to the use of fewer features.
Description
Keywords
Atrial fibrillation Logarithm energy Shannon entropy Jitter of ECG Shimmer of ECG
Citation
Borghi, Pedro Henrique; Borges, Renata Coelho; Teixeira, João Paulo (2021). Atrial fibrillation classification based on MLP networks by extracting Jitter and Shimmer parameters. In International Conference on ENTERprise Information Systems (CENTERIS), International Conference on Project MANagement (ProjMAN), International Conference on Health and Social Care Information Systems and Technologies (HCist). Procedia Computer Science. ISSN 1877-0509. p. 931-939