Banca de QUALIFICAÇÃO: RAFAEL MONTEIRO LARANJEIRA

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : RAFAEL MONTEIRO LARANJEIRA
DATE: 21/03/2024
TIME: 10:00
LOCAL: Remoto
TITLE:

USING COMPUTER VISION AND DEEP LEARNING TO EXTRACT TIME SERIES AND CLASSIFY HEART DISEASE FROM ELECTROCARDIOGRAMS

KEY WORDS:

Electrocardiogram, Atrial Fibrillation, Classification, Multimodal Neural Networks

PAGES: 36
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUMMARY:

This investigation presents a specialized multimodal neural network for classifying image-based electrocardiogram (ECG) exams. The model is designed to distinguish between normal cardiac rhythms and Atrial Fibrillation (AF) using a dataset exclusively comprising ECG exam images. The model exhibits a preprocessing stage adept at extracting the DII lead from PNG images. Subsequently, the extracted lead generates a time series and a spectrogram input to feed the multimodal network. The cross-validation metrics demonstrate the efficacy of the methodology with an accuracy of 97.65%, AUC of 94.08%, specificity of 96.89%, sensitivity of 99.20%, and an F1-score of 96.57%. Additionally, the methodology exhibits impressive performance across various data sources and multiple folds, achieving an average accuracy of 90.70%, AUC of 90.78%, specificity of 90.62%, sensitivity of 90.94%, and an F1-score of 82.09%. The multimodal approach recommended here eliminates the need for specialized software, making it easier to integrate into clinical practice and enhancing the diagnostic capabilities of healthcare professionals

COMMITTEE MEMBERS:
Presidente - 2021852 - THIAGO DAMASCENO CORDEIRO
Interno(a) - 1388993 - BRUNO ALMEIDA PIMENTEL
Externo(a) à Instituição - ESTELA RIBEIRO
Externo(a) à Instituição - MARCO ANTONIO GUTIERREZ - USP