Sistemas de Deep-learning no apoio ao rastreio do bloqueio do ramo direito através de sinais ECG

  • Pedro Miguel da Silva Baptista Ribeiro (Student)

Student thesis: Master's Thesis


With the exponential growing up in the number of cases for cardiovascular diseases, the idealizationof an algorithm that can distinguish pathologies is a great ally in diagnosis.The Right Bundle Branch Block, even though is a disease that can never present symptoms, it isan excellent indicator for future cardiovascular diseases.In order to detect the apppearence of the BBB in the early stages, in this work the DiscreteWavelet Transform was applied to the ECG signals, which allowed to extract characteristics such asenergy, entropy and coherence from three different levels of signal decomposition.The signal discrimination was performed through CNN in the 30-fold cross-validation process.The comparison accuracy between BBB and other diseases, present in the database, ranged from98,90% and 100% using small portions of signal as input/output pairs fotr the CNN. In the case ofthe energy measurement, CNN provided an accuracy between 91,14% and 66,51%, for the entropy,91,68% and 64,31% and using coherences, a maximum accuracy of 90,83%.
Date of Award24 Feb 2021
Original languagePortuguese
Awarding Institution
  • Universidade Católica Portuguesa
SupervisorPedro Miguel Rodrigues (Supervisor)


  • ECG
  • Convolutional neural network
  • Cross-validation
  • Right bundle branch block
  • Discrete wavelet transform


  • Mestrado em Engenharia Biomédica

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