Data analytics to augment decision making in cardiac care

  • Khaled Rjoob

Student thesis: Doctoral Thesis


Acute coronary syndrome (ACS) causes the majority of deaths in ischaemic heart disease. Hence, ACS needs to be rapidly and correctly diagnosed. The electrocardiogram (ECG) is a non-invasive method that is used to diagnose ACSs alongside cardiac biomarkers such as troponin. However, ACS diagnostics when using the ECG and blood biomarkers have several challenges such as poor ECG data quality due to electrode misplacement or noise. Moreover, there are limitations with the use and accuracy of cardiac biomarkers since they typically require a waiting time to attain the result. The aim of this PhD is to use data science to augment clinical decision-making by improving, 1) ECG data quality and 2) discovering novel biomarkers to improve the sensitivity and specificity of detecting acute myocardial infarctions, specifically ST-elevation myocardial infarction (STEMI). Datasets including blood biomarkers and ECGs have been collected from Altnagelvin hospital. In the first part of this PhD, several traditional machine learning (ML) and deep learning (DL) algorithms have been used to detect electrode misplacement in ECG data. The ML algorithms achieved high performance rates (accuracy=93% and 0.9 area under the curve). In the second part of this PhD, the impact of signal noise on ECG interpretation was investigated, and it transpired that noise has a significant impact (P
Date of AwardJun 2022
Original languageEnglish
SupervisorRaymond Bond (Supervisor), Victoria Mc Gilligan (Supervisor) & Aaron Peace (Supervisor)


  • Machine learning
  • Lead misplacement
  • ECG
  • Deep learning
  • Proteomics

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