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Data Science: Detection of Paroxysmal Atrial Fibrillation
We aim to predict accurately paroxysmal atrial fibrillation within 7 days of its occurrence.
Keywords: Time series analysis, ECG analysis, data science, medical technologies, and digital health
The detection of paroxysmal atrial fibrillation (PAF) is crucial as it is a type of arrhythmia that is often underdiagnosed. We try to apply machine algorithms to ECG signals to predict the onset of PAF.
The detection of paroxysmal atrial fibrillation (PAF) is crucial as it is a type of arrhythmia that is often underdiagnosed. We try to apply machine algorithms to ECG signals to predict the onset of PAF.
- Implement ARIMA model, EMA crossover, neural networks, or any other time series algorithms
- Testing and comparing trends and prediction accuracy among different algorithms
**Tasks**
- Literature review (10%)
- Data analysis (loading data, data filtering) (20%)
- Design and implement a time series prediction model (20%)
- Design and implement a basic machine learning algorithm (20%)
- Test, compare and evaluate results (10%) Report and present (20%)
- Implement ARIMA model, EMA crossover, neural networks, or any other time series algorithms - Testing and comparing trends and prediction accuracy among different algorithms
**Tasks**
- Literature review (10%) - Data analysis (loading data, data filtering) (20%) - Design and implement a time series prediction model (20%) - Design and implement a basic machine learning algorithm (20%) - Test, compare and evaluate results (10%) Report and present (20%)
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
Researchgate: https://www.researchgate.net/profile/Mohamed-Elgendi
Dr Moe Elgendi (moe.elgendi@hest.ethz.ch) will supervise the student at the Biomedical and Mobile Health Technology Research Group in ETH Zurich’s D-HEST Department of Health Sciences and Technology.
Google Scholar: https://scholar.google.com/citations?user=-WFwzjoAAAAJ&hl=en
