Frequency and Time Domain Feature Engineering and Predictive Modeling Based on ECG, SpO2, and Respiration Signals
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Journal Title
Journal ISSN
Volume Title
Perustieteiden korkeakoulu |
Master's thesis
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Author
Date
2020-03-16
Department
Major/Subject
Computer Science
Mcode
SCI3042
Degree programme
Master’s Programme in Computer, Communication and Information Sciences
Language
en
Pages
77 + 1
Series
Abstract
Today, there exists a challenge in simplifying biosignals into features that are well suited for machine learning and clinician understanding. This work reports the feature engineering exercise involved with such challenge, along with the predictive modeling. We primarily tackle ECG, Respiration (Thoracic Impedance), and SpO2 (Plethsmographic) signals extracted from a proprietary dataset used by GE Healthcare. Throughout the study, we analyze biosignals while searching for general characteristics which may help describe (and even highlight) human function for a machine learning model, while maintaining clinical value. Wave Morphology Analysis in the Time Domain, Wavelet Decomposition and Fast Fourier Transforms were the main methods explored for feature engineering. Finally, results from a Convolutional Neural Network and a Random Forest model are reported, whereby the best performing model is able to predict Sepsis with 77% accuracy at least three (3) hours in advance.Description
Supervisor
Ollila, EsaThesis advisor
Samiee, KavehKeywords
signal processing, machine learning, feature enEngineering, healthcare, sepsis
