Print Email Facebook Twitter Feature extraction and classification on heart rate time series for cardiovascular diseases Title Feature extraction and classification on heart rate time series for cardiovascular diseases Author Beekhuizen, Michael (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Pattern Recognition and Bioinformatics) Contributor Reinders, M.J.T. (mentor) Naseri Jahfari, A. (mentor) Martinez, Jorge (graduation committee) Tax, D.M.J. (graduation committee) Ghorbani, R. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science | Bioinformatics Date 2023-06-23 Abstract Cardiovascular diseases are one of the primary causes of mortality worldwide. Paroxysmal atrial fibrillation is a specific type that is difficult to detect and diagnose in a short time frame. To overcome this, we investigated if long-term wearable data can be used for the detection of heart diseases. The BigIdeasLab_STEP dataset and long-term Fitbit data from the ME-TIME study were used to examine this.Our analysis showed a correlation between the window/stride size and accuracy when performing activity classification with the BigIdeasLAB_STEP dataset. Moreover, variability was found between subjects due to differences in the physical structure of their hearts. Normalization proved to be a crucial step to minimize the subject variability and significantly improved performance. Grouping subjects and performing classification inside a group helped to improve performance and decrease inter-subject variability. Integrating handcrafted features in deep learning networks also improved classification performance.Analysis of the long-term Fitbit data showed that there is a difference between individuals based on their health condition. Classification of individual peaks was possible and worked best when utilizing a time series-specific support vector machine and grouping peaks together. Grouping peaks per week from a person and calculating a percentage of heart disease-predicted peaks also worked relatively well to distinguish between heart disease and reference subjects. Like with the previous dataset, normalization proved to be a crucial step to minimize subject variability.The findings indicate that heart rate time series can be utilized for classification tasks like predicting activity or the detection of heart diseases. However, normalization and grouping techniques need to be chosen carefully to minimize the issue of subject variability. Subject Deep LearningMachine LearningHeart rateclusteringCardiovascular disease To reference this document use: http://resolver.tudelft.nl/uuid:1be54eb0-34f0-4292-9e38-7cd5f7e27b32 Part of collection Student theses Document type master thesis Rights © 2023 Michael Beekhuizen Files PDF MSc_thesis_M_Beekhuizen.pdf 6.9 MB Close viewer /islandora/object/uuid:1be54eb0-34f0-4292-9e38-7cd5f7e27b32/datastream/OBJ/view