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Web-based R Shiny application for teaching, learning, and performing statistical analyses common in the biological/healthcare sciences.
This is an algorithm which detects electrocardiogram features using persistent homology and then measures the PR,QT,ST-interval, QRS-duration, P-wave duration, and T-wave duration based off of the upper and lower time axis bounds of the representative cycles of the detected features within the dataset. Essentially, P, Q, S, and T waves are classified as dimension one homology features with a persistence and center of mass of representative cycle within certain ranges depending on the specific waveform being identified. For more about intuition behind the topological invariants of the data along with how these invariants are used to measure intervals of interest and eventually characterize arrhythmias, see "ekg_feature_extraction_intro.docx"
This repository contains Python scripts which process a given ECG signal and construct predictor variables based off of persistent homology-derived statistics. This repository also contains R scripts which implement a variety of binary classification models for detection of arrhythmias.
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