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AI Biotech/Diagnostics: Other Innovation
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Application US20200357518
Published 2020-11-12
Data Preparation For Artificial Intelligence-based Cardiac Arrhythmia Detection
Techniques are disclosed for preparing data for use in artificial intelligence (AI)-based cardiac arrhythmia detection. In accordance with the techniques of this disclosure, a computing system may obtain a cardiac electrogram (EGM) strip that represents a waveform of a cardiac rhythm of a same patient. Additionally, the computing system may preprocess the cardiac EGM strip. The computing system may then apply a deep learning model to the preprocessed cardiac EGM strip to generate arrhythmia data indicating whether the cardiac EGM strip represents one or more occurrences of one or more cardiac arrhythmias.
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- 1. A method comprising:
obtaining, by a computing system, one or more cardiac electrogram (EGM) strips that represent a waveform of a cardiac rhythm of a patient; preprocessing, by the computing system, the one or more cardiac EGM strips; and applying, by the computing system, a deep learning model to the one or more preprocessed cardiac EGM strips to generate arrhythmia data indicating whether the one or more cardiac EGM strips represent one or more occurrences of one or more cardiac arrhythmias.
- 13. A computing system comprising:
a storage device configured to store one or more cardiac electrogram (EGM) strips that represent a waveform of a cardiac rhythm of a patient; one or more processing circuits configured to:
preprocess the one or more cardiac EGM strips; and
apply a deep learning model to the one or more preprocessed cardiac EGM strips to generate arrhythmia data indicating whether the one or more cardiac EGM strips represent one or more occurrences of one or more cardiac arrhythmias.
- 25. A computer-readable storage medium having instructions stored thereon that, when executed, cause a computing system to:
obtain one or more cardiac electrogram (EGM) strips that represent a waveform of a cardiac rhythm of a patient; preprocess the one or more cardiac EGM strips; and apply a deep learning model to the one or more preprocessed cardiac EGM strips to generate arrhythmia data indicating whether the one or more cardiac EGM strips represent one or more occurrences of one or more cardiac arrhythmias.
