Electrocardiogram wave segmentation using machine learning
A method includes classifying, using a machine learning model, a portion of an electrocardiogram measurement as an artifact. The method further includes normalizing the electrocardiogram measurement except the portion of the electrocardiogram measurement classified as the artifact. The method further includes applying the machine learning model to the normalized electrocardiogram measurement to detect a cardiac event.
1 . A method comprising:
training a machine learning model using labeled electrocardiogram measurements,
wherein the labeled electrocardiogram measurements comprise labeled segments,
and wherein the labeled segments comprise a segment labeled as an artifact;
classifying, using the machine learning model, a portion of an electrocardiogram measurement as an artifact, wherein the classifying the portion as the artifact comprises:
classifying the portion as a pause indicating that a heart stopped,
determining a probability that classifying the portion as the pause is correct, and
classifying the portion as the artifact in response to determining that the probability does not meet a threshold;
normalizing the electrocardiogram measurement except the portion of the electrocardiogram measurement classified as the artifact; and
detecting a cardiac event from the normalized electrocardiogram measurement.
2 . The method of claim 1 , wherein the electrocardiogram measurement classified as the artifact is a bandpass filtered electrocardiogram measurement.