  In this work we search for best practices in pre-processing of
Electrocardiogram (ECG) signals in order to train better classifiers for the
diagnosis of heart conditions. State of the art machine learning algorithms
have achieved remarkable results in classification of some heart conditions
using ECG data, yet there appears to be no consensus on pre-processing best
practices. Is this lack of consensus due to different conditions and
architectures requiring different processing steps for optimal performance? Is
it possible that state of the art deep-learning models have rendered
pre-processing unnecessary? In this work we apply down-sampling, normalization,
and filtering functions to 3 different multi-label ECG datasets and measure
their effects on 3 different high-performing time-series classifiers. We find
that sampling rates as low as 50Hz can yield comparable results to the commonly
used 500Hz. This is significant as smaller sampling rates will result in
smaller datasets and models, which require less time and resources to train.
Additionally, despite their common usage, we found min-max normalization to be
slightly detrimental overall, and band-passing to make no measurable
difference. We found the blind approach to pre-processing of ECGs for
multi-label classification to be ineffective, with the exception of sample rate
reduction which reliably reduces computational resources, but does not increase
accuracy.
