[EN] Although standard 12-lead ECG is the primary
technique in cardiac diagnostic, detecting different
cardiac diseases using single or reduced number of leads
is still challenging. The purpose of our team, itaca-UPV,
is to provide a method able to classify ECG records using
minimal lead information in the context of the 2021
PhysioNet/Computing in Cardiology Challenge, also using
only a single-lead.
We resampled and filtered the ECG signals, and
extracted 109 features mostly based on Hearth Rhythm
Variability (HRV). Then, we used selected features to train
one feed-forward neural network (FFNN) with one hidden
layer for each class using a One-vs-Rest approach, thus
allowing each ECG to be classified as belonging to none
or more than one class. Finally, we performed a 3-fold
cross validation to assess the model performance.
Our classifiers received scores of 0.34, 0.34, 0.27, 0.30,
and 0.34 (ranked 26th, 21th, 29th, 25th, and 22th out of 39
teams) for the 12, 6, 4, 3 and 2-lead versions of the hidden
test set with the Challenge evaluation metric.
Our minimal-lead approach may be beneficial for novel
portable or wearable ECG devices used as screening tools,
as it can also detect multiple and concurrent cardiac
conditions. Accuracy in detection can be improved adding
more disease-specific features.Jiménez-Serrano, S.; Rodrigo Bort, M.; Calvo Saiz, CJ.; Castells, F.; Millet Roig, J. (2021). Multiple Cardiac Disease Detection from Minimal-Lead ECG Combining Feedforward Neural Networks with a One-vs-Rest Approach. 1-4. https://doi.org/10.22489/CinC.2021.1091