Background: Covid-19 patients may show specific electrocardiographic change. Here, we present an automatic algorithm for detection of the virus-altered 12-lead ECGs.
Methods: We analyzed a case group (C-Covid) and a control (NC-non Covid) group. The C-group included 12-lead ECG recordings from patients (age range [19-96] years) hospitalized at Ospedale San Matteo in Pavia (Italy) during the first 2020 pandemic outbreak. In all patients, infection was confirmed by nasal swab testing. The NC-group was built by collecting ECG in sinus rhythm from three public datasets: Georgia ECG (USA), PTB-XL (Germany) and CPSC 2018 (China). Control ECGs were selected among those matching the Covid ones by gender, age (±3 year) and Heart Rate (±3 bpm), simultaneously. Globally, the dataset consisted of 1792 signals: 896 (C group) plus 896 (NC group). An additional NC-group of 896 recordings was extracted from Ningbo (China) database, using the same criteria as before. This was used to check model generalization capability on unseen data. After a preprocessing step (3rd order Butterworth filter [0.5-45 Hz] and z-score normalization), a 4-layers convolutional neural network (CNN), with increasing filter size plus a final fully connected (FC) layer, was designed to classify C vs NC group. It was trained and k-fold cross validated (k=7) using for each model 1316 ECGs for testing and 220 for validation. Every fold model was used to classify a separate common test set of 256 ECGs. Performances are reported in terms of accuracy and False Positive Rate (FPR) (mean±std).
Results: The accuracy was 0.813±0.018 on the validation sets and 0.787±0.015 on the test set. The FPR on NC group was 0.214±0.030 (test set) and 0.215±0.021 (additional Ningbo test set) (p>0.05, ns) showing that no bias was induced by the selection of datasets.
Conclusion: These preliminary results evidence the potentiality of 12 ECG analysis for Covid screening.