A spectral-longitudinal model for detection of heart attack from 12-lead electrocardiogram waveforms

Conference item

Cardiovascular diseases (CVDs) remain responsible for millions of deaths annually. Myocardial infarction (MI) is the most prevalent condition among CVDs. Although datadriven approaches have been applied to predict CVDs from ECG signals, comparatively little work has been done on the use of multiple-lead ECG traces and their efficient integration to diagnose CVDs. In this paper, we propose an end-to-end trainable and joint spectral-longitudinal model to predict heart attack using data-level fusion of multiple ECG leads. The spectral stage transforms the time-series waveforms to stacked spectrograms and encodes the frequency-time characteristics, whilst the longitudinal model helps to utilise the temporal dependency that exists in these waveforms using recurrent networks. We validate the proposed approach using a public MI dataset. Our results show that the proposed spectrallongitudinal model achieves the highest performance compared to the baseline methods.

Authors: Tadesse, GA; Javed, H; Weldemariam, K; Zhu, T

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IEEE
• Publication date: 2020-08-27
• Acceptance date: 2020-04-10
• Event title: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020)
• Event website: https://embc.embs.org/2020/
• Event start date: 2020-07-20
• Event end date: 2020-07-24
• DOI: 10.1109/EMBC44109.2020.9176253
• EISSN: 1558-4615
• ISSN: 1557-170X
• EISBN: 9781728119908
• ISBN: 9781728119915
• Language: English
• Keywords: FFR; cardiovascular disease; transfer learning; health informatics; PTB ECG database; deep learning