Overview of featurization techniques used in traditional versus emerging deep learning-based algorithms for automated interpretation of the 12-lead ECG.

Dewar Finlay¹, Raymond Bond², Michael Jennings², Christopher McCausland², Daniel Guldenring³, Alan Kennedy⁴, Pardis Biglarbeigi², Salah S Al-Zaiti⁵, Rob Brisk⁶, James McLaughlin²

¹Faculty of Computing, Engineering and the Built Environment, Ulster University, Jordanstown Campus, Northern Ireland, UK. Electronic address: d.finlay@ulster.ac.uk.

²Faculty of Computing, Engineering and the Built Environment, Ulster University, Jordanstown Campus, Northern Ireland, UK.

³University of Applied Sciences Kempten, Fakultät Elektrotechnik, Kempten, Germany.

⁴PulseAI, Belfast, Northern Ireland, UK.

⁵Departments of Acute & Tertiary Care Nursing, Emergency Medicine, and Cardiology, University of Pittsburgh, Pittsburgh, PA, USA.

⁶Department of Cardiology, Craigavon Area Hospital, Craigavon, Northern Ireland, UK.

PubMed DOI

Abstract

Automated interpretation of the 12-lead ECG has remained an underpinning interest in decades of research that has seen a diversity of computing applications in cardiology. The application of computers in cardiology began in the 1960s with early research focusing on the conversion of analogue ECG signals (voltages) to digital samples. Alongside this, software techniques that automated the extractio…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.