Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
arXiv:1912.07618 · doi:10.1007/978-3-030-64610-3_40
Abstract
Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from the v6, vz, and ii leads are critical to correctly identify myocardial infarction. Second, we use this finding and adapt the ConvNetQuake neural network model--originally designed to identify earthquakes--to attain state-of-the-art classification results for myocardial infarction, achieving classification accuracy on a record-wise split, and classification accuracy on a patient-wise split. These two results represent cardiologist-level performance level for myocardial infarction detection after feeding only 10 seconds of raw ECG data into our model. Third, we show that our multi-ECG-channel neural network achieves cardiologist-level performance without the need of any kind of manual feature extraction or data pre-processing.
arXiv commentAccepted to the European Medical and Biological Engineering Conference (EMBEC) 2020
References in corpus (21)
- Deep Learning in Neural Networks: An Overview
- Deep learning for time series classification: a review
- A unified deep artificial neural network approach to partial differential equations in complex geometries
- ECG Heartbeat Classification: A Deep Transferable Representation
- Improving galaxy morphologies for SDSS with Deep Learning
- Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
- Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
- Glitch Classification and Clustering for LIGO with Deep Transfer Learning
- Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
- Using transfer learning to detect galaxy mergers
- Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
- Reduced-order modeling with artificial neurons for gravitational-wave inference
- Transfer learning for galaxy morphology from one survey to another
- Enabling real-time multi-messenger astrophysics discoveries with deep learning
- Detection of Inferior Myocardial Infarction using Shallow Convolutional Neural Networks
- Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey
- Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
- Denoising Gravitational Waves using Deep Learning with Recurrent Denoising Autoencoders
- Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders
- Transfer learning for radio galaxy classification
- Fusing numerical relativity and deep learning to detect higher-order multipole waveforms from eccentric binary black hole mergers