Deep learning and the electrocardiogram: review of the current state-of-the-art

Abstract

In the recent decade, deep learning, a subset of artificial intelligence and machine learning, has been used to identify patterns in big healthcare datasets for disease phenotyping, event predictions, and complex decision making. Public datasets for electrocardiograms (ECGs) have existed since the 1980s and have been used for very specific tasks in cardiology, such as arrhythmia, ischemia, and cardiomyopathy detection. Recently, private institutions have begun curating large ECG databases that are orders of magnitude larger than the public databases for ingestion by deep learning models. These efforts have demonstrated not only improved performance and generalizability in these aforementioned tasks but also application to novel clinical scenarios. This review focuses on orienting the clinician towards fundamental tenets of deep learning, state-of-the-art prior to its use for ECG analysis, and current applications of deep learning on ECGs, as well as their limitations and future areas of improvement.

Keywords: Artificial intelligence; Big data; Cardiovascular medicine; Electrocardiogram; Deep learning.

Figure 1

Understanding important layer types. Two common layer types used in deep learning pipelines for image processing are fully connected layers (top), which function simply as many linear regression models with a non-linear activation function that increases the informational capacity of the model. Convolutional layers (bottom) are composed of many ‘kernels’ that learn particular patterns to pick up (small gradient boxes) and scan across an input signal where that pattern may be present. In this example, the kernels from the top to below represent the shape of a R-S wave, a P-wave, and T-P wave segment, and their relative strengths of detection (high: yellow, low: blue) are shown for the input ECG signal (magenta). The resulting signals demonstrate localization of these key kernel patterns that helps the deep learning model learn both the presence and relationship of such features in the input signal. ECGs, electrocardiograms.

Figure 2

Supervised deep learning pipeline: this figure shows what a simple deep learning pipeline for ECG analysis may look like. First, ECGs recorded from patients may be stored in an electronic health record system that can be queried for their retrieval (Panel 1). While user-readable formats may be generated when clinicians query the EHR for viewing a patient ECG, these ECGs will be stored as a sequence of numbers with accompanying header information (i.e. patient medical record number, date of ECG acquisition, etc.) in an easily queryable data structure. Next, during time of analysis, all stored patient ECGs may be queried selectively to construct a dataset that is more easily amenable for a DL model (i.e. matrix format) for training and evaluation as well as being relevant for the application of interest (Panel 2). Third, ECGs must be pre-processed for noise removal and baseline variation. These may then be further re-represented as one-dimensional signals, as pixelated images, in the Fourier space, or as wavelets (Panel 3). Finally, the dataset may be split into training, validation, and testing and used to help a deep neural network learn to predict on a particular outcome of interest (Panel 4). ECGs, electrocardiograms.

Figure 3

Paper selection process: consort diagram demonstrating the selection criteria used in retrieving the literature pieces evaluated in this review. The number of articles corresponding to different application categories is also shown.