We release the largest public ECG dataset of continuous raw signals for representation learning containing over 11k patients and 2 billion labelled beats. Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown subtypes of arrhythmia and anomalous ECG signal events. To this end, we propose an unsupervised representation learning task, evaluated in a semi-supervised fashion.   We provide a set of baselines for different feature extractors that can be built upon.   Additionally, we perform qualitative evaluations on results from PCA embeddings, where we identify some clustering of known subtypes indicating the potential for representation learning in arrhythmia sub-type discovery. Arrhythmia detection is presently performed by cardiologists or technologists familiar with ECG readings. Recently, supervised machine learning has been successfully applied to perform detection of certain types of arrhythmia (Hannun et al., 2019; Yıldırım et al., 2018; Mincholé & Rodriguez, 2019; Porumb et al., 2020) . However, there may be ECG anomalies that warrant further investigation because they do not fit the morphology of presently known arrhythmia. We seek to use a data driven approach to finding these differences that cardiologists have anecdotally observed, which motivates the representation learning potential of this data. Our data is collected by the {DEVICENAME} TM , a single-lead heart monitor device from {COMPANYNAME} (Paquet et al., 2019) . The raw signals were recorded with a 16-bit resolution and sampled at 250Hz with the {DEVICENAME} TM in a modified lead 1 position. The wealth of data this provides us can allow us to improve on the techniques currently used by the medical industry to process days worth of ECG data, and perhaps to catch anomalous events earlier than currently possible. All data is made public 1 . The ethics institutional review boards at the {UNIVERSITY} approved the study and release of data #{STUDYID} Single-lead heart monitors like the {DEVICENAME} TM are increasingly common, and have the potential for cardiologists to learn much more about arrhythmia and related heart diseases. However, this amount of data means manual analysis is no longer practical. Machine learning has been widely deployed in the medical field by training a model to predict the right diagnosis based on human expert labels. Supervised learning serves well as an assistant in medical field; however, it hardly provides information beyond human knowledge. Additionally, certain human body signals can be very complex and imply non-linear features that cannot be easily identifiable manually. At present, representation learning methods have a potential in disentangling complex features, and potentially, unveil new signal structures of certain diseases which can correlate with clinical presentations. By releasing this dataset, we believe that we can leverage unsupervised representation learning expertise to not only help to enable training models with lower number of samples, but potentially find new diseases and identify patterns associated with them. We have proposed an evaluation pipeline for learning a feature extractor and evaluating extracted features using known arrhythmia as a proxy to measure the usefulness of the features. In addition, we have provided baseline results for frame-level representations under different feature extraction methods. Our data preparation makes a three level hierarchy available -the segment and patient level grouping of data. While we did not provide baselines that exploit this, future work that can Figure 8 : Reconstructions using the AE and PCA 100 . Two samples are shown, one for each column. The input is shown on the top followed by the AE and then PCA. take advantage of this context to extract better representations, and perhaps, find more interesting structure in the representation space. We also believe that this dataset can serve as a benchmark in other areas of machine learning, such as anomaly and outlier detection and hierarchical sequence modelling.
We release a dataset constructed from single-lead ECG data from 11,000 patients who were prescribed to use the {DEVICENAME}(TM) device. This paper describes a large-scale ECG dataset the authors intend to publish and provides unsupervised analysis and visualization of the dataset.