Paper:
We release the largest public electrocardiogram (ECG) dataset of continuous raw signals for representation learning containing 11 thousand 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.

Review:
Overall the paper is well written and organized and is an application of various paradigms to extract most relevant features for Arrhythmia Subtype classification.
Plan to release the largest (claimed) public ECG data set of continuous raw signals for representation learning containing 11 thousand patients and 2 billion labelled beats.
Our goal is to enable semi-supervised ECG models to be made as well as to discover unknown sub types of arrhythmia and anomalous ECG signal events.
The stated intended goal though is the discovery of new  Arrhythmia Sub types (title), “automated arrhythmia detection” (1.1 Objective). 
A definition/example of a regular ECG signal, one presenting arrhythmia, various types of arrhythmia signal forms, would be welcome for the paper to fit this audience and be self-contained.
This work suffers from many drawbacks:
-	A definition/example of a regular ECG signal, one presenting arrhythmia, various types of arrhythmia signal forms, would be welcome for the paper to fit this audience and be self-contained.
-	Given the intended audience in ICLR, the application domains specificities are not well explained. Many concepts are not introduced clearly:
      o	ECG models, sub types of arrhythmia, anomalous ECG signal events.
      o	In related work section: 
            	Paragraph 2: more “leads”, RR interval, PR interval, QRS duration, induced myocardial ischemia.
            	Paragraph 3: single-lead wearable devices.
      o	In 3 privacy concerns: heartbeats as biometrics section/ Paragraph 2:
            	alternative ways to sense “cardiac motion”.
      o	In 4 {companyname} 11k data set section:
            	Paragraph 2: “third line exam”.
            	Paragraph 3: “beats and rhythms”, these are fundamental to understanding graphical results provided. 
      o	In 5.1 quantitative evaluation section:
            	Paragraph 3: “ .. irregular RR intervals, no distinct P waves and usually variable intervals between two atrial activations. 
            	Page 6:  “ .. high-level abnormality labels”
      o	5.2 qualitative evaluation section
            	Paragraph 1:  “ .. has a dominant S wave in V1 lead.”
      o	In 6 conclusion section:
            	“ Single-lead heart monitors” .
-	Figures are barely decipherable
      o	In Figure 6: 
            	What are ESSV and ESV, btype and rtype. Could you give a list of known subtypes?
            	Is this for PVC data only?
-	Many paragraphs are not clear to us:
      o	Introduction/Paragraph 2: 
            	“While cardiologists are able to see these differences, it is hard to conclude that they are ‘real’ by finding the same anomalous signal across multiple time points and patients without a data driven approach.”
      o	In 3 privacy concerns: heartbeats as biometrics section/ Paragraph 3:
            	Contrary to 1), 3) & 4), is (2) “.. the expression of environmental variables on the heartbeat data is unique to the individual”, a limitation to ECG to being considered a biometric measure?
      o	In 4 {companyname} 11k dataset section:
            	Paragraph 4:  “we segment each patient record into segments of 220 +1 signal samples ( ~70 minutes). Care to explain the rationale?
            	Last paragraph: “ .. we believe that processing the data with these levels of hierarchy results in some grouping information that could be leveraged to attain better results.”. This is a multi-scale approach. But do you have any medical (application domain) knowledge that would justify/hint to using such approach?
      o	Figure 5:
            	Only Beats are labelled. No Rhythm labelling?
      o	Table 2:
            	“Only 2 types of labels are provided”. Are these beats and Rhythms? What are anomalies considered in the study then?
      o	In 5.1 quantitative evaluation section:
            	Paragraph 2: “..PAC is an abnormal beat only because it appears too soon and disrupts the rhythm (frequency). Furthermore, a PAC beat has the same shape as a normal beat, so taken alone, you can nearly not make the difference with a normal beat.”. A graphical representation would be welcome.
            	Paragraph 3: “..Both require a representation that will compose a representation showing the difference between beats over time.”.
      o	qualitative evaluation section
            	Paragraph 2: “We note that these are easy to see because of the different colors we use to highlight the points, but there seems to be remaining clusters that have not been analysed.”. The clusters are not evident to us even after purposefully colored.