3 ECG Annotation Tools for Machine Learning

In this guide, we compare three of the top ECG annotation tools to train AI in the healthcare industry. From automated annotations to beginner-friendly features, we’ll explore how each tool empowers the future of cardiac care.

Machine learning has made waves within the medical community and healthcare industry. Artificial Intelligence (AI) has proven itself useful in numerous uses across a variety of domains, from Radiology and Gastroenterology to Histology and Surgery. 

The frontier has now hit Electrocardiography (ECG) ECG is a cornerstone of cardiac diagnostics, offering a window into the electrical activity of the heart. By capturing this activity as waveforms, ECG provides vital insights into heart health, helping clinicians detect and monitor conditions such as arrhythmias, ischemia, and hypertrophy.

With an annotation tool, you can annotate the different waves on your Electrocardiogram diagrams and train machine learning models to recognize patterns in the data. The effectiveness of these models hinges on the quality of their training data—specifically, the annotations that mark key features of ECG waveforms.

In the guide below, we’ll explore three leading ECG annotation tools, examining how they enhance the annotation process and contribute to the future of cardiac care.

Compare Top ECG Annotation Tools

In the sections below, we'll provide some information about three top ECG annotation tools, covering their features, benefits and who they might work best for. The three tools we will be reviewing today are:

• Encord ECG
• OHIT ECG Viewer
• WaveformECG

Here is an overview of them:

Encord ECG

Encord is an automated and collaborative annotation platform for medical companies looking at ECG Annotation, DICOM/NIfTI annotation, video annotation, and dataset management. It's the best option for teams that are:

• Looking for automated, semi-automated or AI-assisted image and video annotation.
• Annotating all ontologies.
• Working with other medical modalities such as DICOM and NIfTI.
• Wanting one place to easily manage annotators, track performance, and create QA/QC workflows.

Benefits & Key features:

• Use-case-centric annotations — from native DICOM & NIfTI annotations for medical imaging to ECG Annotation tool for ECG Waveforms.
• Allows for point and time interval annotations.
• Supports the Bioportal Ontology such as PR and QT intervals.
• Integrated data labeling services.
• Integrated MLOps workflow for computer vision and machine learning teams.
• Easy collaboration, annotator management, and QA workflows — to track annotator performance and increase label quality.
• Robust security functionality — label audit trails, encryption, FDA, CE Compliance, and HIPAA compliance.
• Advanced Python SDK and API access (+ easy export into JSON and COCO formats).

Best for teams who:

• Are graduating from an in-house solution or open-source tool and need a robust, secure, and collaborative platform to scale their annotation workflows.
• Haven't found an annotation platform that can actually support their use case as well as they'd like (such as building complex nested ontologies, or rendering ECG waveforms).
• Team looking to build artificial neural networks for the healthcare industry. AI-focused cardiology start-ups or mature companies looking to expand their machine-learning practices should consider the Encord tool.

Pricing: Free trial model, and simple per-user pricing after that.

OHIF ECG Viewer

The OHIF ECG Viewer provides can be found from Radical Imaging’s Github.

The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. For a cardiologist just getting started this is a great tool and provides a baseline for comparing to other tools.

Benefits & Key features:

• Leader in open-source software.
• Renders ECG waveform natively.
• Easy (& free) to get started labeling images with.
• Great for manual ECG annotation.

Best for: Teams just getting started.

Pricing: Free.

WaveformECG

The WaveformECG tool is a web-based tool for managing and analyzing ECG data.

The tool provides a streamlined annotation experience and native image rendering with the ability to perform measurements of all relevant ontologies. It is easy to export annotations or create a report for later investigation. The tool does not support any dataset management or collaboration which might be an issue for more sophisticated and mature teams. So if you're new to the deep learning approach to ECG annotations the WaveformECG tool might be useful but if you’re looking at more advanced artificial neural networks or deep neural networks it might not be the best place.

Benefits & Key features:

• Allows for point and time interval annotations and citations.
• Supports the Bioportal Ontology and metrics.
• Annotations are stored with the waveforms, ready for data analysis.
• Renders ECG waveform natively.
• Supports scrolling through each ECG waveform.
  

Best for: Researchers and students.

Pricing: Free.

Why are ECG Annotations Important in Medical Research?

ECG annotation is an essential aspect of medical research and diagnosis, involving the identification and interpretation of different features in the ECG waveform. It plays a critical role in the accurate diagnosis and treatment of heart conditions and abnormalities, allowing you to detect a wide range of heart conditions, including arrhythmias, ischemia, and hypertrophy.

Through the meticulous analysis of the ECG waveform, experts can identify any irregularities in the electrical activity of the heart, accurately determining the underlying cause of a patient's symptoms. The information gleaned from ECG annotation provides vital indicators of heart health, including heart rate, rhythm, and electrical activity.

Regular ECG monitoring is invaluable in the management of patients with chronic heart conditions such as atrial fibrillation or heart failure. Here ECG annotation assists experts in identifying changes in heart rhythm or other abnormalities that may indicate a need for treatment adjustment or further diagnostic testing. With regular ECG monitoring and annotation, clinicians can deliver personalized care, tailoring interventions to the unique needs of each patient.

ECG Annotations Examples

The first open-source frameworks have been developed to build models based on ECG data e.g. Deep-Learning Based ECG Annotation. In this example, the author automated the process of annotating peaks of ECG waveforms using a recurrent neural network in Keras.

Even though the model was not 100% performant (it struggles to get the input/output right). It seems to work well on the QT database of PhysioNet. The Authors does mention it fails in some cases that it has never seen.

Potential future development of machine learning would be to play with augmenting the ECGs themselves or create synthetic data.

Another example of how deep learning and machine learning is useful in ECG waveforms can be found in the MathWorks Waveform Segmentation guide.

Using a Long Short-Term Memory (LSTM) network, MathWorks achieved impressive results as seen in the confusion matrix below:

If you want to get started yourself you can find a lot of open-source ECG datasets, e.g. the QT dataset from PhysioNet.

How can Machine Learning Support ECG Annotations?

Machine learning has significant potential in supporting and automating the analysis of ECG waveforms, providing a powerful tool for clinicians for improving the accuracy and efficiency of ECG interpretation. 

By utilizing machine learning algorithms, ECG waveforms can be automatically analyzed and annotated, assisting clinicians in detecting and diagnosing heart conditions and abnormalities faster and at higher accuracy.

One of the main benefits of machine learning in ECG analysis is the ability to process vast amounts of patient data. By analyzing large datasets, machine learning algorithms can identify patterns and correlations that may be difficult or impossible for humans to detect. This can assist in the identification of complex arrhythmias or other subtle changes in the ECG waveform that may indicate underlying heart conditions.

Additionally, machine learning algorithms can help in the detection of abnormalities or changes in the ECG waveform over time, facilitating the early identification of chronic heart conditions. By comparing ECG waveforms from different time points, machine learning algorithms can detect changes in heart rate, rhythm, or other features that may indicate a need for treatment adjustment or further diagnostic testing.

Lastly, machine learning models can be trained to recognize patterns in ECG waveforms that may indicate specific heart conditions or abnormalities. For example, an algorithm could be trained to identify patterns that indicate an increased risk of a heart attack or other acute cardiac event. By analyzing ECG waveforms and alerting clinicians to these patterns, it can help in the early identification and treatment of these conditions, potentially saving lives.

Conclusion

There you have it! The 3 Best ECG annotation Tools for machine learning in 2023. 

We’re super excited to see the frontier being pushed on ECG waveforms in machine learning and proud to be part of the journey with our customers. If you’re looking into augmenting the ECGs themselves or creating synthetic data get in touch and we can provide you input and help with it!