Applications of deep learning and artificial intelligence in wearable cardiac monitoring

Recent technological advancements have enabled machine learning to recognize voice commands like Amazon Alexa, and recognize your face like Apple Face ID. Today, these technical advances are utilized in healthcare to help identify cancer in CT scans, detect diabetic retinopathy and identify irregular rhythms in ECG. Cardiac defibrillators, insulin pumps and monitors all work wirelessly and can feed data directly into the electronic health record (EHR).

The BodyGuardian® Remote Monitoring System is one such example. Patients wear the cardiac monitor, which feeds real-time data into a cloud-based health platform physicians can access. The need for these technologies is growing as we see a greater incidence of cardiac disease and the population is aging. This will bring a greater reliance on algorithms to provide high-quality reporting in a timely manner. These factors are amplified in the case of mobile cardiac telemetry (MCT), where ECG is streamed directly to data processing centers, annotated, and may be used to quickly alert clinicians of potentially life-threatening cardiac events. For MCT to be most effective, data annotation must be highly accurate and quick.

Caution: U.S. Federal law restricts this device to sale by or on the order of a physician.



Traditional machine learning is a type of artificial intelligence that enables machines to learn statistical relationships between inputs and outputs. Uncovering these relationships through learning produces algorithms that make decisions in a way that can mirror humans. Decisions are driven by experience and capture complexity and nuance that non-learning algorithms cannot reproduce. However, machine learning relies on human insight and engineering to extract the important information from raw data, placing a fundamental limit on the ability of these algorithms to achieve human-level performance.

Deep learning is a type of artificial intelligence that is like machine learning but with a powerful new trick. Learning is used to identify the important information within data and to make decisions based on that information. This means that the performance of deep learning algorithms is not limited to what we know is important about the data. The learning processes can uncover new features that may be difficult to describe but are fundamental to human decision making. Applying learning more generally is what enables deep learning algorithms to achieve human-level performance.



Improved patient experience, improved health of populations and reduced costs are the Holy Grail of healthcare today. Using AI for clinical decision support empowers clinicians with more accurate information, faster. Enabling clinicians to deliver better care to more patients is the promise of AI. Because deep learning leverages computation but learns like humans, it can be trained through trial-and-error on thousands or millions of ECG recordings. This provides the algorithm with a lifetime of training in only a few hours. Like with human learning, more training makes for better decision making.