Smartwatches have come a long way in health and wellness monitoring since the introduction of the Apple Watch. FDA Approved ECG (Electrocardiogram) and Atrial Fibrillation detection has become staples on the Apple Watch, Samsung Galaxy Watch, and some Fitbit devices.
- Apple Watch could become a self-check tool for symptoms of heart attack (myocardial infarction)
- Apple Watch could solve a big problem with monitoring cryptogenic stroke patients
- Apple’s new ECG feature for detecting AFib at high HR gets FDA Clearance
- Apple Watch can help detect SVT (Supraventricular Tachycardia) and help with the treatment
- Apple is hiring new project personnel for a Class II Medical device
However, all the current smartwatches offering heart monitoring features have one major drawback.
They all generate an ECG that is similar to a single-lead ECG.
When you take an ECG in a doctor’s office, a standard 12-lead ECG is usually taken. This 12-lead ECG records electrical signals from different angles in the heart to produce twelve different waveforms.
A single-lead ECG can provide information about heart rate and rhythm and enable the classification of AFib.
However, a single-lead ECG cannot be used to identify some other conditions, like heart attacks.
Multiple-lead ECG is necessary for the accurate and robust detection of cardiac disorders, notably acute myocardial infarction.
The Big Story
According to a new study published by researchers in South Korea, advances in Artificial intelligence-based algorithms can help detect myocardial infarction (heart attacks) using a single lead smartwatch.
According to earlier studies, Apple Watch can generate multi-channel electrocardiograms with some maneuvering. Last year, researchers found that ECGs recorded on smartwatches might help obtain an earlier diagnosis of acute coronary syndromes. These researchers used an Apple Watch Series 4 for their study. Their findings were published in JAMA Cardiology.
Apple Watch and other smartwatches with ECG functionality can obtain multi-lead asynchronous ECG recordings by placing the watch sensor sequentially on different body parts.
Researchers of this new study noted that asynchronous ECG lead sets could be derived from ECG reports simulating a situation similar to the sequential recording of ECG leads via smartwatches. For example, a 4-lead subset consisting of leads I, aVR, V1, and V4 from the ECG report is entirely asynchronous.
According to the researchers of this study, “Our primary aim was to develop an AI model for detecting acute myocardial infarction from asynchronous ECG signals, which outperforms the automatic ECG interpretation provided by the GE ECG analysis program.
Our secondary aim was to determine the optimal number of leads required for sufficient diagnostic power.”
Their AI-based algorithm extracted data for 97,742 patients aged 20 years or older, with 183,982 ECGs recorded within 24 hours of each emergency room visit.
This study shows the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for the automated diagnosis of cardiac disorders by developing an AI model for detecting acute myocardial infarction with asynchronous ECG signals.
The study results showed that measuring at least three leads, ideally more than four is necessary for accurate detection.
For every 30-minute delay in coronary reperfusion, the relative 1-year mortality rate increases by 7.5%.
According to the researchers, measuring additional leads would only take less than a minute with smartwatches, and the benefit of doing so would greatly outweigh the risk.
The researchers argue that their study has substantial medical and economic impacts.
“First, our model can significantly reduce time to diagnosis, and consequently reduce time to reperfusion, which is the elapsed time between the onset of symptoms and reperfusion and is critical to the clinical outcome of the disease.”
“With our model implemented on smartwatches, reliable preliminary diagnosis can be made even before contact with emergency services, thereby greatly reducing the time from the onset of symptoms to diagnosis. With the preliminary diagnosis already made, patients can be promptly triaged to the most appropriate form of treatment”.
You can read about this new study’s details, the researcher’s guidelines for future work, limitations around the current approach, and more.
The ability of Apple’s ECG app to accurately classify an ECG recording into AFib and sinus rhythm was tested in a clinical trial of approximately 600 subjects. It demonstrated 99.6% specificity concerning sinus rhythm classification and 98.3% sensitivity for AFib classification for the classifiable results.
Similar large-scale clinical studies would be required to pursue the efficacy of using multi-lead asynchronous ECG for detecting Heart Attacks with a smartwatch.
With improvements in Edge computing and smartwatch companies adding more processing power and battery power, it is not long before we see some sophisticated health monitoring technologies on our wrists that could potentially save lives.
Source: Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals—Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study