Wearable ECG Device Comparisons and the Inconclusive Reading Problem

This article covers head-to-head accuracy research comparing consumer wearable ECG devices, and what published data say about inconclusive or unreadable tracings. It does not cover blood pressure wearables in depth, heart failure monitoring, or clinical management decisions after an abnormal reading.

Research that tested multiple direct-to-consumer ECG devices against a physician-read 12-lead ECG in the same patients found that sensitivity and specificity varied by device, and every device produced a meaningful share of tracings its own algorithm could not classify at all. That inconclusive category, not just accuracy when a reading is produced, is where these devices diverge most from each other and from a clean pass or fail.

The question behind comparing wearable ECGs

Someone straps on a smartwatch, taps the ECG feature during a moment their chest feels off, and gets back a result that reads something like 'inconclusive.' Not normal, not atrial fibrillation, just unreadable. If they own a second device, or a friend's watch gives a different verdict on a similar sensation, the natural question is whether these gadgets are actually measuring the same thing with the same reliability, or whether the brand on the wrist changes what counts as a usable reading.

That question turns out to have a direct answer, at least for one comparison where several devices were tested against the same reference standard in the same group of people.

Why accuracy figures and inconclusive rates are two different problems

It is tempting to reduce all this to a single accuracy number, but the head-to-head device comparison actually surfaces two separate performance questions. One is: when the algorithm gives an answer, how often is that answer right. The other is: how often does the algorithm decline to answer at all. Those numbers do not move together. A device can have solid sensitivity and specificity on the readings it does produce, while still leaving a large fraction of attempts in the unclassified bucket, and the study of five devices found real spread on that second measure, from roughly one in six readings to about one in four, depending on which device was used.

That distinction matters because an inconclusive tracing is not a false negative or a false positive. It is closer to no data at all. Broader review work on cardiac wearables has noted that consumer smartwatches show strong detection accuracy in general population use, while also flagging that questions like decompensation detection or algorithm behavior across different conditions remain less settled, which is a reminder that 'accurate' and 'always able to produce a usable answer' are not interchangeable claims. For a closer look at how these single-lead readings stack up against a full diagnostic ECG in the first place, the accuracy question gets examined on its own.

Where the algorithm's read and a clinician's read can also disagree

Device-versus-device comparison is one layer of this. Another is what happens after a single-lead tracing reaches a human reader. Research examining cardiologist interpretation of point-of-care single-lead ECGs found variability in how physicians read the same tracings, even when reviewers were blinded to the device's own algorithmic call and to the same-day 12-lead result used as the reference standard. That points to a layer of uncertainty that sits above the hardware entirely, in how any single-lead strip, however it was captured, gets interpreted once it leaves the device.

Separately, an Apple Watch-specific study looked at accuracy of the irregular rhythm notification feature in people who already had a history of non-permanent atrial fibrillation, comparing notifications against data from implanted cardiac monitors over an extended wear period. That is a narrower and clinically different population than the general screening cohort in the five-device comparison, and it is a reminder that a device's published accuracy in one group does not automatically transfer to another. Questions about what a positive or inconclusive screening result should lead to next, including whether earlier detection changes downstream risk, are addressed separately in coverage of whether wearable-based screening affects stroke outcomes.

The bigger picture: why this comparison problem exists at all

Atrial fibrillation is common enough, and rising enough in incidence and prevalence worldwide, that the appeal of a wrist-based screening tool is easy to understand. It is also a condition where recent-onset cases are increasingly caught early through public self-screening and routine device use, which is part of why so many separate consumer products have entered this space and why direct comparisons between them have become a research question in their own right. The underlying engineering also varies more than the marketing suggests. Work on running atrial fibrillation detection algorithms on small embedded processors shows that different classifier designs, even on similar hardware, produce different tradeoffs, which helps explain why one wrist device and another can process what looks like similar raw signal data and land on different conclusions, including different rates of simply giving up and calling a reading inconclusive.

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