Real-time monitoring and continuous monitoring are different goals. The use case for something like this is probably a wearer who is experiencing heart palpitations, and has the time and clarity of thought to actually find the correct app on their device and start recording.

Continuous monitoring is extremely challenging still because ECG data needs to be sampled at a relatively high frequency (~200 Hz) to accurately identify the QRS complex in the waveform. That uses a lot of power, and the batteries we have still aren't good enough to support those types of demands. 200 * 60 * 60 = 720,000 samples per hour to collect and process.

It's possible that algorithmic approaches may be able to reduce the sampling frequency required. Power-constraints were the main issue when I studied this topic 10 years ago during my master's degree. I had looked into non-frequency domain techniques (such as empirical mode analysis/Hilbert-Huang transform) as a possible way to reduce sampling frequency and thus power consumption.

https://github.com/AshwinSundar/Empirical-Mode-Decomposition...

There’s a strong history of useful signals from single lead ecgs.

Detection of ecg anomalies(especially episodic ones with intermittent recording) was the subject of the physionet cardiac computing challenge almost 10years ago[0].

It’s amazing how far machine learning has come. I know teach a version of this challenge as a one day in class activity in my department’s physiology class. They actually get to train multiple models on a gpu cluster (and compare that to trying to train models on their laptops).

One thing we reinforce in the lesson is human vs. computer “interpretation”. They/clinicians can look at ecgs and make some sense of them. An LTSM is worse than random chance/a medical student. However moving to the frequency domain makes the LTSM more accurate than cardiologists, but neither they nor clinicians can “see” afib ina spectrograph. It’s a great way to talk about algorithmic versus human reasoning and illustrate that to students.

That then gets reinforced with other case studies of the ying and yang of human and machine decision making throughout our curriculum- like alpha fold working great until you ask it about a structure in the absence of oxygen, because that’s not in its training data.

[0] https://physionet.org/content/challenge-2017/1.0.0/

It seems to be using ECG, the (correct) springer link is https://link.springer.com/chapter/10.1007/978-3-031-82377-0_...

>is an indictment of the legal framework around medical devices.

Well medical devices aside, the legal framework around anything, including business, manufacture etc. is more impeding while failing to address things like environmental destruction/pollution which causes real harm. ( notice, that I did not say climate change, a separate subject).

It all makes sense when one sees it either though the lens of either corruption of more likely human stupidity - where a bunch of rules give people the comfort of being protected.

There’s no “might” here, in a device advertised as “92% accuracy”. This device _will_ give false results, almost certainly an overwhelmingly number of false results relative to the actual heart attacks it detects. And those false results have real, guaranteed harm and cost as well.

Both false positives and false negatives are harmful. False positives will send people to the hospital for no reason and divert resources from people with real emergencies - not to mention leaving them with a large ER bill to pay. False negatives will result in people with actual heart attacks dismissing their symptoms and dying.

I agree the legal restrictions (and liability) should be looser. But other countries are pretty bad about this too in my experience. It's more of a medical establishment monopoly thing it seems.

Also you can already buy home ECG devices for a couple hundred bucks. Not sure if there is some history of being banned in the past or whatever, but otherwise I'd guess the main problem is just a lack of much interest in the market.

What is burdensome about the regulations? They are here: https://www.fda.gov/medical-devices/guidance-documents-medic...

They allow a summary report. They don't require a clinical human trial. They barely care if you follow the FCC and safety requirements for electronics in general. This does not look burdensome.

The most common heart attack symptoms are the same for men and women. The less common, less diagnostic symptoms are more common in women. Over time the medical opinion has swung back and forth (eg thinking women had less pain, until it was recognized that many men have heart attacks without pain) but the current consensus is that the important symptoms are pretty much the same between men and women. Women may be more likely to experience nausea during a heart attack, but you can't use nausea to diagnose a heart attack and men get nauseated a similar amount. These symptoms also don't take into account body types, which have a big impact on the type of attack and symptoms.

Men and women have slightly different ECGs and a doctor can usually tell your gender from an ECG. The appearance of a heart attack will be more similar than a normal heartbeat. Gender differences have a much smaller impact on an ECG than things like body mass and blood pressure. Overworked hearts will look more like overworked hearts.