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How do you get a decent ECG with a wearable that has a single point of contact with the skin. If you need to touch a contact on the device to get a two lead equ

by krunck 1y ago

How do you get a decent ECG with a wearable that has a single point of contact with the skin. If you need to touch a contact on the device to get a two lead equivalent ECG then how can this be real time monitoring?

deleted 1y ago
[deleted]
ashwinsundar 1y ago
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-for-MIT-BIH-Arrhythmia-Data/blob/master/Ashwin%20Sundar%20Final%20Report.pdf https://github.com/AshwinSundar/Empirical-Mode-Decomposition...
- sneak 1y ago
  i would imagine you could take an ekg for 1-2 seconds every 30 seconds and run it through a much more simple model to determine if it should take a longer sample, no? rather than reducing the frequency of the sampling, dynamically adjust the duty cycle of when sampling is happening? this is probably a dumb suggestion, it seems pretty obvious. for example the apple watch doesn’t do o2 monitoring continuously, just for some fraction of the time. do you need to sample every second to detect heart attacks? don’t they continue to show up on an ekg for more than 30 seconds?
  - elric 1y ago
    > apple watch doesn’t do o2 monitoring continuously, just for some fraction of the time Making it effectively useless? Unless the fraction of the time is multiple times per minute? E.g. in sleep apnea it's not uncommon for some desaturation to occur, triggering an arousal and deeper breaths, restoring saturation, only for the cycle to repeat 2 minutes later. My Garmin has a similarly useless feature. I have no idea what the supposed benefit is. Maybe they hope that if they sample multiple nights they can detect some desaturation anyway and can get the user in for polysomnography? Might be worth it.
    - jml78 1y ago
      I have mild sleep apnea and my Apple Watch is the reason I figured it out. If you wear it every night, you will catch it. I have on average 1-2 events an hour. Even with that few, the Apple Watch would catch the O2 drop enough times over a month that it worried me to see my O2 drop under 90%
      - elric 1y ago
        Impressive that it caught it. Did the apple watch catch 1-2 events per hour? Or was that a sleep study? Sleep apnea isn't usually "officially" diagnosed until you hit >5/hour, 1-2/hour isn't even "mild", it's basically "normal".
        jml78 1y ago
        Apple Watch just caught my blood oxygen dropping. I had a legit sleep study done. With my cpap, I normally have two 1-2 events/hour but my blood oxygen no longer drops
- firesteelrain 1y ago
  Fitbit supports detection of Afib and has FDA approval. It’s not entirely out of the realm of possibility that one day it could also receive approval to detect heart attacks. It probably already has the data that might lead a savvy user to infer it just by looking at elevated resting heart rate, SpO2 differences, and irregular heart rhythm alerts.
  - lazyasciiart 1y ago
    My Fitbit record showing heart rate spikes to 125 was one of the things that I noticed that suggested I was having a heart attack in 2020 (I was).
    - stavros 1y ago
      Hm, why is 125 a heart attack? Isn't that a fairly normal heart rate for exertion? Or were you just at rest?
      - firesteelrain 1y ago
        GP could have been having a non STEMI heart attack.
        lazyasciiart 1y ago
        I was, actually, but I don’t know how that’s connected? This was something like a ten minute spike in the middle of the night, a couple of times.
        firesteelrain 1y ago
        The sympathetic nervous system activates, increasing heart rate and the body is trying to compensate for the compromised heart muscle. Then, there is the possible adrenaline surge because you are actually in pain. If the NSTEMI causes left ventricular dysfunction, the heart compensates with a faster rate to maintain blood flow. I went through this with a relative just two weeks ago and learned this from the cardiologists
- vlovich123 1y ago
  There are audio recorders like TileRec by Attodigit that can record for 30 hours on a single charge and voice is recorded at 16khz and compressed live to mp3 and is super tiny at 0.5z. I’m thoroughly unconvinced that power or complexity of handling a 200hz signal is the bottleneck. 200hz is nothing probably even to a low end microcontroller these days.
  - ashwinsundar 1y ago
    I hope this is true for ECG sampling as well! It does beg the question as to what is the bottleneck today then, if it's not power.
- hwillis 1y ago
  This is out of my specific expertise, but the AFE1291 does 8k 24 bit samples per second and uses 495 μW when sampling. That's enough to run full rate for 10 straight days on an apple watch battery (1.189 Wh was the first google result). Integrated frontends have had big impacts on efficiency and improving batteries have had basically the same increase in specific energy. I would be shocked if power was a limiting factor. https://www.ti.com/lit/ds/symlink/ads1291.pdf?ts=1746971354934 https://www.ti.com/lit/ds/symlink/ads1291.pdf?ts=17469713549...
avs733 1y ago
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/ https://physionet.org/content/challenge-2017/1.0.0/
- closewith 1y ago
  > There’s a strong history of useful signals from single lead ecgs. But to be clear, a single lead ECG requires two electrodes at a minimum and commonly a third as ground. So a single lead ECG will have minimum two cables attached to electrodes on the patient. The placement depends on which lead (eg lead I, lead II, etc) but there's always two minimum.
  - avs733 1y ago
    thanks for unpacking this - that is an important clarification.
ashoeafoot 1y ago
How do you handle the whole legal elements without turning your app utterly useless? If a ekg sensoradhesive goes loose in the hospital its a alarm event but not a code. So a human has to look and evaluate the data for plausibility and alarm?

by krunck 1y ago

deleted 1y ago

[deleted]

ashwinsundar 1y ago

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-for-MIT-BIH-Arrhythmia-Data/blob/master/Ashwin%20Sundar%20Final%20Report.pdf https://github.com/AshwinSundar/Empirical-Mode-Decomposition...

sneak 1y ago
i would imagine you could take an ekg for 1-2 seconds every 30 seconds and run it through a much more simple model to determine if it should take a longer sample, no? rather than reducing the frequency of the sampling, dynamically adjust the duty cycle of when sampling is happening? this is probably a dumb suggestion, it seems pretty obvious. for example the apple watch doesn’t do o2 monitoring continuously, just for some fraction of the time. do you need to sample every second to detect heart attacks? don’t they continue to show up on an ekg for more than 30 seconds?
- elric 1y ago
  > apple watch doesn’t do o2 monitoring continuously, just for some fraction of the time Making it effectively useless? Unless the fraction of the time is multiple times per minute? E.g. in sleep apnea it's not uncommon for some desaturation to occur, triggering an arousal and deeper breaths, restoring saturation, only for the cycle to repeat 2 minutes later. My Garmin has a similarly useless feature. I have no idea what the supposed benefit is. Maybe they hope that if they sample multiple nights they can detect some desaturation anyway and can get the user in for polysomnography? Might be worth it.
  - jml78 1y ago
    I have mild sleep apnea and my Apple Watch is the reason I figured it out. If you wear it every night, you will catch it. I have on average 1-2 events an hour. Even with that few, the Apple Watch would catch the O2 drop enough times over a month that it worried me to see my O2 drop under 90%
    - elric 1y ago
      Impressive that it caught it. Did the apple watch catch 1-2 events per hour? Or was that a sleep study? Sleep apnea isn't usually "officially" diagnosed until you hit >5/hour, 1-2/hour isn't even "mild", it's basically "normal".
      - jml78 1y ago
        Apple Watch just caught my blood oxygen dropping. I had a legit sleep study done. With my cpap, I normally have two 1-2 events/hour but my blood oxygen no longer drops
firesteelrain 1y ago
Fitbit supports detection of Afib and has FDA approval. It’s not entirely out of the realm of possibility that one day it could also receive approval to detect heart attacks. It probably already has the data that might lead a savvy user to infer it just by looking at elevated resting heart rate, SpO2 differences, and irregular heart rhythm alerts.
- lazyasciiart 1y ago
  My Fitbit record showing heart rate spikes to 125 was one of the things that I noticed that suggested I was having a heart attack in 2020 (I was).
  - stavros 1y ago
    Hm, why is 125 a heart attack? Isn't that a fairly normal heart rate for exertion? Or were you just at rest?
    - firesteelrain 1y ago
      GP could have been having a non STEMI heart attack.
      - lazyasciiart 1y ago
        I was, actually, but I don’t know how that’s connected? This was something like a ten minute spike in the middle of the night, a couple of times.
        firesteelrain 1y ago
        The sympathetic nervous system activates, increasing heart rate and the body is trying to compensate for the compromised heart muscle. Then, there is the possible adrenaline surge because you are actually in pain. If the NSTEMI causes left ventricular dysfunction, the heart compensates with a faster rate to maintain blood flow. I went through this with a relative just two weeks ago and learned this from the cardiologists
vlovich123 1y ago
There are audio recorders like TileRec by Attodigit that can record for 30 hours on a single charge and voice is recorded at 16khz and compressed live to mp3 and is super tiny at 0.5z. I’m thoroughly unconvinced that power or complexity of handling a 200hz signal is the bottleneck. 200hz is nothing probably even to a low end microcontroller these days.
- ashwinsundar 1y ago
  I hope this is true for ECG sampling as well! It does beg the question as to what is the bottleneck today then, if it's not power.
hwillis 1y ago
This is out of my specific expertise, but the AFE1291 does 8k 24 bit samples per second and uses 495 μW when sampling. That's enough to run full rate for 10 straight days on an apple watch battery (1.189 Wh was the first google result). Integrated frontends have had big impacts on efficiency and improving batteries have had basically the same increase in specific energy. I would be shocked if power was a limiting factor. https://www.ti.com/lit/ds/symlink/ads1291.pdf?ts=1746971354934 https://www.ti.com/lit/ds/symlink/ads1291.pdf?ts=17469713549...

avs733 1y ago

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/ https://physionet.org/content/challenge-2017/1.0.0/

closewith 1y ago
> There’s a strong history of useful signals from single lead ecgs. But to be clear, a single lead ECG requires two electrodes at a minimum and commonly a third as ground. So a single lead ECG will have minimum two cables attached to electrodes on the patient. The placement depends on which lead (eg lead I, lead II, etc) but there's always two minimum.
- avs733 1y ago
  thanks for unpacking this - that is an important clarification.

ashoeafoot 1y ago

How do you handle the whole legal elements without turning your app utterly useless? If a ekg sensoradhesive goes loose in the hospital its a alarm event but not a code. So a human has to look and evaluate the data for plausibility and alarm?