PlaySafe Week 2 Update

PlaySafe is a digital health wearable that predicts whether someone is about to have a sudden cardiac arrest.

PlaySafe is focused towards three target groups:

  1. Athletes (According to the NIH, sudden cardiac death is the most common cause of death in young athletes)
  2. The elderly (According to Nature Cardiology, sudden cardiac death is a major cause of mortality in the elderly)
  3. ICU patients (Critically ill hospital patients are constantly at risk of dying from sudden cardiac arrest)

Also, these are two “metrics” I’m using to evaluate the overall PlaySafe system:

  1. Raw accuracy (what is the false positive rate?, how accurately does it identify cardiac arrest, etc;)
  2. Time to event (How much prior warning can PlaySafe give while maintaining its accuracy?)

Week 2 Updates:

I accomplished three things this week.

1) Hardware Prototype

I used the CAD model I made last week to make a fully functioning hardware prototype this week:

Here are pictures of it:

2) Improving Software Prototype

I added an SVM to my initial LSTM model and improved my prediction accuracy on the Holter dataset from 73% to 79%. (More graphs coming in the next couple weeks).

3) Visual GUI + Integration

Also, I was able to integrate the software with the hardware prototype to get real-time predictions of cardiac arrest onset. I made a simple Processing and Serial monitor GUI to visualize this.

The picture below shows heart rate ECG data as a real-time signal.

IMG_0340

The picture belows show the prediction software in real-time. Notice the section where it says “Consecutive 4 minute windows passed threshold”. This means a cardiac arrest is imminent in the next 5 minutes and a warning is provided.  Screen Shot 2018-09-09 at 11.54.18 PM

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