Tuesday, May 24, 2016

Freestyle Libre: some data dump.

In previous posts, I explained how I regressed an approximate calibration slope for the Libre in early 2015. In this post, I will put it to use and provide some data, including full raw scans, for you to download. [download]. This is the data set used for the charts at the bottom of this post.

The source code below contains the data I used for this regression. There might be some overlap with the above files, and some missing. In fact, I could have used more than 100 values, which would not have helped much and would have added to the tedium.

I am sorry that I am unable to provide a ton of nicely arranged dumps, but remember that this data was acquired in 2014 and early 2015, with various tools, including pencil and paper. I actually shared that data previously on github, but removed it when it became clear I was getting zero data in return and tons of requests for the "formula".

 import matplotlib.pyplot as plt  
 import numpy as np  
 from scipy import stats  
 mono = {'family' : 'monospace'}  
 # scanned values  
 xlist = [101, 101,  72, 161, 75, 69, 112, 203, 163, 154, 168,  93, 99, 66, 80, 105, 137, 124, 156, 277, 141, 135, 67]  
 # observed counts  
 ylist = [906, 892, 689, 1291, 755, 664, 954, 1605, 1360, 1340, 1485, 805, 865, 632, 818, 971, 1206, 1064, 1368, 2150, 1300, 1167, 641]  
 # some outliers  
 x_outlier = [101, 153]  
 y_outlier = [1084, 1160]  
 # do the regression  
 gradient, intercept, r_value, p_value, std_err = stats.linregress(xlist, ylist)  
 print ("Gradient and intercept, r, p, std", gradient, intercept, r_value, p_value, std_err)  
 # plot it  
 fig = plt.figure()  
 ax = fig.add_subplot(111)  
 ax.set_title('Libre: correlation reported values / observed counts')  
 ax.text(350, 2500, '{:_<10}'.format('intercept: ') + str('{:06.2f}'.format(intercept)), fontdict=mono)  
 ax.text(350, 2390, '{:_<10}'.format('gradient : ') + str('{:06.2f}'.format(gradient)), fontdict=mono)  
 ax.text(350, 2280, '{:_<10}'.format('r    : ') + str('{:06.4f}'.format(r_value)), fontdict=mono)  
 ax.text(350, 2170, '{:_<10}'.format('p    : ') + str('{:.2e}'.format(p_value)), fontdict=mono)  
 ax.text(350, 2060, '{:_<10}'.format('std   : ') + str('{:06.4f}'.format(std_err)), fontdict=mono)  
 ax.set_ylim(0, 3000)  
 ax.set_xlim(0, 520)  
 ax.set_xlabel('Reported Value')  
 ax.set_ylabel('Observed Counts')  
 mono = {'family' : 'monospace'}  
 line = plt.plot([0, 300], [intercept, intercept+300*gradient], color='r', linestyle='-', linewidth=1, label=("04/2015"))  
 plt.legend()  
 plt.show()  

Which gave

Gradient and intercept, r, p, std 7.26235656465 181.083590507 0.99295809065 6.0805538057e-21 0.189073777001

The conversion applied is as follows, based on the previously derived parameters. (I kept the habit of masking on 14 bits because in theory that is what the TI chip should deliver...)

def LibreConvert(r):
    bitmask = 0x3FFF    return ((r & bitmask) - 181.08) / 7.26

Here are the results on the data set above.

Small deviation, please note scanned value is "in trend"

Small deviation, trend uncertain

Small deviation, in trend.

Small deviation, in trend, possible noise

Small deviation, in trend

Bigger deviation, but in trend from the 145 going down, the Libre doesn't know we corrected the fall

Flattish, very small error

Again small deviation, fully in trend.

Nice regular trend, spot on.

Outlier, but in trend, the Libre doesn't know there's some exercise.

Stablish, in trend, possible noise our trend change.

Stable conditions, matching the scan

Deviation, but again in trend.

Nice trend, nice match.

Very large mismatch, but explainable if trend based on previous minutes
Note: I think I used this data previously on the blog - it should be noted that "raw" was in line with the BG meter testing, much more than the Libre Scan.

In trend.

Now, to recap...

  • I don't use the Libre anymore, this is based on 2014/2015 data. Some of you sent data, I will definitely have a look at it, thanks.
  • Match is near perfect, especially when the trend isn't changing abruptly.
  • These mismatches (I could devote an entire post to mismatched graphs) are always well in line with the previous trends and never against the trend which led me to examine the eventually predictive nature of the Libre's algorithm.
  • In many cases, direct interpretation of raw data led to better acccuracy with BG meter!
  • Those results are quite close to the actual factory calibration slope of the Libre (edit: the Libre we had - again, haven't tried any recent ones). I am confident this will be confirmed now that the official apk is in wide circulation. 
  • I still consider those results to be insufficient for release. That will be my opinion until the full Libre algorithm is documented.
PS: I'd like to stress, again, that I don't suggest, ever, that anyone uses a formula found on the Internet through copy and paste. IMHO and as shown by the outliers, calibration isn't the whole story.





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