This content is for the Kenek Edge pulse oximeter.

Intended Use

The Kenek Edge pulse oximeter by LionsGate Technologies (LGThealth) is an iOS app (available from Apple App Store) and audio-port finger sensor (available from Amazon) for spot-checking blood oxygen saturation (SpO2) and heart rate.  The device should only be used while the user is not moving and not engaging in vigorous activity. 

The Kenek Edge pulse oximeter is not a medical device and is not intended or available for sale for medical or clinical use by physicians or patients.

Using Kenek Quick Grapher

Kenek Quick Grapher is a Java Script tool that runs locally on your browser, either on your mobile device or on your laptop/PC.  Due to the restrictions on sharing files on smartphones, it is simplest to send the data from your smartphone to your laptop/PC and then follow these instructions.

Please see the detailed instructions to record and send data to Kenek Quick Grapher.

Generating Overnight Data

The Kenek Edge pulse oximeter records measurements of SpO2 (blood oxygen saturation) and pulse rate. For more details, read this article on  how to record measures, change the period of recording, and to export data  or this article for tips on  extended or overnight recording .

How To Graph Your Overnight CSV Data

The Kenek Edge pulse oximeter records and exports comma separated values (CSV) data files.  They can be read by a human or imported easily to a spreadsheet program.  Here we provide step-by-step instructions on how to use the Kenek Quick Grapher.

1. On the Kenek Edge app for iPhone, record and export data on the Oximeter screen (see the links above)

  •      Mail the CSV file attached to the exported data to yourself

2. On your PC or laptop, open your email client and save the file to your desktop

3. Open a browser on your PC/laptop and go to the URL below - it should say Kenek Quick Grapher at the top

4. On the browser page that appears, click the Browse button - select the CSV file you saved to your Desktop

5. The Kenek Quick Grapher will then show you graphs of your data as well as statistical summaries

Understanding the Graphs and Charts

A variety of charts, graphs and statistics are generated by Kenek Quick Grapher.  

A typical graph is shown below - each component of the graph is numbered in red:

  1. Print button - prints the entire report on your printer
  2. Detail level - increase number to smooth data (10 is default)
  3. Show SQI - optional graphing (in grey) of signal quality index
  4. Graphs of SpO2 and heart rate - shown for selected data; axis on the left is both % SpO2 and bpm; axis on bottom is time; optional SQI
  5. Data selector - move the knobs on left and right to select data shown in 4.
  6. SpO2 distribution charts - cumulative density function on the left; probability density function on the right
  7. Heartrate distribution charts - cumulative density function on the left; probability density function on the right
  8. Recording time - statistics of the recording time for this data
  9. Max/min/mean - maximum, minimum and typical values for SpO2 and heart rate
  10. SpO2 Events - amount of time, number of events (changes), and mean time per event at each level of SpO2
  11. Expanded graphs - SpO2 and heart rate shown in detail; optional SQI
  12. (not shown at bottom) Metadata - details of the app version, date of export, email address, Apple device

About CDF and PDF Graphs

Cumulative Density Function (CDF) and Probability Density Function (PDF) graphs can be challenging to understand if you are not a statistician.  However they offer some useful insights that anyone can benefit from.

You can learn a lot more about PDFs and CDFs by checking out Wikipedia or the Khan Academy.  But you will find that the descriptions get very mathematical, very fast.  This article simplifies the meanings to make these graphs accessible for non-mathematicians.  

Unlike the other graphs, CDFs and PDFs are not about time - they show all the data together, representing the whole collection of measurements.  Their shape is useful way to make comparisons between data sets and help focus on features related to the entire distribution of data, instead of specific events in time.

In brief, the probability density function graph (PDF for short) is the one on the right that shows how often (or how probable) a given value appears in your data.  In the example above, the blue column for SpO2 = 97% is the tallest compared to the rest of the columns.  That means it is the most common value in the dataset and that SpO2 level most often appeared during the recording.

In the blue SpO2 values, there is one peak, around which the rest of the data is scattered.  In the green heart rate values, there are two fairly clear peaks - this says that there were two distinct values around which the person measured most of the time - a lower rate of around 57 and a higher rate of 64.  Most of the measures are scattered evenly between the maximum and minimum.

The cumulative distribution or density function (CDF) graph is on the left - blue on top for SpO2 and green below for heart rate.  It indicates how much of the data falls below a given measurement.  Reading from left to right on the bottom of the CDF for the heart rate, there was no data (zero) below the value of 52 - so that is the minimum.  As the graph goes to the right, it linearly (in a straight line) increases until it reaches all data (one) at around 69 - that is the maximum value in the data set.  All of the data falls between 52 and 69.  The shape of the graph shows how the data is distributed between the maximum and minimum - either bunched at one side or the other (see the PDFs) or scattered evenly between the limits.

In the example, the shape of the SpO2 CDF is quite different from the heart rate CDF - it rises slowly at first and then very quickly to the halfway point and then slows down again as it finally reaches one.  This suggests that the data is bunched in the middle (as seen in the SpO2 PDF).  The heart rate CDF is fairly flat and rises evenly from zero to one - suggesting that it is evenly distributed between the maximum and minimum. 

CDFs and PDFs provide distinctive overviews of the whole data set.


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