Monthly Archives: February 2015

Using the Parasympathetic Flatline

Using the Parasympathetic Flatline I analyzed a discussion with a colleague. I was following up on a topic that was not controversial. We had discussed this topic about a month prior. The colleague and I get along in a positive way.  So this should have been a relatively stress free and short meeting.

I used Marco Altini’s Heart Rate Variability Logger and a Polar H7 heart rate monitor to gather the base data while recording the meeting with my smart phone. Pulling that data into a spreadsheet I used my Parasympathetic Flatline model to determine at what point in the meeting I was experiencing physiological stress. I pulled the recording, the heart rate variability readings and the transcript into a timeline graph.


What we see here is when I talked (green), when my colleague talked (blue) and when I was experiencing a physiological event of what I call Parasympathetic Flatline (red), or stress. There are specific points in the discussion when I was amped up, but they were not what I expected.

I had a hypothesis that I was entering these states when I was putting myself “out there,” however I had one moment about 3/4 of the way through the meeting where I really pushed the boundary of a sensitive topic but I did not experience stress. I was synched with my colleague and the discussion did not trigger stress at that point.

There were four points in the discussion, however, that I entered a heightened state when I wanted the conversation to go in a different direction. My agenda was not being followed. In the first two, early in the conversation, I wanted to hear the answer to the question more directly. I was impatient. In the second two, I had the information I needed and wanted to wrap up.

It appears that difficult topics are not stress inducing when discussing them with a colleague when we are in synch, but my overall judgements about the progress of the discussion seem to trigger an aroused state. It is our judgements about the situation that may be the source of stress.

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Testing the Parasympathetic Flatline

Last week I proposed measuring a Parasympathetic Flatline (PS Flatline) where at least ten successive heart beat intervals were close together. This differs from using 30 second averages for rMSSD, a time based measure of average intervals. I wanted to to a side by side comparison to see which of these methods more accurately could point me to moments when I triggered a shutdown of my Parasympathetic Nervous System which allowed the fight or flight reaction to run things.

I measured several meetings where I was able to audio record the proceedings while measuring my heart rate. I used my Android phone to record the audio, a Polar H7 heart rate belt to pick up my heart rate and Marco Altini’s Heart Rate Variability Logger to capture the data. Afterward, I downloaded the csv files for both the rr intervals and the 30 second rMSSD.

The meeting was 55 minutes long and included one person in the room with me and one person on the phone. We were discussing a topic I was comfortable with and an activity I had experience doing. I was briefing my colleagues on a time schedule and details. I was in a relaxed state going into the meeting and did a short two minute breathing reset using BreatheSync prior to the meeting.

In previous studies I had determined that a 30 second rMSSD under 48 was probably a stress state. I had used several stress events to create this baseline. When looking at this meeting, however, I was disappointed to see that using this method definitely overstated the number of stress states. The graph below shows blue bars where I had a 30 second rMSSD under 48 and you can see it shows I was in that state for much of the meeting.


This was not my experience of the meeting. I had the facts to hand, we had a good discussion and overall it was a friendly, informative discussion. So the 30 second rMSSD had not done the job for this meeting. I then looked at the PS Flatline that would show when my beat to beat intervals were less than 17 milliseconds for 10 consecutive beats. Here is what the meeting looked like using the PS Flatline.


Listening to the audio I could definitely see that this method caught moments that had me shifting into high gear. I chose this meeting because there was a point at the fifteen minute mark where my information was completely wrong and I felt the flush of embarrassment. This came through accurately in the chart. It also showed several moments where I was trying to calculate sums with an audience and I needed to shift into gear and focus. You can see on the chart different elements of the meeting I heard in the audio and my memory of what was happening.

So the PS Flatline approach seems to be much more accurate though I have to do further analysis on other meetings to ensure it is catching all of the stress trigger events. I have 10 additional meetings recorded with audio and HRV so i will start crunching those number next.

Finding the Parasympathetic Flat Line

From the start I have wanted to pinpoint the moment I was stressing out and identify the causes. I started with logging Upsets, then moved to using different devices to read Heart Rate Variability (HRV). I was always bothered by the lack of precision in how the devices give insight into HRV. They never said “you were stressed from the 15th to the 45th second.” Rather, they gave an average score over a longer and generalized period of time. I want to nail down the specific time my physiology starts and stops going berserk. To understand precisely when this is happening I have to look at  RR Interbeat intervals (pictured below) and find those periods in the readings where I have multiple consecutive intervals with very little variability.


When in stress mode the distance between successive beats for multiple beats remains very nearly the same. This occurs when the Parasympathetic Nervous System (rest & digest) flat lines and lets the Sympathetic Nervous System (fight or flight) run the show. Here are graphs of my RR Intervals for a similar time period using slow breathing to create a calm state described in last week’s blog post and a session on an elliptical where I was exercising and my heart rate was 145 beats per minute.


You can see that the RR Intervals vary while calm, and there is no variability at all while exercising. While running the Sympathetic Nervous System has the hammer down. In relaxation the Parasympathetic Nervous Systems is braking the machine and providing periodic slowdown. That means that even while resting and digesting our RR interbeat intervals are close to the same values for 32% of the time (red circled areas).


When reading HRV the fundamental output is the RR interval. All analysis is derived from that one string of numbers which are simply the number of milliseconds between beats. So it is straightforward to find periods in readings where those intervals are close together. Looking at the raw data I hypothesized that the Parasympathetic Nervous System is flat lined when the variance is small for 10 consecutive beat to beat intervals.


I then looked at how a rule of 10 consecutive intervals would work for my readings of the calm state and while exercising. The maximum number of low variance intervals in the calm session reading was 7 consecutive beats, and while exercising there was no variance in more that 5 beats. So if I gave each interval a value of “1” if it was in a group of of 10 ore more intervals with low variance and a value of “0” if it was not in such a group, the graphs of the calm and exercising sessions would be as seen below. No intervals are in a group of 10 low variance readings in the calme state, all intervals in are a group of 10 or more for exercising.


The second half of the calculation is the definition of “low variance.” I proposed in my post on HRV and Stress Free State that 25 milliseconds was low. So I took the rule set that the I was in berserk status when 10 consecutive intervals were under 25 ms and graphed if for a meeting I participated in last week. That graph shows more of the meeting in high vibration than I remember and didn’t quite look right to me. I lowered the number to 15 milliseconds and the amount of unrestrained Sympathetic activity seemed to get too small. Not a very rigorous sensitivity analysis I realize, but I have to pick a start point that seems to somewhat resemble what I remember happening.



I set the calculation for 17 seconds and the graph started to look like general stress cadence of the meeting as I remembered it. Fortunately, I had audio recorded the meeting!  So I went back and listened to those portions where it looked like my Parasympathetic Nervous System had stepped aside.



In those three portions of the recording I could hear in my voice that I was in a state of high vibration. In the first case I was presenting something and I sounded unsure of myself. In the second I sounded confident, but my cadence was noticeably slower and it sounded like I was searching for words at times. In the final case I actually said “I don’t understand your question” and there was a bit of confusion.

So I have a base that I am now going to start running data through to see if I can validate the 10 consecutive beat, 17 millisecond ruleset. If it starts identifying points of stress with precision I will have a framework that can help me start creating preparation regimes for 1:1 interactions based on precise knowledge of stressor that flatline my Parasympathetic Nervous System.


Stress Free State Through Lens of HRV

I have been collecting data on my Heart Rate Variability (HRV) to determine how to improve my personal performance and reduce stress. In doing so I have come to doubt the usefulness of “stress measurements” touted by some wearable devices and apps. They claim to be able to measure your physiology and indicate when you are stressed. My experience has been that the readings and the physiology is much more complex than a simple “red, amber, green” meter can indicate. It came to a head recently when I had a nicely engaged conversation with a colleague and my physiology readings indicated signs of stress. In fact, the experience had not been stressful at all. We were working hard on solving a problem and I felt engaged.

So I wanted to get a baseline by looking at data where I knew there was no stress at all. I had an HRV reading from an eight minute meditation session where I was completely relaxed and had nothing to distract me. The graph below shows how my heart rate stayed in the range from sixty-four to seventy-four beats per minute, a normal relaxed range for me.


The heart rate is negatively correlated to HRV, meaning that the body when in a state of arousal or stress will elevate the heart rate and naturally lower the amount of variability between beats. The variability in heart rate is examined by looking at the intervals between beats. Here is a graphic showing the “R-R Intervals” and how they vary. You can see in this example how the heart speeds up and slows down beat by beat.

interbeat intervals

For the eight minute session my RR interval steadily rose and fell. In this graphic I display the distance between beats for the entire period. You can see the distance increase and decrease regularly.


What you see here is my heart rate following my inhalations and exhalations. When we breathe in our heart accelerates and variability decreases. When we breathe out our heart decelerates and variability increases. During this exercise I used a breath pacer set for one breath every ten seconds. This graphic shows one ten second interval where I took one full breath in and out. You can see how the intervals drop on inhalation, rise on exhalation.


When all of the interval changes are put side by side you can see that in a stress free state the changes are regular. The following graph shows the changes both positive and negative in the differences in intervals during the entire eight minute session. Notice that on the outward breath, when the variability is positive, that the scale of the changes are larger than on the inward breath when the variability is negative.This means that the breathing outward has a powerful effect on the overall measured averages of HRV readings.


The averages of the readings, expressed best in the time based measure rMSSD, show for this session a settling at at rMSSD of 55 which is relaxed as any average reading for me indicates no stress when above 48.


But this reading does not give a good picture of what is actually happening physiologically. It looks like in a relaxed state the rMSSD stays above 48 and implies that if I drop below 48 I will be stressed. So a natural reaction would be to try and devise methods that would keep the rMSSD above 48. But this is an average and obscures how variable the underlying heart beats are.

Looking at those successive heart beats where the interval change is “close” or under 25 milliseconds. These beats would indicate a stress response for a lot of the session, but we know this period was stress free. In this graph you see the blue lines are those intervals under 25 milliseconds, and the white spaces are those intervals over 25.


My conclusion is this. In the drive to make wearable devices and apps more palatable to a mass audience the natural tendency is to bring the simplest interface to indicate stress or relaxation, be it a color or a score. This is useful for directing relaxation activity for beginners, and as the quantified self enthusiast digs into the underlying data much more nuanced picture emerges. What the last graph shows is the in the most relaxed and meditative state possible my beat to beat “score” was low for 41% of the beats. The fact is that these low score beats are part of healthy variability that goes both high and low. It isn’t right to see it as “59% good and 41% bad” –  it more correct to understand the mix of highs and lows, short term variability and the incredible speed with which the body becomes aroused and can calm itself. Sadly, the Apple Watch will not solve your stress problem. Data collection, repeated measurements and comparison with experience is the only way forward.