My Worst and Best Nights of Sleep

In the previous post I noted that my monthly average ZQ over the past 3+ years (August 6, 2009 to December 19, 2012) has regularly hovered around 90, with the highest monthly average being 96 and the lowest 86.  Now, looking more closely at the data, I can see that my worst and best nights of sleep varied quite dramatically from that median, in both directions.


The ZQ is the Zeo sleep monitor’s overall sleep quality score, a number that accounts for total amounts of REM sleep, deep sleep, and time awake during sleep.  Using this metric, I was able to identify the extreme ends of my overall range of sleep.  My lowest ZQ was 57, on November 9, 2009.  On that night I slept a total of 4 hours and 48 minutes, with 1 hour 10 minutes of REM sleep, 55 minutes of deep sleep, and 5 minutes in waking.  My highest ZQ was 127, on January 7, 2012, when I slept a total of 10 hours 59 minutes, with 3 hours 37 minutes of REM sleep, 1 hour 27 minutes of deep sleep, and 0 minutes in waking.

Here are the 16 lowest ZQ nights from the last three years:

57       November 9, 2o11

58       January 21, 2010; August 29, 2011

60       September 16, 2011; July 16, 2012

62       October 3, 2012

63       June 16, 2012; September 10, 2012

64       December 16, 2009; May 20, 2010

65       August 12, 2010; September 9, 2011; September 12, 2012

66       November 4, 2011

67       March 15, 2010; January 6, 2012

I still need to double-check to see that none of these low ZQ nights was the result of faulty technology.  But assuming they’re all valid records of a given night’s sleep, I immediately notice a few things.  First, there are no consecutive nights of very low ZQ.  The closest are September 10 and 12, 2012, when my children were in the midst of ending summer and starting the new school year.   Second, many of the lowest ZQ nights came in the late summer and fall of 2011, when my family was moving to a new city.  It makes sense that my sleep would be especially disrupted during that transitional time.  Third, the low ZQ on October 3, 2012 was very likely due to my anxious feelings the night after the first Presidential Debate of the 2012 campaign between Barack Obama and Mitt Romney.

I suspect that some of these low ZQ nights came before an early-morning departure for a trip, or following a late and stimulating social event.

Overall, it looks like nearly all the low ZQ nights have some direct connection to an unusual circumstance in my waking life.

Here are the 13 highest ZQ nights from the last three years:

127     January 7, 2012

125     November 21, 2010

123     November 22, 2009

120     November 23, 2011

119     January 8, 2012

117     November 16, 2011; January 9, 2012

116     November 22, 2010

115     November 23, 2010, November 24, 2010, July 15, 2011

114     October 21, 2010, January 21, 2012

My highest streaks are January 7-9, 2012, November 21-23, 2011, and November 21-24, 2010.  Not surprisingly, all of these nights of very high ZQ took place during vacations at the same quiet and beautiful oceanside resort.  No mystery there!  Late fall and early winter seem to be the prime times for high ZQ, though I’ll have to separately analyze nights on vacations vs. nights at home to see if the influence comes from the changing season (longer nights) or the fact of being on a holiday trip, or some combination of the two.

It looks like the biggest rebound from a low to a high ZQ night occurred on January 6-7, 2012, when I went from a 67 to a 127.  The first night was prior to an early-morning departure for a vacation, and the second night marked the beginning of that vacation.

Again, my overall impression is that the highest ZQ nights occured in direct relation to certain kinds of unusual waking life circumstances at that time.

Here, it seems, is another instance of deep consistency in my sleep patterns. My worst and best nights of sleep deviate by a comparable degree from my normal ZQ baseline of around 90.  My worst nights are 30+ lower than the baseline, and my best nights are 30+ higher than the baseline.

Next step is to look at possible connections with dreams….


1001 Zeo Nights

Last night (December 19, 2012) I recorded my 1001st night of sleep data using the Zeo sleep monitoring system.  I first started using the Zeo on August 6, 2009, and have worn it 81.25% of the time since (1001 out of 1232 nights).  Most of the non-Zeo nights have been due to miscellaneous technical problems.  I was surprised at how quickly I acclimated to wearing the headband while sleeping, and I have worn it consistently throughout this period, even on the non-Zeo nights. I’m confident that my data, even though it excludes roughly 1 out of every 5 nights, fairly represents my sleep experiences during this time.


The stability of my sleep patterns jumped out at me when I first reviewed the data.  For more than three years the basic elements of my sleep–the amounts of REM, deep, and light sleep–have remained very consistent.  A typical night includes approximately 30% REM, 15% deep, and 55% light sleep.  These percentages vary to a degree, but I found the same fundamental proportions (something like 1/3 to 1/6 to 1/2) in nights of very short total sleep as well as nights of very long total sleep.

Using the Zeo’s aggregate sleep quality score, the ZQ, my monthly average has hovered around 90 for this entire period.  My highest monthly ZQ was 96, in November 2010 (a year of a particularly restful Thanksgiving vacation) and my lowest monthly ZQ was 86, in June 2010 (of time of moving houses).  My average ZQ was between 88 and 92 for 33 out of the 41 months for which I have data.

Over the next few weeks I’ll share more detailed analyses of this collection of data.  During this time I have also been keeping a dream journal (@500 reports), and naturally I’ll be looking at patterns of dreaming in relation to the Zeo sleep measurements.  Soon I should have all this material, Zeo + dream reports, available for anyone to study on the Sleep and Dream Database.

If you have any questions or hypotheses you think I should test with this data, let me know!


Research Suggestions Welcome

The basic functionality of the Sleep and Dream Database is now in place and ready to use.  Some aesthetic tweaks still need to be made, and better export options are in the works, but I’m finally starting to turn my attention from the architecture of the database to its contents.  In coming weeks I will upload several new data sets, including the Hall and Van de Castle norm dreams, a new demographic survey from Harris Interactive, and a collection of reports from a small group of people who have been wearing the Zeo sleep manager device while keeping dream journals.


Looking farther ahead, I’d like to collect dream reports from distinctive individuals and/or groups whose waking life concerns could be studied in light of patterns in their dreams.  For example, I’d love to study the dreams of serious athletes to learn about their visions of victory and fears of injury or defeat.  It would be fascinating to look at the dreams of avid gun owners to understand better the psychological roots of their passion for firearms.  I’d be curious to explore the dreams of both prison inmates and prison guards, to get a deeper sense of life on both sides of the penal system.

Now the SDDb is up and running, these kinds of projects are easier than ever to pursue.

If you have suggestions about types of people you think would be good prospects for new research, please let me know.  Better yet, if you would like to collaborate in gathering and analyzing dreams from specific groups, I’d like to hear what you have in mind.

Dystopian Dreaming

While sitting in the audience and taking notes during the recent IASD conference in Berkeley, I found myself marking several instances where something the presenter said triggered my dystopian imagination.  I confess to being a long-time fan of science fiction and fantasy stories about frightening future worlds controlled by alien invaders, zombie hordes, inhuman technologies, totalitarian governments, and/or rapacious capitalists (I made a list of some favorites below).  I enjoy these stories as literary nightmares: vivid, emotionally intense simulations of real psycho-cultural threats, looming now and in our collective future.


At the IASD conference I realized I could turn this interpretive process inside out.  I began to look at dream research from the genre perspective of dystopian fiction.  What would an uber-villain in such stories find appealing in state-of-the-art dream research?


Let me be clear, these are my own shadowy speculations and in no way reflect anything directly said or intended by the presenters!


Sleep paralysis induction.  There is now a proven technique for inducing the nightmarish experience of sleep paralysis–that is, causing someone to enter a condition in which their bodies are immobilized but their minds are “awake” and vulnerable to terrifying images, thoughts, and sensations.   I can imagine this technique being put to nefarious use by military intelligence agents, state-controlled psychiatrists, and cybernetic overlords.  The ability to trap a person within a state of sleep paralysis would be a horribly useful tool for anyone bent on total mind control.


Transcranial magnetic stimulation.  This technology enables the direct manipulation of neural activity during REM sleep, targeting specific regions of the brain.  If the technology were refined with malevolent purposes in mind, it could potentially disrupt people’s normal dreaming patterns, controlling what they do and don’t dream about.  An evil scientist could thus invent a kind of anti-dream weapon, a magnetic beam aimed at the head of a sleeping person and programmed to stun, control, or destroy.


Disrupting PTSD memory formation.  Trauma victims can diminish the symptoms of PTSD if they perform a series of distracting cognitive tasks with six hours of the trauma, thereby disrupting the formation of long-term traumatic memories.  The future militarization of this method seems inevitable.  Anything that alters memory can be used by evil governments to manipulate people against their will, either to do things they don’t want to do (black ops soldiers) or forget things that have been done to them (massacre survivors).


Remote monitoring of a person’s sleep.  The Zeo sleep monitoring system (which I’ve used for three years) has now developed a wireless version that instantly relays the user’s sleep data from the headband via a bedside mobile phone to the Zeo database.  This kind of technology opens the door to real-time remote monitoring of people’s sleeping experience, and potentially the ability to reverse the flow of data and influence/shape/guide people while they sleep.  If enough people were linked into the system, it could serve police states as a valuable tool in 24-hour mind-body surveillance.


My interest in these morbidly malevolent scenarios is not entirely theoretical.  Over the past few years of developing the Sleep and Dream Database I’ve been thinking of the darker possible applications of this technology, less Star Trek and more Blade Runner.  If it’s true, as most researchers at the IASD are claiming, that dreams are accurate expressions of people’s deepest fears, desires, and motivations, then it’s also true a real potential exists to put that dream-based information to ill use.


Projecting even farther forward, I wonder if there might be some kind of future inflection point where the amount of data we gather suddenly reveals much bigger patterns and forms of intelligence than we had previously been able to recognize or scientifically document.  What would happen if this leap of knowledge enabled our collective dreaming selves to somehow unite to challenge the dominance (one might say totalitarian regime) of waking consciousness?


I think about all this as I continue building up the SDDb, trying to make good decisions and avoid the nightmare pitfalls.  Dystopian fantasies help me clarify what’s at stake, where the dangers lurk, and how the future may unfold.


You may be familiar with Arthur C. Clarke’s 1953 science fiction short story “The Nine Billion Names of God.”  If so, you’ll understand why, as I work on developing new database technologies for dream research, I meditate on the phrase, “The Nine Billion Dreams of God.”




Dystopian Films and TV: Blade Runner, 12 Monkeys, Children of Men, Logan’s Run, The Matrix, Soylent Green, V for Vendetta, Battlestar Galactica, The Prisoner, Gattica, Terminator, Alien, Total Recall, 28 Days


Dystopian Novels: The Hunger Games, Fahrenheit 451, Neuromancer, 1984, Brave New World, The Time Machine



Zeo Sleep Data and the Ur-Patterns of Dream Content

So far I’ve done word search analyses on 20 series of dreams from individuals and 9 sets of dreams from large groups of people, a total of more than 18,000 dream reports. It’s too early to say anything definite about the patterns that have emerged from this data. More reports need to be gathered from a wider variety of people, and more improvements need to be made in the SDDb word search template.

Still, a few basic patterns have appeared in nearly all the collections I’ve studied. I’m calling them ur-patterns because they seem to represent deep structural elements of dream content (ur- as in “original” or “primal”). That’s my general hypothesis, anyway, and each new set of dreams is another chance to test and refine it.

Here are the ur-patterns I’ve identified so far:

  1. Of the five senses, sight words are used most often, smell and taste the least.
  2. Of the five major emotions (fear, anger, sadness, confusion, happiness), fear words are used most often.
  3. Of all the categories of cognitive activity, speech words are used most often.
  4. Of the four natural elements, water words are used most often.
  5. Falling words are used more often than flying words.
  6. There are more references to family characters than animal characters, and more to animals than to fantastic beings.
  7. There are more references to friendliness than physical aggression.

Looking at the KB DJ 2009-2010 series with Zeo sleep data (available at google docs), a scan for these patterns finds good but not perfect evidence for each one.

Vision-related words are used more frequently across all the Zeo measurements, with smell and taste words almost entirely absent. Fear words are used more frequently than other emotion words. Speech words are the most used among the cognition categories, and water is the highest among the natural elements, though earth is a consistently high second. The usage of falling words is always higher than, or equal to, flying words.

The family > animals pattern > fantastic beings was not as clear-cut. Fantastic beings always had the lowest word usage, but animals were not always lower than family. When the names of the dreamer’s immediate family were added to the search for characters, the total frequency of family-related words rose higher than the usage of animal words in 15 of the 17 subgroups.

The friendliness > physical aggression pattern was not perfectly evident either. In part this is due to a “false positive” problem in the SDDb template. The word search category for physical aggression includes the word “bit,” which the dreamer used in almost 10% of all the reports as a term meaning “small amount,” not a physical bite. I’ll provide revised numbers once I’ve fixed this. For now, looking at how often the word “bit” is used in each Zeo subgroup, it appears the physical aggression frequencies will drop below the friendliness frequencies in most, but not all, subgroups.

In sum, the ur-patterns appear across virtually all the subgroups of Zeo sleep measurement. No matter what aspect of sleep was measured, the dream reports used the same basic frequencies of words in several major categories. High or low proportions of sleep did not correlate with any major change of dream content, at least at this level of analysis.

In future posts I’ll look at the few variations from these patterns (high physical aggression, animal, flying, and earth references) in relation to the dreamer’s waking life concerns, taking the possibility of metaphorical meaning into account.

I will also look at each of the five types of Zeo data and see if I can identify any particular variations that rise to the level of statistically significant correlation. If any such correlations emerge, they may guide us toward specific areas where a measurable aspect of sleep does interact with basic patterns of dream content.


High Zeo Scores, Big Dreams

The week before Thanksgiving I went on a family vacation to Mexico, just as I did last year.  My two all-time highest Z scores have come from these vacations–123 on the second night of the trip last year, and 125 on the first night of the trip this year.  Both times I was in a big sleep rebound mode, having slept poorly the previous few nights before traveling.  Both times I was in a very dark room and had absolutely nothing to do when I woke up.  In the 15 months I’ve used the Zeo, these were the most predictably opportune times for a maximal amount of sleep.  I’m glad to see the Zeo technology is capable of accurately representing the distinctive features of these kinds of unusual sleep experiences. 

My next highest Z scores came the two nights afterwards in both cases–116 and 116 following the 123 last year, and 116 and 115 following the 125 this year.  That third night this year, with the 115 Z score, I woke up from a very intense and elaborate dream.  I immediately looked at the Zeo clock–it was 3:33 am, which struck me as an odd bit of symmetry. 

I’ve transcribed the dream below.  It’s one of the longest I’ve had in quite a while.  Over the years I’ve had a few dreams similar to this, with an epic cinematic feel and lots of reality/perspective shifting.   My phobia about New York City is on full display here! 

Looking at my Zeo sleep graph for that night, I see that entered a REM phase at 2:25 am that lasted until 3:20 am.  Then I entered a brief period of deep sleep until 3:25 am.  After another bit of REM sleep at 3:30 am I was awake by 3:35 am.

I’m usually done with my deep sleep by midnight or 1 am, so it’s unusual to see the 3:25 am deep sleep.  Since dreaming is usually associated with REM sleep and stage 2 NREM sleep (part of light sleep for Zeo purposes), I wonder if  a) my dream came during the long REM phase ending at 3:20 am, or b) it was all condensed into the short REM phase at 3:30 am, or c) I started dreaming during the long REM phase and kept dreaming through the dip into deep sleep, back into REM and then awakening at 3:33. 

If it were true that the dream continued from REM through the deep sleep phase and back into REM, perhaps that might be related to some of the dramatic shifts and metamorphoses appearing at various points in the dream narrative.

Trying to Escape in New York City

I’m in New York City, walking down a street crowded with people….I go into a weird alley area….fences and garbage and strange people….I see a guy hunched up behind a garbage can in the shadows….I try to get back to the regular street, but to do so I have to climb through a hole in the fence, then try climbing up and over another fence….it has barbed wire at the top, so I’m stuck….as I try to do all this, I get in worse trouble, the bad guys see me caught up….Then I go to an inside place where the top bad guy is sitting at a table….there’s no way for me to escape….they’re laughing and talking, and I join in, but there’s a sense of menace in the air, I’m very scared….at one point one of the guys grabs at my balls, I jump back in alarm….they have total control over me….then cops come after the bad guys, and we all flee….it becomes an endless chase, with me trying to get away….but I can’t….at one point I find myself watching the filming of a Clint Eastwood movie about World War II, with planes swooping over a big canyon area and shooting at soldiers below….it’s like this place belongs to Mayor Bloomberg, he’s letting them use it for the movie?….Then I see Japanese guards with pick axes attacking people….they’re coming after me, too?….I run….at some point there’s a parade through the NY city streets….My friend E. is in the parade, he goes forward with the rest, with a big truck behind him….I keep running….we go inside buildings, and now I’m trying to help the bad guys; the cops are worse, they’re our mutual enemy….I could get away, but I’m part of the group now….it’s like we’re in a big department store….at one point we swing on elastic straps hanging down from the ceiling lights….we fly through the air, swinging around to get away….there’s a flashback to when the main guy lost his son to the cops?….that explains why he hates them so much….a few of us run down an escalator and hold it steady for the rest to come quickly, while I go ahead and find an emergency exit….but it turns out to be a weird orange or red room with people in it, like a party place….not an exit….so I go back out….I run through the garage of a fire fighters station, and think about taking their uniforms to disguise us….but no….then outside, we seem to have a moment where the cops don’t know where we are….I sit down at the table of a sidewalk cafe, with the top bad guy….other people just left the table, so we sit there as if we’re normal….the cops ignore us here…but the waiter knows the top bad guy….they’re lovers?….now the table is like a pool of water, and the top guy and I are floating in it….the waiter guy turns into a weird creature or machine, and he goes hunting/searching through the building for information to help us….At one point I drop my wallet, I see it back by a garbage can….I quickly dash down to get it before someone else grabs it….at another point I’m hiding behind a garbage can, just like the guy at the beginning….toward the end my wife is there, she’s skeptical and disapproving of all the mayhem and property damage that’s being caused….then it’s like the end of a movie….I miss the final credits, and I ask my daughter what the last line said….she says, “thus ends the tale of Don Quixote”….oh, I realize the whole thing has been an adaptation of the Don Quixote story….the main guy is Don Quixote, and some little guy is Sancho?….the end, which I’m watching rather than being in, involves the final destruction and death of all the bad guys from the cops, who are robots or machines now….no way to stop them, their dominance is inevitable….now the movie is over, we walk back through the rubble of the city….My mother-in-law is with us?….she and my daughter are walking fast ahead of us….I’m worried that we need to get out of here quickly, we’re in a weird city place that’s dark and filled with rubble, I don’t want to have trouble for real….