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Review Clinical Publications, Introduction to SleepImage, and FAQs to learn more about SleepImage.

Clinical Publications

SleepImage has been thoroughly validated in peer-reviewed publications.  Click on a topic to access its content.

Introduction to SleepImage

SleepImage is a clinical sleep evaluation, diagnostic and management tool like no other.
SleepImage is FDA-cleared Software as a Medical Device (SaMD) that uses a single sensor device to collect the data, making it the easiest to use and lowest cost clinical sleep diagnostic device available, while being FDA-cleared to display an automatically generated Apnea Hypopnea Index (AHI), that is comparable to AHI from Polysomnography (PSG).
SleepImage is a clinical tool that can improve patient care across all specialties.

             Introduction to SleepImage


About SleepImage



The SleepImage System is patented technology that is Food and Drug Administration (FDA) cleared Software as a medical Device (SaMD) that establishes Sleep Quality and measures Sleep Duration. The technology is based on Cardiopulmonary Coupling (CPC)- analysis of data, typically collected during sleep, derived from electrocardiogram- (ECG) or photoplethysmogram- (PPG) sensors. The SleepImage System is intended for use by or on the order of a Healthcare Professional to aid in the evaluation and management of sleep disorders in children and adults. The SleepImage System optionally analyzes SpO2 data to measure oxygen desaturation and calculate the SleepImage Apnea Hypopnea Index (sAHI) that is FDA-cleared to aid clinical diagnosis of Sleep Disordered Breathing (SDB) in children and adults.

The SleepImage System is patented, cloud-based and Health Insurance Portability and Accountability Act (HIPAA) compliant software. The SleepImage System optionally graphs accelerometer data, and when recorded on the torso, it can calculate and display body position and snoring.

The SleepImage System output is automatically generated and presented in an easy to understand manner, with test results for each biomarker displayed and color coded along with expected values for health care professionals for diagnosis and management of sleep disorders and related chronic diseases. 


The SleepImage Output

The Sleep Spectrogram and the Cardiopulmonary Coupling (CPC) software generate measures of sleep duration and with the biomarkers of sleep quality and sleep pathology it provides a new and practical approach to trend sleep as a vital sign of health over time and to identify and manage sleep disorders. SleepImage is a useful clinical tool, whether it is to evaluate symptoms of Insomnia or diagnose Sleep Disordered Breathing (SDB), where Obstructive Sleep Apnea (OSA) is the most common type. SleepImage offers an intuitive and simple interface at low cost offering the potential to objectively measure sleep to identify sleep disorders before initiating any therapy. Balanced diet and exercise are important, but sleep is critical for good health and wellness, and sleep evaluation should therefore be integrated in protocol across healthcare specialties. With this type of approach, sleep disorders can be identified accurately and at early stages which can mitigate the development or progression of other chronic diseases. Sleep health management with SleepImage is easy, and being low cost, the test can be repeated in patients’ own sleeping environment over multiple nights and multiple time points to optimize disease management.

The SleepImage Cardiopulmonary Coupling (CPC) software analysis is based on data collected with electrocardiogram (ECG) or photoplethysmogram (PPG) sensor during sleep. Both signals contain information/data on heart (pulse) rate variability (HRV/PRV) as a measure of autonomic drive (sympathetic and parasympathetic) and respiration, that both are strongly modulated by sleep. The SleepImage software utilizes mathematical and frequency analysis to calculate the synchronization between HRV/PRV and respiration to provide visualization of sleep in the Sleep Spectrogram and reported numerically as sleep quality, sleep duration, sleep apnea, fragmentation and periodicity.

Sleep Quality Index (SQI) is a summary index of the CPC biomarkers of sleep quality, sleep stability, Fragmentation and Periodicity and provides a meaningful unit of measure of sleep health. The SQI is displayed on a scale of 0-100 and with expected values for both children and adults.  The SQI can be used as a “unit of measure” for sleep health that is useful to track sleep health over time, whether to identify the need for further clinical investigation or to track therapy.  The SQI is easily communicated and relatable for the patient or other lay persons, while at the same time being a summary measure of sleep health that has been clinically validated.

Sleep Apnea Indicator (SAI) provides a measure of SDB and is based on detecting oscillations in cardiac intervals associated with prolonged cycles of sleep apnea, based on Cyclic Variation of Heart Rate (CVHR) during unstable breathing (tidal volume fluctuations in breathing). During each apnea event, blood oxygen decreases and is accompanied by a physiological reaction of bradycardia and, when breathing resumes, a relative tachycardia and thus reflecting hypoxemia. When SAI is presented with the Sleep Quality Index, Fragmentation and Periodicity, it is possible to use the SAI to not only help detect apneas, but also to differentiate between obstructive, central and complex/mixed sleep apnea.

Apnea Hypopnea Index (sAHI) is an automated measure of Apnea/Hypopnea events, following the American Academy of Sleep Medicine (AASM) scoring guidelines of the Apnea Hypopnea Index (AHI) for both adults and children, as mild, moderate and severe sleep apnea. When blood oxygenation data (SpO2) is recorded, the SleepImage System analyzes the SpO2 data to generate desaturation events, display an SpO2 graph and automatically calculate SleepImage Apnea

Hypopnea Index (sAHI) by combining other CPC-biomarkers and hypoxic events where a qualifying event is characterized by a minimum of ten (10) seconds in duration and a 3% oxygen desaturation during sleep time measured by CPC. The automatically generated sAHI is FDA-cleared to be comparable to AHI scoring from polysomnography (PSG) studies to aid diagnosis of

Sleep Disordered Breathing (SDB) in both children and adults.

Fragmentation (eLFCBB) is a subset of low-frequency coupling, it is an indicator of pain, upper airway resistance and Obstructive Sleep Apnea (OSA).

Periodicity (eLFCNB) is a subset of low-frequency coupling, it is an indicator of periodic type breathing and Central Sleep Apnea (CSA).


The SleepImage Apnea Hypopnea Index (sAHI) and the Apnea Hypopnea Index (AHI), both following the American Academy of Sleep Medicine (AASM) guidelines

The SleepImage Apnea Hypopnea Index (sAHI) is an automated measure of Apnea/Hypopnea events, following the American Academy of Sleep Medicine (AASM) scoring guidelines of the Apnea Hypopnea Index (AHI) for both adults and children as mild, moderate and severe sleep apnea. When blood oxygenation (SpO2) data is recorded, the SleepImage System analyzes the SpO2 data to generate desaturation events, display an SpO2 graph and automatically calculate SleepImage Apnea Hypopnea Index (sAHI) by combining other Cardiopulmonary Coupling (CPC) biomarkers and hypoxic events where a qualifying event is characterized by a minimum of ten (10) seconds in duration and a 3% oxygen desaturation during sleep time measured by CPC. The automatically generated sAHI is FDA-cleared to be comparable to AHI scoring from polysomnography (PSG) studies to aid diagnosis of Sleep Disordered Breathing (SDB) in both children and adults.

The sAHI, like the AHI, reports the number of paused breathing events during the sleep period. Events are displayed based on CPC sleep states (Stable and Unstable NREM sleep and REM sleep). Additionally, events are presented concurrently with and without cyclic variation of heart rate (CVHR) to aid in clinical interpretations and management of SDB.  When reviewing the overall sAHI score, it is recommended to consider SDB events concurrent with CPC sleep states to evaluate and determine disease severity and for differential diagnosis of Obstructive (OSA), Central (CSA) or Complex/Mixed Sleep Apnea, using the pathology biomarkers of Fragmentation (elevated low-frequency coupling broad band, eLFCBB), that is associated with obstruction and Periodicity (elevated low-frequency coupling narrow band, eLFCNB), that is associated with periodic breathing.    


SleepImage (CPC) and Polysomnography (PSG)

Over the last decades, Polysomnography (PSG) has been the most widely used clinical measure of sleep where sleep is described as NREM sleep and REM sleep based on Electroencephalogram (EEG) morphology. NREM sleep is presented in 3 sleep stages and REM sleep is also often referred to as dream sleep.

SleepImage, Cardiopulmonary Coupling (CPC) is based on the physiological changes in the autonomic nervous system that occur during sleep. It integrates information from the brain electrical activity through the autonomic nervous system. Analyzing heart rate variability (HRV) coupled with respiration, CPC captures the essence of sleep by looking at the ebb and flow of slow wave power that is the accepted marker of sleep drive in humans and in non-human species.  The CPC-method does not rely on the same data streams as PSG and the output is not meant to match PSG, it however complements conventional sleep staging, albeit with a different method of categorizing sleep.  Rather than being dependent on manual interpretation, primarily of EEG morphology, the automated output reveals that NREM sleep has a distinct bimodal-type structure marked by distinct alternating and abruptly varying periods of high and low frequency CPC-power. High frequency coupling (HFC) or stable sleep occurs during stage part of NREM-2 and all NREM-3 and is associated with periods of stable breathing, non-cyclic alternating pattern (n-CAP) EEG, increased absolute and relative delta power, strong sinus arrhythmia and blood pressure dipping. Conversely, low frequency coupling (LFC) or unstable sleep occurs during NREM-1 and part of NREM-2 and has opposite features and is characterized by temporal variability of tidal volumes, cyclic alternating pattern (CAP) EEG and non-dipping of blood pressure, lower frequency cyclic variation in heart rate. CPC defines REM sleep into Stable and Unstable REM sleep based on frequency analysis of how the dominant CPC state has been classified as vLFC, where fragmented REM sleep is often accompanied by elevated Low Frequency Coupling.

In a healthy sleep pattern, cycles between Stable, Unstable and REM sleep (Stage 1, 2 and 3 NREM – REM sleep cycles on PSG) occur every 30-90 minutes and approximately 4-8 cycles occur during an 8-hour healthy sleep period.  The ratio of NREM sleep to REM sleep in each cycle varies during the course of the sleep period. The first episode of REM sleep may last only a few minutes, but time-period spent in REM sleep increases progressively over the sleep period, with the final period of REM sleep that may last up to 30 minutes.  In summary, Stable sleep (NREM slow-wave sleep) is prominent in the first third of the night and REM sleep is prominent in the last third of the night.

Stable & Unstable Sleep (NREM sleep)
NREM Sleep accounts for 75-80% of the sleep time. During this phase, thinking and most physiological activities slow down, but movement can still occur.

Stage 1 NREM sleep = Unstable Sleep – accounts for 3-8% of total sleep period, each period is about 5-10 minutes long and occurs most frequently in the transition from wakefulness to the other sleep stages or following arousal during sleep.  In stage 1 NREM sleep, alpha activity, which is characteristic of calm wakefulness, diminishes and low-voltage theta waves appear on EEG.   While in stage 1 sleep, people lose awareness of their surroundings, but they are easy to wake up.

Stage 2 NREM sleep = Unstable & Stable Sleep – accounts for 45-55% of total sleep time.  This is the first stage of effective sleep and each period lasts about 10-25 minutes. The characteristic EEG findings of this stage are sleep spindles believed to occur when the brain disconnects from outside sensory input and begins the process of memory consolidation and K complexes that are sort of built-in vigilance system that keep you poised to awake if necessary. Delta waves first appear during this period of sleep but are present in small amounts.   Most people spend about half of the night in this stage, where eyes are still, and heart rate & breathing gradually slows down.

Stage 3 NREM sleep = Stable Sleep – accounts for 15-20% of the total sleep period. The characteristic EEG findings of this stage are that slow-brainwaves or Delta waves become dominant.  The brain becomes less responsive to external stimuli, making it difficult to wake up the sleeper.  Slow-wave sleep is the time for the body to renew and repair.  During this sleep stage muscle tone decreases, breathing becomes more regular, blood pressure drops, and pulse rate slows.  Blood flow is directed less toward the brain and at the beginning of this stage the pituitary gland releases a pulse of growth hormone (GH) that stimulates tissue growth.  When a sleep deprived person gets some sleep, he or she will pass quickly through the lighter sleep stages, into the deeper sleep stages and spend a greater proportion of the sleep period in this stage.  This is believed to indicate that slow-wave sleep has an essential role in a person’s optimal functioning.

REM Sleep
REM sleep accounts for about 20% of the sleep time. Dreaming occurs during REM sleep.  The first REM sleep episode occurs 60-90 minutes after the onset of NREM sleep. Characteristics of REM sleep is atonic of skeletal muscle groups, but the brain is actively thinking and dreaming as the eyes move back and forth behind closed eye lids, hence the name Rapid Eye Movements (REM). During this stage heart rate and blood pressure increase and respiration becomes irregular. Despite all this brain and eye activity, the body hardly moves, the motor function becomes “paralyzed”. Like Stable sleep (slow-wave sleep) restores the body, REM sleep or dream sleep restores the mind by facilitating learning and consolidating memories. When a person deprived of REM sleep falls asleep, he or she will enter REM sleep stage earlier and spend a higher proportion of sleep time in REM sleep.



Signal Quality & Abnormal Heart Rhythms

Signal Quality is presented above the timeline. Disconnection or interruption in signal recordings and irregularities in heart rate are displayed by the Signal Quality line.  If the input data is of high quality the line is green. If the input signal has some interruption but still most of the data recorded has good signal quality, the color indicator turns to yellow. If the input signal is fully compromised the signal quality bar turns red. It is recommended that a minimum of 4 hours of predominantly green signal quality should be used for clinical decision-making. Red signal affects the output metrics causing it to be invalid. A red signal quality report section should not be used for any clinical decision-making.

The figures present sections of "noisy" spectrogram where clear separation between staple and unstaple sleep disappears during periods when the ECG recording has a compromised signal quality, in this case irregular heart rate. The total amount of recording time demonstrating PACs will determine the usability of the recording for sleep analysis. 

ECG- & PLETH signal Artifacts - Distorted Baseline
An irregular ECG or PLETH pattern causes the derived respiration signal to be 'noisy' and provides poor data for coupling and the Spectrogram shows dominant LFC during this period. In this case, the LFC has little influence from actual respiration and is therefore of limited clinical value.

ECG & Respiration artifact due to distorted baseline

ECG & Respiration during a period of good signal detection

Compromised Signal
CPC is based on analyzing normal sinus rhythm signals.  CPC analysis should be constrained to areas of good signal quality to ensure meaningful data interpretation and it is recommended to base clinical decisions on studies containing at least 4 hours of green signal.  CPC signal quality is shown on the Signal bar presented below the spectrogram.

A compromised signal is shown as yellow (when there is some noise, but the signal has still mostly normal sinus rhythm) or red (when the signal has lost sinus rhythm or there is no signal).  When there is no data, there is a gap in the spectrogram and the area is excluded from sleep analysis. As shown in the following example (using ECG), at 4:23 am Signal Quality turns red and the Spectrogram shows a corresponding loss of data.  The same applies for PLETH signal loss.


Premature Atrial Contractions (PACs)
PACs are a common cardiac dysrhythmia characterized by premature heartbeats originating in the atria. They are most often asymptomatic and are not considered an abnormal finding but as they cause irregular heartbeat, they can interfere with the CPC algorithm and cause the Signal Quality to turn from green to yellow or red. The result on the Spectrogram is "noisy" causing the clear separation between stable and unstable sleep to disappear during periods when the ECG signal is compromised causing deteriorated signal quality with LFC dominating the picture during periods of irregular heartbeat. (see also PVCs/VECs)

Irregular Heartbeat (PVC/VES)
PVC/VES (Premature Ventricular Contraction / Ventricular Extra Systole), as with PACs are most often asymptomatic and are not considered an abnormal finding but, as they cause irregular heartbeat, they can interfere with CPC analysis and cause the Signal Quality to turn from green to yellow or red.

These ECG and Spectrogram examples are from a patient with known PVCs. The Signal Quality bar shows poor signal quality periods (yellow and red) during this sleep recording. The irregularity in the heartbeat causes the ECG-derived respiration (EDR) signal to be inaccurate respiration data for coupling and the result in the Spectrogram is ‘noisy’ or the data is lost and the recording for that period therefore of limited clinical value.

Figure. CPC signal quality is compromised due to irregular hearth rythm caused by PVC's.


The following examples are from the same recording demonstrating that the subject’s irregular heartbeat causes the variability in CPCs detection of signal quality during the night.

Signal Quality Green: period of good signal detection: 02:50 am

Signal Quality Green: some PVCs but not affecting results: 02:55 am

Signal Quality Yellow: period of compromised signal detection: 03:58 am

Signal Quality Red: period of data loss due to frequent VES: 04:08 am

Figure demonstrating ECG periods of good signal quality (green), periods of partially compromised signal quality (yellow) and fully compromised signal (data loss) due to frequent VES (red). 

PVC/VES may be perceived as a “skipped beat” or felt as palpitations in the chest and is a relatively common event where the heartbeat is initiated by Purkinje fibers in the ventricles rather than by the Sinoatrial node, the normal heartbeat initiator. The electrical events of the heart detected by the electrocardiogram (ECG) allow PVC/VES to be easily distinguished from a normal heartbeat. Most often, PVC/VES are benign and may even be found in otherwise healthy hearts but can also be a sign of decreased oxygenation to the heart muscle.

Atrial Fibrillation
This data is from a patient with known atrial fibrillation. The bar shows poor signal quality (yellow and red). The irregularity in the heartbeat causes the derived respiration signal to be ‘noisy’ and provides poor data for coupling, it does not represent actual respiration and is therefore of no clinical value for a sleep quality or pathology measure.


Spectrogram from a patient with Atrial Fibrillation (AF) and ECG-screenshot presents the lack of the p-wave




About Sleep


SleepImage, Sleep Quality and Sleep Duration

Sleep quality is not the same as sleep quantity. Spending 8 hours in bed asleep and not waking refreshed is a clear indication of poor sleep quality.  Good sleep quality and sufficient sleep are both essential for maintaining good health and well-being.

Prevalence of non-restorative sleep, fatigue and sleep problems is high in the general adult population and the two most common sleep disorders in adults are insomnia and sleep disordered breathing (SDB) with obstructive sleep apnea (OSA) the most common form. These diseases have different etiology and therefore need different therapy approaches. Although etiology and treatment for Insomnia and SDB/OSA differ, both conditions are associated with a higher risk of accidents, absenteeism and onset and progression of other health problems like depression, type 2 diabetes, obesity, cardiometabolic and cardiovascular diseases like hypertension and other cardiovascular risk factors like stroke. Currently it is estimated that around 85% of patient with OSA are undiagnosed, and prevalence of insomnia around 30% in the general population. It is often difficult for both patients and providers, without objective testing, to identify the cause for patients awakenings and their daytime fatigue, therefor with a simple clinically validated sleep assessment tool, like SleepImage, that both assesses sleep quality and detects sleep pathologies like OSA, diagnosis and management of sleep disorders can be improved. This method can therefore be helpful for physicians, to test their patients sleep, to have objective data to discuss before initiating therapy and also to get feedback regarding effectiveness of therapy.

SleepImage is based on Cardiopulmonary Coupling (CPC)- analysis, patented algorithms developed and validated by sleep researchers, using continuous, evenly sampled, normal sinus rhythm electrocardiogram- (ECG) or plethysmogram-signal (PLETH) as the only input requirement. To further enhance the value of the CPC technology, it is optional to collect SpO2 -data to generate SleepImage Apnea Hypopnea Index (sAHI) for diagnosis of Sleep Disordered Breathing (SDB).

During the validation of the CPC technology, output comparison to tens of thousands of Polysomnography (PSG) studies, which is considered the reference standard in sleep medicine, were conducted and a high level of correlation with PSG sleep power mapping has been confirmed. The ebb and flow of slow wave power is the accepted marker of sleep drive in humans and in non-human species. Delta power measured from surface EEG in PSG studies strongly correlates with ECG- or PLETH-derived Cardiopulmonary Coupling Stable Sleep, further supporting a link between cortical EEG electrical activity and brainstem-related cardiorespiratory functions. Please see our Clinical Publications List.



Healthy Sleep – Good Sleep Matters

Getting sufficient good quality sleep at the right times is vital for good health and well-being. Sleep improves mental and physical health as well as quality of life and is an essential physiological process that we can’t’ live without. Sleeping poorly for too long makes us feel exhausted but when getting a good night’s sleep, we feel good and energized. Poor sleep or sleep deficiency can affect how well you think, react and get along with others, affects ability to learn and increase risk for developing chronic health problems.

During sleep the body is hard at work repairing and rebuilding muscles and tissues. Chronic sleep deficiency and poor sleep affects the immune system and is linked to various chronic diseases like cardiovascular- and cardiometabolic diseases, diabetes, obesity and mental diseases.

Sleep is also a time for brain reorganization as memories are consolidated, so upon awakening, learned information is clear and available. Sleep-deprived persons have difficulties focusing, lack attention and therefore cannot learn efficiently, may have trouble solving problems and making decisions and controlling emotions and behavior. Sleep deficiency and poor sleep increases daytime sleepiness and fatigue, which results in increased vulnerability to injury’s and accidents as well as being linked to depression suicide and risk-taking behavior.

Healthy Sleep - Child
This Spectrogram is from a from the analysis of data from a photoplethysmogram (PPG) sensor. Periods of stable sleep dominate the sleep period, are consolidated and good oscillations between stable (HFC) and unstable (LFC) vs. REM/Wake (vLFC) episodes, as would be expected in a healthy child.


Healthy sleep in a child. The sleep recording is approximately 8 hours. SQI = 81 (expected >70), SAI = 0 (expected <2); e-LFCBB = 3% (expected <8%); e-LFCNB = 0% (expected <0%). 

Healthy Sleep - Adult
Sleep spectrogram from an overnight PPG recording with Home Sleep Apnea Test (HSAT). Both AHI and sAHI are zero. Stable sleep dominates the Spectrogram with an expected high percentage of stable sleep (HFC). Long consolidated stable sleep periods (30-60 minutes) with intermittent periods of unstable sleep (LFC) and periods of REM sleep / Wake (vLFC) episodes are present in the approximately 8-hour sleep recording.  Periods of stable sleep are consolidated and good oscillations between stable and unstable sleep is seen. A minimal amount of unstable sleep is present as is expected for healthy sleep.

Healthy sleep in an adult. The sleep recording is approximately 8 hours, SQI = 86 (expected >55), sAHI=4; SAI = 1 (expected <5); HFC = 78% (expected >50%); LFC = 5% (expectede <30%); Fragmentation (e-LFCBB = 1% (expected <15%); Periodicity (e-LFCNB = 0% (expected <2%). 




Intra-night variability of Sleep

Each night’s recording with SleepImage accurately displays a Sleep Quality Report for that night. Sleep is however variable between nights for everyone, making it very valuable to record sleep quality repeatedly over time to learn what behavioral or environmental changes may impact the user’s sleep quality, which are different things for different people.

Night-to-night variability in sleep is well recognized and this variance should be expected to be more prominent between nights in individuals with sleep disorders or the presence of comorbidity.  It is well documented how sleep apnea severity can vary considerably from night to night as has been reported in SDB-patients undergoing PSG-studies on consecutive nights or one month apart, when 18%-65% changes in AHI have been observed. For patients with insomnia, stable sleep tracks slow wave power, SQI that summarizes the sleep biomarkers during each night, and SQI and sleep apnea indicator (SAI) significantly correlate with key Polysomnography (PSG) outcomes of sleep efficiency (SE) and wake after sleep onset (WASO) as Fragmentation detects arousals. Additionally, sleep opportunity, sleep latency and sleep duration when sleep is recorded over multiple nights, could provide new insights into night-to-night sleep variability providing a picture of symptom presentation over time that can aid therapy management and improve outcomes. It is important to treat sleep disorders as other chronic conditions that may present different levels of symptoms over time that need to be taken into consideration for management of sleep disorders.


Effects of Caffeine on Sleep Quality

During wake, neurons in the brain produce adenosine (a molecule found in all cells and involved in providing energy for many biochemical processes), a by-product of the cells’ activities. Adenosine will start to build up in your brain from the moment you wake up in the morning and during the waking period. The longer you are awake, the more adenosine will accumulate. This build-up of adenosine in the brain is thought to be one factor that causes the increasing desire to sleep and promotes the drive to sleep or “sleep pressure.” Most people feel an urge to sleep after twelve to sixteen hours of being awake.

This sleep signal of adenosine and the desire to sleep can however been “muted” by caffeine. Caffeine works by binding to the “adenosine sites/receptors” in the brain, blocking the adenosine from the sites and inactivating them. Therefore, caffeine blocks the sleepiness signal normally communicated to the brain by adenosine, tricking you to feel awake and alert, despite the high levels of adenosine that would otherwise cause you to feel sleepy.

Levels of caffeine peak approximately thirty minutes after consumption. What though is problematic is the persistence of caffeine in your system, as the “half-life” of caffeine is approximately 5 – 7 hours. This is only the time it takes half of the caffeine to be removed from your system, much more decomposition is required before all caffeine is out of the system.  In general, people do not realized how long it takes to overcome a single dose of caffeine late in the afternoon and therefor fail to make the link between having issues falling asleep, staying soundly a sleep and why in the morning they are un-refreshed after not too good night’s sleep.

Caffeine is removed from the body by processes in the liver, and how long it takes to remove all caffeine from the system is based on genetics and therefore sensitivity of individuals to caffeine differs.  Aging also alters the speed of caffeine clearance, and the older we get the more sensitive we become to sleep-disrupting influence of caffeine.
The example below is from a 27-year-old healthy male with no sleep complaints or history of known sleep disorders, and a BMI of 22.  With no caffeine consumption, sleep quality assessment with SleepImage (CPC- analysis) demonstrates baseline conditions of a healthy adult sleep, with good sleep quality, SQI=82 (Sleep Spectrogram baseline night).

Baseline Night

Baseline Night - Spectrogram no caffeine consumption. SQI=82, Sleep Interruptions=34 and Fragmentation 1%.

The day after the subject consumed one cup of strong coffee containing approximately 150 mg of caffeine at 7:00pm and his sleep is presented in the Sleep Spectrogram below, Caffeine night.

Caffeine Night. The Spectrogram was recorded after the subject consumed approximately 150 mg of caffeine at 7:00 pm. SQI-66, Sleep Interruptions=82 Fragmentation 15% and Stable Sleep=63%

On the baseline night, the sleep pattern is healthy; the subject had no issues falling asleep and good stable sleep consolidation is seen through the night, SQI=82. The subject’s sleep quality (SQI=66) during the night upon which caffeine was consumed approaching bedtime was significantly decreased as demonstrated by CPC biomarkers, demonstrated in the table below.
On the caffeine consumption night, one cup of strong coffee was consumed at 07:00PM, then approximately 2hr and 30 minutes elapsed until the first period of consolidated stable sleep lasting more than 30 minutes is seen. Movements are increased during this period and, for the rest of the night, sleep is noticeably more fragmented when compared to the baseline night. The response is likely attributable to caffeine increasing EEG arousals through known inhibition of adenosine metabolic pathways. The result of this experiment supports previous work showing that utilization of heart rate variability is a sensitive measure for evaluating the effects of caffeine on the autonomic nervous system. CPC analysis mathematically integrates heart rate variability and respiration, which is responsive in capturing the caffeine effect during sleep. This example is indicative of the value of repeated sleep measures to test the effect of lifestyle changes that may be important and helpful to identify and administer the most effective treatment method for patients who suffer from sleep disruption, regardless of the presence of sleep disorders.



SleepImage, Insomnia and management of Insomnia Therapy

All individuals experience difficulty sleeping every now and then, most often caused by some social or work-related stressors. Individuals diagnosed with Insomnia, have suffered from inadequate ability to generate sleep, despite allowing for adequate opportunity to get sleep and this happens on more than three days a week over a period of three months. Numerous things can cause insomnia, including genetic, psychological, physical, medical and environmental factors, with the two most common trigger being psychological – (1) emotional concerns or worry and (2) emotional distress or anxiety, increasing sympathetic activation in the autonomic nervous system (ANS) causing increase in metabolic rate preventing the drop in body temperature necessary to initiate sleep and increased levels of the stress hormones (cortisol, adrenaline and noradrenaline) preventing the cardiovascular system to calm down  and at the same time areas in the brain still remain active instead of slowing down.

A condition called sleep-state misperception or paradoxical insomnia, were patients report sleeping poorly throughout the night but when their sleep is objectively tested there is a mismatch and sleep recordings indicate that the patient has slept far better than they themselves believe and sometimes indicate that a they have a healthy sleep. Therefore, in individuals with insomnia complaints, improved access to simple objective sleep testing before any therapy is initiated, should improve clinical diagnosis as well as clinical management of insomnia, and could in some instances avoid unnecessary sleep medication use as in those instances cognitive behavioral therapy (CBT) should be the therapy of choice.

There is documented substantial overlap in symptom presentation of Insomnia and Obstructive Sleep Apnea (OSA). It is therefore important to evaluate the presence and type of sleep disorders before diagnosing a patient as treatment options for insomnia and sleep apnea are different. As patients often have a misconception of their subjective symptoms, there is a need for simple, clinically validated and low-cost methods like SleepImage, to objectively evaluate sleep complaints and symptoms. SleepImage captures sleep quality, sleep latency, sleep duration, sleep arousals and sleep apnea in patients with insomnia complaints (issues falling asleep, maintaining sleep or waking up too early) or symptoms of non-restorative sleep daytime fatigue, but these symptoms as well be caused by the presence of sleep apnea. The SleepImage sleep apnea indicator (SAI) and the SleepImage Apnea Hypopnea Index (sAHI) can help determine the presence of sleep apnea that may need to be treated, either as the primary treatment or a concurrent treatment with Insomnia. 

Offering access to an objective and medically validated test for patients with sleep complaints who currently are considered ineligible for polysomnography test (PSG) or home sleep apnea test (HSAT), will fill a void in clinical management of sleep disorders, improve patient care and their health and quality of life.
Insomnia (Difficulty maintaining sleep)
The subject is a 60-year-old female describing her sleep issues as “It is easy to fall asleep, but having a chronic difficulty staying asleep”, wakes up a few times every night. Analysis of an ECG recording during sleep shows lack of stable sleep periods and fragmented sleep dominates the Spectrogram.

The Sleep duration is approximately 8 hours, sleep latency = 20 min, wake transitions #21 and wake after sleep onset (WASO) = 59 min. SQI = 65; SAI = 3; Fragmentation (e-LFCBB = 7%. Sleep is fragmented throughout the sleeping period as patient describes herself, more prominent the latter part of the night. 

Insomnia (Difficulty falling asleep and maintaining sleep)
The subject is a 37-year-old male, describing his sleep issues as difficulty falling asleep, often not managing to fall asleep until in the early morning hours. Analysis of an PLETH-recording during sleep shows lack of stable sleep periods and wake dominates the Spectrogram until 05.45AM when he finally manages to fall into stable sleep for 45 minutes.

The Sleep duration is approximately 7 hours 40 minutes, sleep latency = 30 min,  wake transitions #21 and wake after sleep onset (WASO) = 60 min. SQI = 65; SAI = 38; sAHI = 12; Fragmentation (e-LFCBB = 24%). Sleep is fragmented throughout the sleeping period as patient describes himself, more prominent the latter part of the night.


Insomnia (waking up too early)
The subject is a 45-year-old female describing her sleep issues having no difficulty falling asleep, but most often waking up after 4-5 hours of sleep, unrested, but not managing to continue sleeping. Analysis of an ECG recording during sleep shows lack of stable sleep periods after 03:15 AM and wake dominates the Spectrogram for mostly rest of the night with the exception of a period of mostly consolidated stable sleep from 04:45AM until 5:30 AM.

The Sleep duration is approximately 8 hours, sleep latency = 8 min, wake transitions #20 and wake after sleep onset (WASO) = 55 min. SQI = 65; SAI = 0; Fragmentation (e-LFCBB = 13%. Sleep is fragmented the latter part of the sleeping period as patient describes herself.





Obstructive Sleep Apnea Syndrome (OSAS) - Child

Cardiopulmonary coupling (CPC) analysis from a PLETH-signal recorded during an overnight polysomnography study in an 8-year-old child (girl) suspected of suffering from obstructive sleep apnea. The output demonstrates a low sleep quality index (SQI) of 56 (expected > 70 for children), the sleep pathology-marker, Sleep Apnea Indicator (SAI) is increased = 6 (expected < 2 in children) as the other sleep pathology markers of Fragmentation = 10 (e-LFCBB expected < 8) and Periodicity (e-LFCNB = 5 expected = 0) and may indicate disease induced central breathing in this case.
She was referred for adenotonsillectomy to resolve apnea events and improve her sleep quality. A follow-up PSG study was conducted 7 months after the surgery.  The CPC output from that PSG study presents significantly improved SQI (SQI = 80) which is within normal range for children. The markers of sleep pathology, SAI = 0. Decreased in SAI correlates with increased sleep efficiency (SE) and decreased wake after sleep onset (WASO). Both Fragmentation (=2) and Periodicity (=0) are within normal expected range.

Full view Spectrogram: ECG analysis from the PSG treatment tracking study 7 months after adenotonsillectomy



Obstructive, Central and Complex Sleep Apnea – Adult

Obstructive Sleep Apnea

The subject is a 42-year-old who underwent a diagnostic Polysomnogram (PSG) study. SleepImage algorithms analyzed the ECG and PLETH data from the PSG study.


The Sleep duration is approximately 8 hours; sAHI=27; (AHI=21); SQI = 39 (expected >55); SAI = 28; (SAI >15 indicates modereate to severe Sleep Disordered Breathing); Fragmentation (e-LFCBB) = 31 (expected <15%); e-LFCNB = 5% (expected <2%). 

Looking at the 3D Spectrogram, the peaks have a broad band distribution indicative of Obstructive Sleep Apnea which correlates with the prevalence of e-LFCBB at 31%

Central Sleep Apnea

Adult female (70-year-old, BMI=22), with complaints of daytime sleepiness and fatigue. Epworth sleepiness score was 19/24 and Polysomnogram (PSG) results revealed severe sleep apnea.

The Sleep recording is approximately 6 hours 50 minutes; SQI = 10 (expected >55); sAHI=66 (AHI=34); SAI = 85; (SAI >15 indicates modereate to severe Sleep Disordered Breathing); HFC = 0% (expected > 50%); LFC = 94% (expected <30%); Fragmentation (e-LFCBB) = 34% (expected <15%); Periodicity (e-LFCNB) = 59% (expected <2%). All CPC parameters are pathological. 

The 3D Spectrogram where the narrow red colored peaks line up representing the pattern of periodicity indicating that this patient has Central Sleep Apnea.

Complex Sleep Apnea – Adult

The subject is a 61-year-old male with BMI of 33 who underwent a Polysomnogram (PSG) study while simultaneously recording with the SleepImage Sleep Data Recorder, recording ECG, actigraphy, snore and body position. The PSG study score was AHI=52 and SAI=63.

The Sleep recording is approximately 7 hours 50; SQI = 20 (expected >55); SAI = 63; (SAI >15 indicates modereate to severe Sleep Disordered Breathing). HFC = 9% (expected > 50%); LFC = 85% (expected <30%); Fragmentation (e-LFCBB) = 37% (expected <15%); e-LFCNB = 37% (expected <2%). All CPC parameters are pathological. 


The 3D Spectrogram shows a combination of peaks. A broad-band pattern mixed with narrow red-colored peaks (representing the pattern of periodicity) indicates that this patient has Complex Sleep Apnea.