Labfront Analytics - Sleep Report

For Researchers or Data Scientists

Abstract

Labfront Analytics Sleep Report provides a detailed analysis of sleep periods, stages and sleep apnea.  To download a sample of the report please visit Labfront Analytics - Sleep Report.


This document includes project and device requirements, technical specifications, report samples and research citations.

1. Project Settings and Device Requirements

To access this report, you must ensure the following:

  • Your wearable device supports BBI (beat-to-beat interval) data collection, examples of wearables supporting BBI include Garmin vívosmart 4 and Garmin Venu Sq.
  • BBI data collection is enabled
  • Garmin Connect is enabled

2. Report Details

Here is a summary of the data in this report, it is reported daily for each participant for the duration of the project.

Index

Description

Normal Values

Unit

Further Details

tTS

Daily Duration - Total Sleep

(Light+Deep+REM+Awake)

360-600

min

Standard amount of sleep including awake periods.

tLS

Daily Duration - Light Sleep

depends on tTS

min

 

tDS

Daily Duration - Deep Sleep

depends on tTS

min

 

tRS

Daily Duration - REM Sleep

depends on tTS

min

 

tW

Daily Duration - Awake

     

rLS

Daily Ratio - Light Sleep

(Duration of tLS / tTS)

<55%

%

Too much light sleep is an indicator of poor sleep quality, causing fatigue and difficulty waking.

rDS

Daily Ratio - Deep Sleep

(Duration of tDS / tTS)

20-60%

%

Deep sleep is most effective for eliminating fatigue. More deep sleep generally indicates better sleep quality, although very high levels of deep sleep over long periods may be a symptom of illness.

rRS

Daily Ratio - REM Sleep

(Duration of REM Sleep / Total Duration)

10-30%

%

Healthy REM sleep helps maintain mental well-being, boosts creativity, and relieves stress.

rW

Daily Ratio - Awake

(Duration of Awake / Total Duration)

 

%

We may be waking at night for number of different reasons. Under normal circumstances, we should wake up fewer than 2 times per night.

cLS

Daily Counting - Light Sleep Stages

<20

#

Counts of each sleep fragment.

cDS

Daily Counting - Deep Sleep Stages

<10

#

Counts of each sleep fragment.

cRS

Daily Counting - REM Sleep Stages

<10

#

Counts of each sleep fragment.

cW

Daily Counting - Awake Stages

<2

#

Counts of each sleep fragment.

tAD*

Daily Duration - Apnea Detected

 

min

Periodic oscillations in RR intervals are often associated with prolonged cycles of sleep apnea. We detect and quantify these periods of both obstructive and central sleep apnea by identifying these oscillatory dynamics in the RR interbeat interval series.*

rAD*

Daily Ratio - Apnea Detected

(Duration of Apnea / Total+Awake Duration)

<15%

%

This ratio reflects both obstructive and central sleep apnea. Instead of a traditional apnea index of AHI<5 for normal people, we recommend that the detected apnea ratio be less than 15% as a normal value of threshold.*

SC**

Sleep Score

   

This score is between 0 and 100 and is defined by the total sleep duration, deep sleep ratio, the degree of fragmentation of deep sleep ,and the ratio of apnea detected. A higher score indicates better sleep quality.**

m0x

The mean value of the Sp02 during sleep

   

Pulse Oximetry is an estimation of peripheral blood oxygen saturation (SpO2%). The range is 0 to 100.

p30Ox

The 30th percentiles of Sp02 during sleep

     

p20Ox

The 20th percentiles of Sp02 during sleep

     

p10Ox

The 10th percentiles of Sp02 during sleep

     

* tAD and rAD are metrics for both obstructive and central sleep apnea.  The algorithm is developed from https://physionet.org/content/apdet/1.0.0/.

** The SC indices are decided from concepts introduced in the textbook of Principles and Practice of Sleep Medicine Edited by: Meir Kryger, Thomas Roth and William C. Dement and the recommendation of sleep experts.


3. Example Report

Screen Shot 2021-10-26 at 7.21.50 PMTo download a sample of the report please visit Labfront Analytics - Sleep Report.

 

4. Science Speak - Citations and References

Compared with the sleep analysis results based on traditional indices identified from polysomnography (PSG), we use a complementary approach to quantify sleep quality in terms of sleep ‘‘stability,’’ originally implemented with an EEG morphological marker that has been termed the cyclic alternating pattern (CAP) [1], [2]. 

A high agreement was found in the classification of human sleep according to the CAP method between the detector and visual scoring [3]. Based on the concepts of CAP, we proposed a method, cardiopulmonary coupling (CPC), utilizing a continuous electrocardiographic (ECG) signal alone to quantify sleep stability [4]–[6]. 

The apnea detection algorithm was developed from PhysioNet, which is approved to correctly classify 26 of 30 subjects (86.6%), and correctly identified the presence or absence of sleep apnea in 13895 of 17045 minutes (82.1%). 

By utilizing these sleep reports you can easily plot sleep fragmentation charts, perform statistical comparison of the duration of sleep stages, estimate the degree of sleep stage fragmentation, and study sleep apnea. 

4.1 References

[1] M. G. Terzano, D. Mancia, M. R. Salati, G. Costani, A. Decembrino, and L. Parrino, “The Cyclic Alternating Pattern as a Physiologic Component of Normal NREM Sleep,” Sleep, vol. 8, no. 2, pp. 137–145, Jun. 1985, doi: 10.1093/sleep/8.2.137.

[2] M. G. Terzano and L. Parrino, “Origin and Significance of the Cyclic Alternating Pattern (CAP),” Sleep Med. Rev., vol. 4, no. 1, pp. 101–123, Feb. 2000, doi: 10.1053/smrv.1999.0083.

[3] A. C. Rosa, L. Parrino, and M. G. Terzano, “Automatic detection of cyclic alternating pattern (CAP) sequences in sleep: preliminary results,” Clin. Neurophysiol., vol. 110, no. 4, pp. 585–592, Apr. 1999, doi: 10.1016/S1388-2457(98)00030-3.

[4] R. J. Thomas, J. E. Mietus, C.-K. Peng, and A. L. Goldberger, “An Electrocardiogram-Based Technique to Assess Cardiopulmonary Coupling During Sleep,” Sleep, vol. 28, no. 9, pp. 1151–1161, Sep. 2005, doi: 10.1093/sleep/28.9.1151.

[5] R. J. Thomas et al., “Differentiating Obstructive from Central and Complex Sleep Apnea Using an Automated Electrocardiogram-Based Method,” Sleep, vol. 30, no. 12, pp. 1756–1769, Dec. 2007, doi: 10.1093/sleep/30.12.1756.

[6] G. Y. Yeh et al., “Enhancement of sleep stability with Tai Chi exercise in chronic heart failure: Preliminary findings using an ECG-based spectrogram method,” Sleep Med., vol. 9, no. 5, pp. 527–536, Jul. 2008, doi: 10.1016/j.sleep.2007.06.003.