This article previously ran in the Q2 2023 issue of A2Zzz.
In the sleep field, wireless technology is increasingly being used to screen for obstructive sleep apnea (OSA). Lagging behind is the use of wireless technology to screen for and/or diagnose non-OSA related sleep problems such as restless leg syndrome/periodic leg movements (RLS/PLMs), however. Additionally, depending on the sleep monitoring system, other physiological features such as sleep position, encephalography (EEG), electrocardiology (ECG), snoring and sleep stages may not be monitored. Various devices are being developed to improve wireless polysomnography (PSG) monitoring, but some remain experimental for now.
Wireless monitoring in PSG typically combines radio detection and ranging (radar) technology — in particular, doppler radar — and Bluetooth and/or Wi-Fi technology. Radar technology involves emitting radio waves from a source to determine the distance a target object is from the signal source. The emitted signal bounces off of the target and returns to the source. The time required for the signal to return to the source reflects how far the target is from the signal source.
Doppler radar technology adds the element of the doppler effect (i.e., changes in the frequency and wavelength of reflected signals as a target moves toward or away from a signal source), which makes monitoring motions such as chest movements possible. In Bluetooth technology, radio waves are directly transmitted a short range (e.g., a few feet) from one object to a mobile device. In Wi-Fi technology, radio waves are used to provide wireless communication between computers, mobile devices and other devices. Additionally, Wi-Fi technology can provide internet access.
In a traditional PSG study, the myriad of sensors attached to a patient’s head, outer canthus of each eye, near the nose and mouth and on the lower neck, chest, legs and finger may be uncomfortable and can therefore interfere with sleep during a sleep study. For these reasons, much research has focused on reducing the number of sensors. To date, current devices used in wireless PSG systems for in-laboratory or in-home studies still require attaching some sensors to the body. Therefore, scientists continue efforts to improve wireless PSG technology by making sensors contactless. Recent findings have shown some encouraging results.
Sound Analysis Algorithm
In 2015, Dafna et al.1 developed the breathing sound analysis algorithm (i.e., a specialized mathematical formula), which allowed the preprocessing of audio signals (i.e., removing unwanted noise such as air conditioner or fan noises) and featured extraction (i.e., distinguishing between breathing and snoring) and estimation of the patient’s sleepwake pattern, based on the audio data obtained from a noncontact microphone placed approximately 3 feet away from a person’s head. The algorithm’s results were compared with those of PSG data, which were obtained simultaneously during the participants’ sleep studies. Dafna found that the sensitivity (i.e., accurately detecting when a person was asleep) and specificity (i.e., accurately detecting when a person was awake) of the algorithm were 92.2% and 56.6%, respectively. The investigators further found that sleep latency, total sleep time, wake after sleep onset and sleep efficiency did not differ significantly between the algorithm and PSG data. Dafna concluded that sleepwake activity and sleep quality parameters can be reliably estimated by using their developed algorithm.
However, a drawback of using breathing sound data is that the data cannot be used to distinguish between non-rapid eye movement (NREM) and rapid eye movement (REM) sleep. With that in mind, Rahman et al.2 in 2015 described a radar-based device, which they called DoppleSleep, to track sleep-related physical and physiological features such as body and sublte chest movements, heart movements associated with breathing and heartbeats. DoppleSleep consists of a unit that collects and amplifies the raw radar signal and transmits the signal to a smartphone via Bluetooth for further processing. An application in the smartphone determines heart and breathing rates, movement estimation and sleep modeling. By using data derived from a wireless EEG headband device as a reference, Rahman found that the DoppleSleep device had a sensitivity of 89.6% for distinguishing between sleep and wake and a sensitivity of 80.2% for distinguishing between REM and non-REM. With such promising results, Rahman believes that the DoppleSleep device could potentially be used to monitor the sleep of patients without the need for wiring. However, it will require further improvements.
In 2017, Chung et al.3 developed an algorithm that uses multiple data types (i.e., breathing rate, breathing pattern, heart rate, body motion and snore sounds) to determine sleep stages to enhance the accuracy of sleep stage classification. To validate the possibility of commercializing their work, Chung compared the sleep stage results of their algorithm with those of a commercially available sleep monitoring device, ResMed S+ (ResMed, San Diego, California), which has a sleep-staging feature. In their study, volunteers with OSA were simultaneously monitored with the wireless system, which used Chung's algorithm, PSG monitoring with a portable, full PSG device and the ResMed S+ device. The researchers found that the algorithm had significantly greater accuracy in detecting wake than the ResMed S+ device (60.0% vs. 43.8%). The algorithm and ResMed S+ detected NREM with an accuracy of 71.9%, and both had low accuracy in detecting REM sleep (algorithm, 26.0%; ResMed S+, 21.5%). The overall accuracy rate for detecting REM/ NREM sleep and wake was 64.4% with the algorithm and 60.9% with ResMed S+ device. Based on their findings, Chung suggests their algorithm may provide higher accuracy in sleep stage detection.
Actigraphy is another noninvasive method used to distinguish between sleep and wake based on the level of movement (i.e., low activity indicates sleep; high activity indicates wake). However, an actigraph placed on a limb does not give information regarding whether a person is prone, supine or lying on one side. To counter this factor, body position during sleep in current wireless, full PSG systems is determined via a small actigraph unit that is embedded in the PSG sensor unit that is attached to the chest or abdomen a placement that may hinder patients in sleeping prone.
In 2022, Kukwa and colleagues4 described a small, wireless sensor developed by Clebre (Olsztyn, Poland) that can be placed at the suprasternal notch at the base of the front neck to determine body position. The Clebre device contains two acoustic channels to detect tracheal breathing sounds and a triaxial accelerometer (i.e., a device that detects vertical, horizontal and axial movements) to detect body position.5 In addition, the device collects and analyzes heart rate and movement activity, and relays the information wirelessly to an application that is downloaded to a mobile phone.
In a validation study, Kukwa and colleagues4 compared positions detected by the Clebre device versus positions recorded with a full, portable PSG system (NOX A1 PSG system; Nox Medical Inc., Reykjavik, Iceland). The Clebre device’s accuracy in detecting supine and nonsupine positions was 96.9% and 97.0% respectively; its accuracy for right and left positions was 98.6% and 97.4%, respectively; and its accuracy in detecting the prone position was 97.3%. Based on these promising findings, Kukwa proposed that body positioning devices should be placed in the suprasternal notch rather than on the chest since this placement allows patients to sleep prone.
Current wireless EEG acquisition devices typically involve a patient wearing a headband or other type of headgear containing sensors that detect EEG signals. However, these devices may be uncomfortable for patients or hinder their movements. In 2015, Debener and colleagues6 described a C-shaped device that encircles the back of the ear to record EEG signals. The device, called cEEGrid, is a flexible, thin strip that contains 10 embedded screenprinted electrodes, which are manufactured by printing different types of ink (containing carbon, silver, gold or platinum) onto a plastic or ceramic substrate and connected to a miniaturized amplifer. The information is then wirelessly transmitted to a smartphone.
Building on Debener’s work, Sterr and colleagues7 compared cEEGrid signal quality with the EEG signal quality on a portable PSG system. They found that both systems had comparable signal quality; the overall signal strength was lower for the cEEGrid device than for the portable PSG system. Other drawbacks of the cEEGrid were noise interference, lower spatial resolution and the electrode array comes only in one size, and therefore it may not fit comfortably for some patients. Scientists hope that when perfected, the cEEGrid could be beneficial for sleep recordings because it is easily applied and potentially may be self-administered in a home environment for home sleep studies.
In 2015, a student team at the University of British Columbia (Vancouver, Canada) developed a wireless electromyography (EMG) prototype that could be used to detect leg movements in patients suspected of having RLS/PLMs.8,9 Their prototype consisted of a band containing electrodes that wrapped around a patient’s leg and collects EMG data. The signal is transferred wirelessly by Bluetooth technology to an app on a mobile phone that converts the information to display a graph of the EMG signals on the phone’s screen. (To see images of the device, visit ece.ubc.ca/better-diagnostic-tools-for-restless-legs-syndrome). The student team conducted a clinical trial of the prototype on pediatric and adult volunteers, and they found that the prototype was able to detect muscle tension that was not visible to the eye.8 Thus, they concluded the prototype had excellent potential to enable the acquisition and analysis of EMG signals in patients suspected of having RLS or PLMs.
Current wireless, full PSG systems that have been approved or cleared by the U.S. Food and Drug Administration (Silver Spring, Maryland) for clinical use are Onera Sleep Test System (Onera Health, Palo Alto, California), Sapphire PSG (CleveMed, Cleveland Medical Devices Inc. Cleveland, Ohio) and Nox A1 (Nox Medical, Alpharetta, Georgia). They are “wireless” in that wires from sensors attached to a patient’s body do not extend from the patient to a minibox. Sensors are instead attached to a patient’s body, collect data and relay the data via radio waves to another device (e.g., smartphone) for processing, which enhances patient comfort. However, noncontact monitoring of sleep stages, EEG, body movement, etc., would be more ideal because this would allow patients to sleep most naturally in a sleep center or in their home setting. For now, scientists continue to do research to make this possible.
- Dafna E et al. Breathing and Snoring Sound Characteristics during Sleep in Adults. J Clin Sleep Med. 2016 Mar 15;12(3):375–384.
- Rahman T et al. DoppleSleep: A contactless unobtrusive sleep sensing system using short-range Doppler radar. UbiComp '15: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Held at: Osaka, Japan on September 7-11, 2015. New York, NY: Association for Computing Machinery. 2015;p.39-50 URL: https://dl.acm.org/doi/abs/10.1145/2750858.2804280. doi: https://doi.org/10.1145/2750858.2804280
- Chung K, Song K, Shin K, Sohn J, Cho SH, & Chang JH. Noncontact sleep study by multi-modal sensor fusion. Sensors (Basel). 2017;17:1685. https://doi.org/https://doi.org/10.3390%2Fs17071685
- Kukwa W et al. Sleep position detection with a wireless audio-motion sensor-a validation study. Diagnostics (Basel). 2022;12:1195. https://doi.org/10.3390/diagnostics12051195
- Młyńczak M, Valdez TA, & Kukwa W. Joint apnea and body position analysis for home sleep studies using a wireless audio and motion sensor. IEEE. 2020;8:170579-170587.
- Debener S et al. Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear. Scientific Reports. 2015;5:16743. doi: https://doi.org/10.1038/srep16743
- 7. Sterr A et al. Sleep EEG derived from behindthe-ear electrodes (cEEGrid) compared to standard polysomnography: a proof of concept study. Frontiers in Human Neuroscience. 2018;12:452. doi: https://doi.org/10.3389/fnhum.2018.00452
- Geisler B, MacDonald T, Ng C, & Saad T. Better Diagnostic Tools for Restless Legs Syndrome. University of British Columbia: Vancouver, BC, Canada. 2015. Accessed on April 19, 2023. https://ece.ubc.ca/betterdiagnostic-tools-for-restless-legs-syndrome/
- Geisler B et al. Smartphone-based electromyography system [EMG] for screening Willis–Ekbom disease [WED] during suggested clinical immobilization test [SCIT]. Sleep Medicine. 2015;16(Suppl 1):S29-S30. https://doi.org/10.1016/j.sleep.2015.02.073s=20. Accessed 4 Feb 2023.