The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

Abstract

Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH's automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm and 3.2 ± 0.6 %, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 ± 6.4% (F1 score : 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Sleep | EEG | Machine learning | Sleep stages | DeviceCorrespondence: research@dreem.com

Closed-Loop Acoustic Stimulation During Sedation with Dexmedetomidine (CLASS-D): Protocol for a Within-Subject, Crossover, Controlled, Interventional Trial with Healthy Volunteers

“…Each participant will then be provided with a Dreem device, designed to record sleep in the home environment and to deliver in-phase acoustic stimulation via bone conduction. 15 , 31 , 32 The device includes frontal and occipital EEG, as well as pulse plethysmography and accelerometry. Participants will receive instructions on using the device to record ambulatory sleep studies.…”

Recent advances in wearable sensors and portable electronics for sleep monitoring

“…The biggest design challenge of sleep monitoring devices that can be used outside of hospitals is making electrodes that can measure EEG from the scalp, mostly covered by hairs (Arai et al, 2016). In standard PSG, conductive gels are widely used to penetrate through the hairs and make an electrical path between the scalp (Arnal et al, 2019) Forehead (Arnal et al, 2019), (Levendowski et al, 2017), (Lin et al, 2017), (Shustak et al, 2019) Ear (Alqurashi et al, 2018), (Sterr et al, 2018), (Mikkelsen et al, 2019) Heart activity ECG Wet/dry electrode Chest (Klum et al, 2020), (Ilen et al, 2019), (Di Rienzo et al, 2018), (Yoon et al, 2018 (Liao et al, 2020), (Kinnunen et al, 2020) Nose (Manoni et al, 2020) Forehead (Arnal et al, 2019), (Levendowski et al, 2017)…”

“…LED & photodiode Wrist (Braun et al, 2020) Forehead (Beppler et al, 2018) Ear (Davies et al, 2020) IPG Dry electrode Wrist (Schneider et al, 2018) Pulse pressure Pressure sensor Wrist (Meng et al, 2020) Respiration Body movement Strain gauge, accelerometer, magnetometer Chest (Klum et al, 2020), (Ding et al, 2018), (Ramírez et al, 2019), (Di Rienzo et al, 2018), (Milici et al, 2018) Head (Arnal et al, 2019) Air flow Humidity sensor Nose (Jin et al, 2017) Lung sound Stethoscope Chest (Klum et al, 2020) Movement EMG Dry electrode Leg (Jortberg et al, 2018) Actigraphy Accelerometer, gyroscope, magnetometer Wrist (Liao et al, 2020), (Kwasnicki et al, 2018) Leg (Bobovych et al, 2020) Chest (Ilen et al, 2019), (Kwasnicki et al, 2018) Body position Accelerometer, gyroscope, magnetometer Wrist (Kwasnicki et al, 2018) Chest (Yun et al, 2020), (Kwasnicki et al, 2018) Nose (Manoni et al, 2020) Others EDA Dry electrode Wrist (Romine et al, 2019), (Kim et al, 2021) Finger (Lam and Szypula, 2018) Tongue deformation Ultrasonic transducer Chin (Weng et al, 2017) Body temperature Thermometer Chest, wrist (Di Rienzo et al, 2018), (Liao et al, 2020) Snoring sound Microphone Forehead (Levendowski et al, 2017) ll OPEN…”

“…Confusion matrix showing an accuracy of automated sleep scoring algorithm. Reproduced with permission, from ref(Arnal et al, 2019), Copyright 2019 In-ear memory foam system with conductive fabric-based EEG electrodes. Plots showing comparable signal quality to scalp EEG and sleep latency detection accuracy.…”

Clinician-Focused Overview and Developments in Polysomnography

“…Importantly, only a few commercial devices (i.e., Dreem2 TM ) used EEG to measure staging as is standard with PSG [36]. These newer devices using either headband or around the ear dry electrodes for EEG are currently in validations studies, and the hope will be to gain more sleep information longitudinally [37]. So far, early validation studies have shown feasible and accessible ways to acquire sleep data in newer montages and a moderate range of agreement on the group level for all sleep staging combined based on blinded rated AASM scoring [38].…”