Sleep architecture measurement based on cardiorespiratory parameters

Sleep Quality Evaluation Based on Single-Lead Wearable Cardiac Cycle Acquisition Device

In clinical conditions, polysomnography (PSG) is regarded as the "golden standard" for detecting sleep disease and offering a reference of objective sleep quality. For healthy adults, scores from sleep questionnaires are more reliable than other methods in obtaining knowledge of subjective sleep quality. In practice, the need to simplify PSG to obtain subjective sleep quality by recording a few channels of physiological signals such as single-lead electrocardiogram (ECG) or photoplethysmography (PPG) signal is still very urgent. This study provided a two-step method to differentiate sleep quality into "good sleep" and "poor sleep" based on the single-lead wearable cardiac cycle data, with the comparison of the subjective sleep questionnaire score. First, heart rate variability (HRV) features and ECG-derived respiration features were extracted to construct a sleep staging model (wakefulness (W), rapid eye movement (REM), light sleep (N1&N2) and deep sleep (N3)) using the multi-classifier fusion method. Then, features extracted from the sleep staging results were used to construct a sleep quality evaluation model, i.e., classifying the sleep quality as good and poor. The accuracy of the sleep staging model, tested on the international public database, was 0.661 and 0.659 in Cardiology Challenge 2018 training database and Sleep Heart Health Study Visit 1 database, respectively. The accuracy of the sleep quality evaluation model was 0.786 for our recording subjects, with an average F₁-score of 0.771. The proposed sleep staging model and sleep quality evaluation model only requires one channel of wearable cardiac cycle signal. It is very easy to transplant to portable devices, which facilitates daily sleep health monitoring.

Enhancing Current Cardiorespiratory-based Approaches of Sleep Stage Classification by Temporal Feature Stacking

This paper presents a generic method to enhance performance and incorporate temporal information for cardiorespiratory-based sleep stage classification with a limited feature set and limited data. The classification algorithm relies on random forests and a feature set extracted from long-time home monitoring for sleep analysis. Employing temporal feature stacking, the system could be significantly improved in terms of Cohen's κ and accuracy. The detection performance could be improved for three classes of sleep stages (Wake, REM, Non-REM sleep), four classes (Wake, Non-REM-Light sleep, Non-REM Deep sleep, REM sleep), and five classes (Wake, N1, N2, N3/4, REM sleep) from a κ of 0.44 to 0.58, 0.33 to 0.51, and 0.28 to 0.44 respectively by stacking features before and after the epoch to be classified. Further analysis was done for the optimal length and combination method for this stacking approach. Overall, three methods and a variable duration between 30 s and 30 min have been analyzed. Overnight recordings of 36 healthy subjects from the Interdisciplinary Center for Sleep Medicine at Charité-Universitätsmedizin Berlin and Leave-One-Out-Cross-Validation on a patient-level have been used to validate the method.Clinical relevance- The method can be employed generically to feature sets for (small scale) datasets to improve classification performance for classification problems with temporal relations with random forest classifiers.

Can We Monitor Breathing During Sleep via Wi-Fi on Smartphone?

Over the last decade there has been high interest in home sleep monitoring among research community and society. Smartphone-based sleep monitoring seems to be especially attractive. However, smartphone should be able to register physiological processes for that, such as breathing or heartbeats. In this study, we tried to detect breathing based on the analysis of channel state information (CSI) of Wi-Fi connection between a smartphone and an access point. We collected data of 5 subjects with different body mass index (17-33) in base sleep postures: prone, supine, and side. The obtained CSI-based respiratory signal was compared with ground truth from a contact belt sensor. The mean correlation coefficient between the signals of 0.85 was achieved. The mean absolute difference between respiratory rates (breaths per epoch) is 0.39. The results show that it is possible to use smartphones without additional sensors for registering breathing based on CSI and potentially for sleep monitoring.

Sleep quality prediction in caregivers using physiological signals

Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

Sleep staging from single-channel EEG with multi-scale feature and contextual information

PURPOSE

Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which extracts sleep EEG features by multi-scale convolutional neural networks (CNN) and then infers the type of sleep stages by capturing the contextual information between adjacent epochs using recurrent neural networks (RNN) and conditional random field (CRF).

METHODS

To verify the feasibility of our model, two datasets, one composed by two different single-channel EEGs (Fpz-Cz and Pz-Oz) on 20 healthy people and one composed by a single-channel EEG (F4-M1) on 104 obstructive sleep apnea (OSA) patients with different severities, were examined. The corresponding sleep stages were scored as four states (wake, REM, light sleep, and deep sleep). The accuracy measures were obtained from epoch-by-epoch comparison between the model and PSG scorer, and the agreement between them was quantified with Cohen's kappa (ҡ).

RESULTS

Our model achieved superior performance with average accuracy (Fpz-Cz, 0.88; Pz-Oz, 0.85) and ҡ (Fpz-Cz, 0.82; Pz-Oz, 0.77) on the healthy people. Furthermore, we validated this model on the OSA patients with average accuracy (F4-M1, 0.80) and ҡ (F4-M1, 0.67). Our model significantly improved the accuracy and ҡ compared to previous methods.

CONCLUSIONS

The proposed SleepStageNet has proved feasible for assessment of sleep architecture among OSA patients using single-channel EEG. We suggest that this technological advancement could augment the current use of home sleep apnea testing.

Deep learning in the cross-time frequency domain for sleep staging from a single-lead electrocardiogram

OBJECTIVE

This study classifies sleep stages from a single lead electrocardiogram (ECG) using beat detection, cardiorespiratory coupling in the time-frequency domain and a deep convolutional neural network (CNN).

APPROACH

An ECG-derived respiration (EDR) signal and synchronous beat-to-beat heart rate variability (HRV) time series were derived from the ECG using previously described robust algorithms. A measure of cardiorespiratory coupling (CRC) was extracted by calculating the coherence and cross-spectrogram of the EDR and HRV signal in 5 min windows. A CNN was then trained to classify the sleep stages (wake, rapid-eye-movement (REM) sleep, non-REM (NREM) light sleep and NREM deep sleep) from the corresponding CRC spectrograms. A support vector machine was then used to combine the output of CNN with the other features derived from the ECG, including phase-rectified signal averaging (PRSA), sample entropy, as well as standard spectral and temporal HRV measures. The MIT-BIH Polysomnographic Database (SLPDB), the PhysioNet/Computing in Cardiology Challenge 2018 database (CinC2018) and the Sleep Heart Health Study (SHHS) database, all expert-annotated for sleep stages, were used to train and validate the algorithm.

MAIN RESULTS

Ten-fold cross validation results showed that the proposed algorithm achieved an accuracy (Acc) of 75.4% and a Cohen's kappa coefficient of [Formula: see text]  =  0.54 on the out of sample validation data in the classification of Wake, REM, NREM light and deep sleep in SLPDB. This rose to Acc  =  81.6% and [Formula: see text]  =  0.63 for the classification of Wake, REM sleep and NREM sleep and Acc  =  85.1% and [Formula: see text]  =  0.68 for the classification of NREM sleep versus REM/wakefulness in SLPDB.

SIGNIFICANCE

The proposed ECG-based sleep stage classification approach that represents the highest reported results on non-electroencephalographic data and uses datasets over ten times larger than those in previous studies. By using a state-of-the-art QRS detector and deep learning model, the system does not require human annotation and can therefore be scaled for mass analysis.

Estimation of a priori probabilities of sleep stages: A cycle-based approach

This paper presents a model for the estimation of a priori probabilities of sleep epoch classes based on the epoch location in a sleep cycle. These probabilities are used as additional features for sleep stage classification based on the analysis of respiratory effort. The model was validated with data of 685 subjects selected from the Sleep Heart Health Study dataset. The model improves a base algorithm by 8 percent points and demonstrates Cohen's kappa of 0.56 ± 0.12. Our results will contribute to the development of screening tools for unobtrusive sleep structure estimation.