The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability

Sleep stage classification in EEG signals using the clustering approach based probability distribution features coupled with classification algorithms

Sleep scoring is one of the primary tasks for the classification of sleep stages in Electroencephalogram (EEG) signals. Manual visual scoring of sleep stages is time-consuming as well as being dependent on the experience of a highly qualified sleep expert. This paper aims to address these issues by developing a new method to automatically classify sleep stages in EEG signals. In this research, a robust method has been presented based on the clustering approach, coupled with probability distribution features, to identify six sleep stages with the use of EEG signals. Using this method, each 30-second EEG signal is firstly segmented into small epochs and then each epoch is divided into 60 sub-segments. Each sub-segment is decomposed into five levels by using a discrete wavelet transform (DWT) to obtain the approximation and detailed coefficient. The wavelet coefficient of each level is clustered using the k-means algorithm. Subsequently, features are extracted based on the probability distribution for each wavelet coefficient. The extracted features then are forwarded to the least squares support vector machine classifier (LS-SVM) to identify sleep stages. Comparisons with several existing methods are also made in this study. The proposed method for the classification of the sleep stages achieves an average accuracy rate of 97.4%. It can be an effective tool for sleep stages classification and can be useful for doctors and neurologists for diagnosing sleep disorders.

A Wrinkle in Measuring Time Use for Cognitive Health: How should We Measure Physical Activity, Sedentary Behaviour and Sleep?

One new case of dementia is detected every 4 seconds and no effective drug therapy exists. Effective behavioural strategies to promote healthy cognitive ageing are thus essential. Three behaviours related to cognitive health which we all engage in daily are physical activity, sedentary behaviour and sleep. These time-use activity behaviours are linked to cognitive health in a complex and dynamic relationship not yet fully elucidated. Understanding how each of these behaviours is related to each other and cognitive health will help determine the most practical and effective lifestyle strategies for promoting healthy cognitive ageing. In this review, we discuss methods and analytical approaches to best investigate how these time-use activity behaviours are related to cognitive health. We highlight four key recommendations for examining these relationships such that researchers should include measures which (1) are psychometrically appropriate; (2) can specifically answer the research question; (3) include objective and subjective estimates of the behaviour and (4) choose an analytical method for modelling the relationships of time-use activity behaviours with cognitive health which is appropriate for their research question.

Assessment of submentalis muscle activity for sleep-wake classification of healthy individuals and patients with sleep disorders

This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.

A jerk-based algorithm ACCEL for the accurate classification of sleep–wake states from arm acceleration

Arm acceleration data have been used to measure sleep–wake rhythmicity. Although several methods have been developed for the accurate classification of sleep–wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep–wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep–wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.

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An algorithm for sleep-wake classification based on arm acceleration is presented

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The algorithm only uses a derivative of triaxial arm acceleration (jerk)

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The algorithm can accurately detect temporal awake during sleep

Transfer learning from ECG to PPG for improved sleep staging from wrist-worn wearables

OBJECTIVE

To develop a sleep staging method from wrist-worn accelerometry and the photoplethysmogram (PPG) by leveraging transfer learning from a large electrocardiogram (ECG) database.

APPROACH

In previous work, we developed a deep convolutional neural network for sleep staging from ECG using the cross-spectrogram of ECG-derived respiration and instantaneous beat intervals, heart rate variability metrics, spectral characteristics, and signal quality measures derived from 5,793 subjects in Sleep Heart Health Study (SHHS). We updated the weights of this model by transfer learning using PPG data derived from the Empatica E4 wristwatch worn by 105 subjects in the `Emory Twin Study Follow-up' (ETSF) database, for whom overnight polysomnographic (PSG) scoring was available. The relative performance of PPG, and actigraphy (Act), plus combinations of these two signals, with and without transfer learning was assessed.

MAIN RESULTS

The performance of our model with transfer learning showed higher accuracy (1-9 percentage points) and Cohen's Kappa (0.01-0.13) than those without transfer learning for every classification category. Statistically significant, though relatively small, incremental differences in accuracy occurred for every classification category as tested with the McNemar test. The out-of-sample classification performance using features from PPG and actigraphy for four-class classification was Accuracy (Acc)=68.62% and Kappa=0.44. For two-class classification, the performance was Acc=81.49% and Kappa=0.58.

SIGNIFICANCE

We proposed a combined PPG and actigraphy-based sleep stage classification approach using transfer learning from a large ECG sleep database. Results demonstrate that the transfer learning approach improves estimates of sleep state. The use of automated beat detectors and quality metrics means human over-reading is not required, and the approach can be scaled for large cross-sectional or longitudinal studies using wrist-worn devices for sleep-staging.

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

Making Sense of Sleep

Traditionally, sleep monitoring has been performed in hospital or clinic environments, requiring complex and expensive equipment set-up and expert scoring. Wearable devices increasingly provide a viable alternative for sleep monitoring and are able to collect movement and heart rate (HR) data. In this work, we present a set of algorithms for sleep-wake and sleep-stage classification based upon actigraphy and cardiac sensing amongst 1,743 participants. We devise movement and cardiac features that could be extracted from research-grade wearable sensors and derive models and evaluate their performance in the largest open-access dataset for human sleep science. Our results demonstrated that neural network models outperform traditional machine learning methods and heuristic models for both sleep-wake and sleep-stage classification. Convolutional neural networks (CNNs) and long-short term memory (LSTM) networks were the best performers for sleep-wake and sleep-stage classification, respectively. Using SHAP (SHapley Additive exPlanation) with Random Forest we identified that frequency features from cardiac sensors are critical to sleep-stage classification. Finally, we introduced an ensemble-based approach to sleep-stage classification, which outperformed all other baselines, achieving an accuracy of 78.2% and F1 score of 69.8% on the classification task for three sleep stages. Together, this work represents the first systematic multimodal evaluation of sleep-wake and sleep-stage classification in a large, diverse population. Alongside the presentation of an accurate sleep-stage classification approach, the results highlight multimodal wearable sensing approaches as scalable methods for accurate sleep-classification, providing guidance on optimal algorithm deployment for automated sleep assessment. The code used in this study can be found online at: https://github.com/bzhai/multimodal_sleep_stage_benchmark.git

Sleep/wake classification via remote PPG signals

This paper proposes a remote sleep/wake classification method by combining vision-based heart rate (HR) estimation and convolutional neural network (CNN). Instead of inputting the estimated HR with low temporal resolution, remote PPG (Photoplethysmogram) signals, which contain high-temporal-resolution HR information, are input into the CNN. To reduce noise in the remote PPG signals, we propose a dynamic HR filter. Evaluation results show that the dynamic HR filter works more effectively in comparison with the static filter, which helps improve the area under the ROC curve (AUC) to 0.70, which is almost as good as the reference 0.71 for HR from a wearable sensor.

Melatonin Therapy Improves Cardiac Autonomic Modulation in Pinealectomized Patients

The purpose of this investigational study was to assess the effects of melatonin replacement therapy on cardiac autonomic modulation in pinealectomized patients. This was an open-label, single-arm, single-center, proof-of-concept study consisting of a screening period, a 3-month treatment period with melatonin (3 mg/day), and a 6-month washout period. The cardiac autonomic function was determined through heart rate variability (HRV) measures during polysomnography. Pinealectomized patients (n = 5) with confirmed absence of melatonin were included in this study. Melatonin treatment increased vagal-dominated HRV indices including root mean square of the successive R-R interval differences (RMSSD) (39.7 ms, 95% CI 2.0-77.4, p = 0.04), percentage of successive R-R intervals that differ by more than 50 ms (pNN50) (17.1%, 95% CI 9.1-25.1, p = 0.003), absolute power of the high-frequency band (HF power) (1,390 ms², 95% CI 511.9-2,267, p = 0.01), and sympathetic HRV indices like standard deviation of normal R-R wave interval (SDNN) (57.6 ms, 95% CI 15.2-100.0, p = 0.02), and absolute power of the low-frequency band (LF power) (4,592 ms², 95% CI 895.6-8,288, p = 0.03). These HRV indices returned to pretreatment values when melatonin treatment was discontinued. The HRV entropy-based regularity parameters were not altered in this study, suggesting that there were no significant alterations of the REM-NREM ratios between the time stages of the study. These data show that 3 months of melatonin treatment may induce an improvement in cardiac autonomic modulation in melatonin-non-proficient patients. ClinicalTrials.gov Identifier: NCT03885258.

Validation of a Consumer Sleep Wearable Device With Actigraphy and Polysomnography in Adolescents Across Sleep Opportunity Manipulations

STUDY OBJECTIVES

To compare the quality and consistency in sleep measurement of a consumer wearable device and a research-grade actigraph with polysomnography (PSG) in adolescents.

METHODS

Fifty-eight healthy adolescents (aged 15-19 years; 30 males) underwent overnight PSG while wearing both a Fitbit Alta HR and a Philips Respironics Actiwatch 2 (AW2) for 5 nights, with either 5 hours or 6.5 hours time in bed (TIB) and for 4 nights with 9 hours TIB. AW2 data were evaluated using two different wake and immobility thresholds. Discrepancies in estimated total sleep time (TST) and wake after sleep onset (WASO) between devices and PSG, as well as epoch-by-epoch agreements in sleep/wake classification, were assessed. Fitbit-generated sleep staging was compared to PSG.

RESULTS

Fitbit and AW2 under default settings similarly underestimated TST and overestimated WASO (TST: medium setting (M10) ≤ 38 minutes, Fitbit ≤ 47 minutes; WASO: M10 ≤ 38 minutes; Fitbit ≤ 42 minutes). AW2 at the high motion threshold setting provided readings closest to PSG (TST: ≤ 12 minutes; WASO: ≤ 18 minutes). Sensitivity for detecting sleep was ≥ 90% for both wearable devices and further improved to 95% by using the high threshold (H5) setting for the AW2 (0.95). Wake detection specificity was highest in Fitbit (≥ 0.88), followed by the AW2 at M10 (≥ 0.80) and H5 thresholds (≤ 0.73). In addition, Fitbit inconsistently estimated stage N1 + N2 sleep depending on TIB, underestimated stage N3 sleep (21-46 min), but was comparable to PSG for rapid eye movement sleep. Fitbit sensitivity values for the detection of N1 + N2, N3 and rapid eye movement sleep were ≥ 0.68, ≥ 0.50, and ≥ 0.72, respectively.

CONCLUSIONS

A consumer-grade wearable device can measure sleep duration as well as a research actigraph. However, sleep staging would benefit from further refinement before these methods can be reliably used for adolescents.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Title: The Cognitive and Metabolic Effects of Sleep Restriction in Adolescents; Identifier: NCT03333512; URL: https://clinicaltrials.gov/ct2/show/NCT03333512.

CITATION

Lee XK, Chee NIYN, Ong JL, Teo TB, van Rijn E, Lo JC, Chee MWL. Validation of a consumer sleep wearable device with actigraphy and polysomnography in adolescents across sleep opportunity manipulations. J Clin Sleep Med. 2019;15(9):1337-1346.

Comparative analysis of different characteristics of automatic sleep stages

BACKGROUND AND OBJECTIVE

With the acceleration of social rhythm and the increase of pressure, there are various sleep problems among people. Sleep staging is an important basis for the diagnosis of sleep disorders and other related diseases. The process of automatic staging of sleep is mainly divided into three core steps: data preprocessing, feature extraction, and classification. Accurate analysis of the features of sleep electroencephalogram (EEG) signals is not only helpful to improve the accuracy of sleep staging, but also help people to understand their sleep status.

METHODS

This paper focuses on the analysis of EEG features during sleep staging, and reviews many feature extraction methods and classification methods for sleep staging and sums up these algorithms used in literatures and its staging results. Besides, this paper lists a total of 22 features based on time domain, time-frequency, and nonlinear analysis methods, including kurtosis, skewness, Hjorth parameters, and standard deviations, wavelets energy; sample entropy (SampEn), fuzzy entropy, Tsallis entropy, fractal dimension (FD), complexity. The data set comes from EDF database. Wavelet transform (WT) and support vector machine (SVM) are used to achieve the sleep staging based on single-channel EEG signal. And the characteristic feature data was analyzed by ANOVA.

RESULTS

By comparison, the SampEn, fuzzy entropy, FD and complexity can achieve ideal sleep staging. The highest accuracy of sleep staging is 85.93%. The FD and complexity are simpler than the entropy value, but their accuracies are lower. Furthermore, the distribution of these methods in different sleep period is more significant than others, which is content with the results of sleep staging.

CONCLUSION

In a word, due to the non-stationary and non-linear characteristics of EEG signals, time domain and time-frequency analysis methods all have some limitations. Nonlinear analysis was more effective and practical for the analysis of sleep EEG.

Sleep-wake classification via quantifying heart rate variability by convolutional neural network

OBJECTIVE

Fluctuations in heart rate are intimately related to changes in the physiological state of the organism. We exploit this relationship by classifying a human participant's wake/sleep status using his instantaneous heart rate (IHR) series.

APPROACH

We use a convolutional neural network (CNN) to build features from the IHR series extracted from a whole-night electrocardiogram (ECG) and predict every 30 s whether the participant is awake or asleep. Our training database consists of 56 normal participants, and we consider three different databases for validation; one is private, and two are public with different races and apnea severities.

MAIN RESULTS

On our private database of 27 participants, our accuracy, sensitivity, specificity, and [Formula: see text] values for predicting the wake stage are [Formula: see text], 52.4%, 89.4%, and 0.83, respectively. Validation performance is similar on our two public databases. When we use the photoplethysmography instead of the ECG to obtain the IHR series, the performance is also comparable. A robustness check is carried out to confirm the obtained performance statistics.

SIGNIFICANCE

This result advocates for an effective and scalable method for recognizing changes in physiological state using non-invasive heart rate monitoring. The CNN model adaptively quantifies IHR fluctuation as well as its location in time and is suitable for differentiating between the wake and sleep stages.