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

Malik, John; Lo, Yu‐Lun; Wu, Hau‐Tieng

doi:10.1088/1361-6579/aad5a9

Cited by 54 publications

(62 citation statements)

References 53 publications

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“…The field has been increasingly studied in recent years. Most of the studies focused on sleep-wake classification 6–9 and wake-REM-NREM classification 4,10–12 while only a few have developed methods that separate light non-REM sleep (N1 and N2) from slow wave sleep (N3), i.e. wake-REM-N1/N2-N3 classification.…”

Section: Introductionmentioning

confidence: 99%

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Radha

Fonseca

Moreau³

et al. 2019

Sci Rep

131

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Automated sleep stage classification using heart rate variability (HRV) may provide an ergonomic and low-cost alternative to gold standard polysomnography, creating possibilities for unobtrusive home-based sleep monitoring. Current methods however are limited in their ability to take into account long-term sleep architectural patterns. A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541.214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard. It is shown that the model outperforms state-of-the-art approaches which were often limited to non-temporal or short-term recurrent classifiers. The model achieves a Cohen’s k of 0.61 ± 0.15 and accuracy of 77.00 ± 8.90% across the entire database. Further analysis revealed that the performance for individuals aged 50 years and older may decline. These results demonstrate the merit of deep temporal modelling using a diverse data set and advance the state-of-the-art for HRV-based sleep stage classification. Further research is warranted into individuals over the age of 50 as performance tends to worsen in this sub-population.

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Section: Introductionmentioning

confidence: 99%

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Radha

Fonseca

Moreau³

et al. 2019

Sci Rep

131

View full text Add to dashboard Cite

show abstract

“…The NREM sleep is believed to occur in three stages, i.e., N1, N2 and N3, where each stage progressively turns into deeper sleep. Among these NREM stages, most of our sleep time is spent in the N2 stage [6], whereas the REM sleep first starts 90 minutes after we fall asleep, and is mostly associated with dreaming. During a full night's sleep we go through multiple cycles of REM and NREM sleep [6].…”

Section: Introductionmentioning

confidence: 99%

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Hasan

Shon

et al. 2020

Applied Sciences

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This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

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“…acquired from different types of sensors (such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), etc.). In particular, many studies [11][12][13][14][15][16][17] were conducted to analyze heart rate variability (HRV) alone, collected with ECG or PPG sensors, which are relatively simple to measure. For example, Vicente et al [18] implemented HRV-derived features extracted from ECG signals for drowsiness detection.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al [16] used various features obtained from PPG and respiration sensors to classify driver drowsiness and awake states. Malik et al [17] applied a series of instantaneous heart rate (IHR) obtained from ECG and PPG sensors to determine whether a subject is awake or asleep.…”

Section: Introductionmentioning

confidence: 99%

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

Lee

Shin

2019

Electronics

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This paper aims to investigate the robust and distinguishable pattern of heart rate variability (HRV) signals, acquired from wearable electrocardiogram (ECG) or photoplethysmogram (PPG) sensors, for driver drowsiness detection. As wearable sensors are so vulnerable to slight movement, they often produce more noise in signals. Thus, from noisy HRV signals, we need to find good traits that differentiate well between drowsy and awake states. To this end, we explored three types of recurrence plots (RPs) generated from the R–R intervals (RRIs) of heartbeats: Bin-RP, Cont-RP, and ReLU-RP. Here Bin-RP is a binary recurrence plot, Cont-RP is a continuous recurrence plot, and ReLU-RP is a thresholded recurrence plot obtained by filtering Cont-RP with a modified rectified linear unit (ReLU) function. By utilizing each of these RPs as input features to a convolutional neural network (CNN), we examined their usefulness for drowsy/awake classification. For experiments, we collected RRIs at drowsy and awake conditions with an ECG sensor of the Polar H7 strap and a PPG sensor of the Microsoft (MS) band 2 in a virtual driving environment. The results showed that ReLU-RP is the most distinct and reliable pattern for drowsiness detection, regardless of sensor types (i.e., ECG or PPG). In particular, the ReLU-RP based CNN models showed their superiority to other conventional models, providing approximately 6–17% better accuracy for ECG and 4–14% for PPG in drowsy/awake classification.

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

Cited by 54 publications

References 53 publications

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Sleep stage classification from heart-rate variability using long short-term memory neural networks

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Using Wearable ECG/PPG Sensors for Driver Drowsiness Detection Based on Distinguishable Pattern of Recurrence Plots

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