scholarly journals Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Pedro Fonseca ◽  
Merel M van Gilst ◽  
Mustafa Radha ◽  
Marco Ross ◽  
Arnaud Moreau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Disorders ◽  
Recurrent Neural Networks ◽  
Clinical Diagnostics ◽  
Sleep Staging ◽  
Substantial Agreement ◽  
Body Movements ◽  
Wide Range

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

Reduced heart rate variability during sleep in long-duration spaceflight

2013 ◽  
Vol 305 (2) ◽  
pp. R164-R170 ◽  
Author(s):  
D. Xu ◽  
J. K. Shoemaker ◽  
A. P. Blaber ◽  
P. Arbeille ◽  
K. Fraser ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Substantial Reduction ◽  
Space Station ◽  
Power Spectral Analysis ◽  
Power Spectral ◽  
Long Duration ◽  
Fractal Properties ◽  
Wide Range ◽  
Simultaneous Movement

Limited data are available to describe the regulation of heart rate (HR) during sleep in spaceflight. Sleep provides a stable supine baseline during preflight Earth recordings for comparison of heart rate variability (HRV) over a wide range of frequencies using both linear, complexity, and fractal indicators. The current study investigated the effect of long-duration spaceflight on HR and HRV during sleep in seven astronauts aboard the International Space Station up to 6 mo. Measurements included electrocardiographic waveforms from Holter monitors and simultaneous movement records from accelerometers before, during, and after the flights. HR was unchanged inflight and elevated postflight [59.6 ± 8.9 beats per minute (bpm) compared with preflight 53.3 ± 7.3 bpm; P < 0.01]. Compared with preflight data, HRV indicators from both time domain and power spectral analysis methods were diminished inflight from ultralow to high frequencies and partially recovered to preflight levels after landing. During inflight and at postflight, complexity and fractal properties of HR were not different from preflight properties. Slow fluctuations (<0.04 Hz) in HR presented moderate correlations with movements during sleep, partially accounting for the reduction in HRV. In summary, substantial reduction in HRV was observed with linear, but not with complexity and fractal, methods of analysis. These results suggest that periodic elements that influence regulation of HR through reflex mechanisms are altered during sleep in spaceflight but that underlying system complexity and fractal dynamics were not altered.

scholarly journals Simple framework for constructing functional spiking recurrent neural networks

2019 ◽  
Vol 116 (45) ◽  
pp. 22811-22820 ◽  
Author(s):  
Robert Kim ◽  
Yinghao Li ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Continuous Variable ◽  
Model Parameters ◽  
Additional Parameter ◽  
Wide Range ◽  
Close Relationship ◽  
Rate Coding ◽  
One To One ◽  
The One

Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only 1 additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.

scholarly journals Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
M. M. van Gilst ◽  
B. M. Wulterkens ◽  
P. Fonseca ◽  
M. Radha ◽  
M. Ross ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Direct Application ◽  
Alternative Methods ◽  
Sleep Staging ◽  
Practical Application ◽  
Sleep Monitoring ◽  
Data Source ◽  
Variability Data ◽  
Ecg Data

Abstract Objective The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited. Results We applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p < 0.001; accuracy 73.0% versus 75.9% p < 0.001). These results show that direct application of an algorithm on a different source of data may negatively affect performance. Algorithms need to be validated using each data source and re-training should be considered whenever possible.

Differences in Recumbent, Ecstatic Trance Postures in Terms of Dream Recall, Imagined Interactions, and Heart Rate

2017 ◽  
Vol 38 (1) ◽  
pp. 31-48
Author(s):  
James M. Honeycutt
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Health Effects ◽  
Health Benefits ◽  
Scientific Research ◽  
Spiritual Practice ◽  
Dream Recall ◽  
Intrapersonal Communication ◽  
Wide Range ◽  
Imagined Interactions

Research has demonstrated the health benefits of a wide range of spiritual practices such as meditation and prayer, and similar nonspiritual practices like mindfulness. Shamanism is a spiritual practice that uses dream recall during ecstatic trances, and scientific research has shown health benefits of dream recall. Researchers have long realized the importance of intrapersonal communication occurring in daydreams, called imagined interactions (IIs), and recently extended the theory to include night dreams. IIs serve six functions: rehearsal, relational maintenance, catharsis, conflict-linkage, self-understanding, and compensation. This study investigates the health effects of ecstatic posture on dream recall in conjunction with the functions of IIs. Results indicate that ecstatic posture while recalling dreams is associated with both heart rate and heart rate variability. However, the II function determines whether the effect is positive or negative.

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