Sleepiness classification by thoracic respiration using support vector machine

Igasaki, Tomohiko; Nagasawa, Kazuki; Akbar, Izzat Aulia; Kubo, Nao

doi:10.1109/bmeicon.2016.7859630

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…In addition to motion, sleep is another subject behavior that has potentially both neural and artifactual correlates. During sleep, respiratory patterns may change (Igasaki et al, 2016), which may lead to more artifactual physiologically driven BOLD fluctuations. In addition, sleeping or drowsy subjects may also exhibit different amounts of head motion.…”

Section: Examining the Effects Of Sleep On Resting State Fmrimentioning

confidence: 99%

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Glasser¹,

Coalson²,

Bijsterbosch

et al. 2017

Preprint

101

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Temporal fluctuations in functional Magnetic Resonance Imaging (fMRI) have been profitably used to study brain activity and connectivity for over two decades. Unfortunately, fMRI data also contain structured temporal "noise" from a variety of sources, including subject motion, subject physiology, and the MRI equipment. Recently, methods have been developed to automatically and selectively remove spatially specific structured noise from fMRI data using spatial Independent Components Analysis (ICA) and machine learning classifiers. Spatial ICA is particularly effective at removing spatially specific structured noise from high temporal and spatial resolution fMRI data of the type acquired by the Human Connectome Project and similar studies. However, spatial ICA is unable (mathematically, by design) to separate spatially widespread "global" structured noise from fMRI data (e.g., blood flow modulations from subject respiration). No methods currently exist to selectively and completely remove global structured noise while retaining the global signal from neural activity. This has left the field in a quandary-to do or not to do global signal regression-given that both choices have substantial downsides. Here we show that temporal ICA can selectively segregate and remove global structured noise while retaining global neural signal in both task-based and resting state fMRI data. We compare the results before and after temporal ICA cleanup to those from global signal regression and show that temporal ICA cleanup removes the biases caused by global physiological noise without inducing the biases of global signal regression. Thus, we show that temporal ICA cleanup provides a "best of both worlds" solution to the global signal and global noise dilemma and that temporal ICA itself unlocks interesting neurobiological insights from fMRI data.

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Section: Examining the Effects Of Sleep On Resting State Fmrimentioning

confidence: 99%

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Glasser¹,

Coalson²,

Bijsterbosch

et al. 2017

Preprint

101

View full text Add to dashboard Cite

show abstract

“…Recent research shows how a subject’s respiration signal can be used for drowsiness detection while driving a car [ 10 , 11 ]. Also emotion classification from respiration and other physiological features has been a focus in some studies [ 12 , 13 ].…”

Section: Related Workmentioning

confidence: 99%

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Kempfle

Laerhoven

2020

Sensors

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Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.

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“…Previous studies on patients' daytime sleepiness prediction have utilized clinical data or laboratory measurements [5,6]. To our knowledge, sleepiness prediction with wearable sensors in free-living settings has not been studied extensively: Igasaki et al predicted sleepiness from respiratory signals with support vector machines during a simulated drive, achieving an 89 % accuracy, whereas Bao et al used wearable body temperature sensing to assess sleepiness over two days [7,8]. Both studies only included a small sample (6-7) of healthy adults measured for a short time in simulated or restricted free-living settings.…”

Section: Introductionmentioning

confidence: 99%

Predicting Daytime Sleepiness from ECG-based Respiratory Rate Using Deep Learning

Antikainen¹,

Rehman²,

Ahmaniemi³

et al. 2022

Computing in Cardiology Conference (CinC)

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Daytime sleepiness impairs the activities of daily living, especially in chronic disease patients. Typically, daytime sleepiness is measured with subjective patient reported outcomes (PROs), which could be prone to recall bias. Objective measures of daytime sleepiness, which are sensitive to change, would benefit the assessment of disease states and novel therapies that impact the quality of life. The presented study aimed to predict daytime sleepiness from two hours of continuously measured respiratory rate using a 1-dimensional convolutional neural network. A wearable biosensor was used to continuously measure electrocardiography (ECG) based respiratory rate, while the participants (N=82) were asked to fill in Karolinska Sleepiness Scale three times a day. Considering the need for a sleepiness measure for chronic diseases, neurodegenerative disease (NDD, N=14) patients, immune-mediated inflammatory disease (IMID, N=42) patients, as well as healthy participants (N=26) were included in the study. The diseaseagnostic model achieved an accuracy of 63% between nonsleepy and sleepy states. The result demonstrates the potential of using respiratory rate with deep learning for an objective measure of daytime sleepiness.

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Sleepiness classification by thoracic respiration using support vector machine

Cited by 3 publications

References 4 publications

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Predicting Daytime Sleepiness from ECG-based Respiratory Rate Using Deep Learning

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