Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

Krejcar, Ondřej; Jirka, Jakub; Janckulik, Dalibor

doi:10.3390/s110606037

Cited by 61 publications

(34 citation statements)

References 19 publications

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“…The changes in common occurring between the EOG signal and the AS signal have produced significant data (p = 0.005 < 0.05) with a 0.97 correlation out of 1. This is in line with what is described in studies of sleep apnea [15,16], since unstable moments in breathing have been found to coincide with the EOG signal and indicating that the subject's rest has been affected.…”

Section: Discussionsupporting

confidence: 77%

Analysis of the sleep quality of elderly people using biomedical signals

Moreno-Alsasua

Garcia-Zapirain

Zorrilla

2015

BME

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Abstract. This paper presents a technical solution that analyses sleep signals captured by biomedical sensors to find possible disorders during rest. Specifically, the method evaluates electrooculogram (EOG) signals, skin conductance (GSR), air flow (AS), and body temperature. Next, a quantitative sleep quality analysis determines significant changes in the biological signals, and any similarities between them in a given time period. Filtering techniques such as the Fourier transform method and IIR filters process the signal and identify significant variations. Once these changes have been identified, all significant data is compared and a quantitative and statistical analysis is carried out to determine the level of a person's rest. To evaluate the correlation and significant differences, a statistical analysis has been calculated showing correlation between EOG and AS signals (p=0,005), EOG, and GSR signals (p=0,037) and, finally, the EOG and Body temperature (p=0,04). Doctors could use this information to monitor changes within a patient.

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Section: Discussionsupporting

confidence: 77%

Analysis of the sleep quality of elderly people using biomedical signals

Moreno-Alsasua

Garcia-Zapirain

Zorrilla

2015

BME

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“…This includes PSG validation of apps using sleep detection algorithms based on the sound detection capabilities of smartphone microphones. 21 Intriguingly, several methods have been recently described to use existing smartphone technology (camera, optical pulse sensor, and photoplethysmography) to reliably monitor heart rate variability and respiratory rate. [22][23][24][25][26] The development of smartphone apps that use autonomic measures in such a manner might present an avenue for more clinically useful sleep staging.…”

Section: (20%)mentioning

confidence: 99%

Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

Bhat

Ferraris

Gupta

et al. 2015

Journal of Clinical Sleep Medicine

113

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Study Objectives: Several inexpensive, readily available smartphone apps that claim to monitor sleep are popular among patients. However, their accuracy is unknown, which limits their widespread clinical use. We therefore conducted this study to evaluate the validity of parameters reported by one such app, the Sleep Time app (Azumio, Inc., Palo Alto, CA, USA) for iPhones. Methods: Twenty volunteers with no previously diagnosed sleep disorders underwent in-laboratory polysomnography (PSG) while simultaneously using the app. Parameters reported by the app were then compared to those obtained by PSG. In addition, an epoch-by-epoch analysis was performed by dividing the PSG and app graph into 15-min epochs. S martphones are now ubiquitous. As their technological capabilities continue to improve, both consumers and healthcare personnel are constantly fi nding new and innovative uses for smartphone apps in the fi eld of health and medicine.1 Apps have been incorporated into the practice of medical specialties as diverse as diabetology and neurosurgery, and have found a role in activities ranging from interpretation of radiology imaging to smoking cessation counseling. Sleep related concerns are commonplace as well, and given the burgeoning popularity and easy availability of inexpensive apps that purport to monitor multiple physiological parameters, it is not surprising that several apps have been designed to evaluate sleep quality.2 The inexorable permeation of smartphones into the fi eld of sleep medicine has resulted in the development of promising apps that screen for obstructive sleep apnea (OSA) 3,4 and periodic limb movements in sleep (PLMS). S C I E N T I F I C I N V E S T I G AT I O N Sinsomnia, circadian rhythm disorders, and hypersomnolence to have objective data about sleep patterns in order to make diagnoses and treatment recommendations. There are several apps that allow users to report and analyze their sleep quality and duration, 6 but such data are often intrinsically subjective. In-laboratory polysomnography (PSG) is the gold standard for BRIEF SUMMARY Current Knowledge/Study Rationale: There are several preexisting, widely available, inexpensive smartphone apps designed to monitor sleep, but it is unclear whether they have clinical utility. Our goal was to systematically compare the results obtained by using one such app, the Sleep Time app (Azumio, Inc.) to the gold standard, polysomnography (PSG). Study Impact: Our study shows that the absolute parameters and sleep staging reported by the Sleep Time app (Azumio, Inc.) for iPhones correlate poorly with PSG. Further studies comparing app sleep-wake detection to actigraphy may help elucidate its potential clinical utility.

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“…The recent advent of mobile technology has spawned numerous smartphones equipped with MEMS systems and microphones that can be used to detect movement in the sleep [4,9]. The phone is usually placed on the mattress, e.g.…”

Section: Related Workmentioning

confidence: 99%

MoviBed - Sleep Analysis Using Capacitive Sensors

Djakow

Braun

Marinc

2014

Lecture Notes in Computer Science

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Abstract. Sleep disorders are a wide-spread phenomenon that can gravely affect personal health and well-being. An individual sleep analysis is a first step in identifying unusual sleeping patterns and providing suitable means for further therapy and preventing escalation of symptoms. Typically such an analysis is an intrusive method and requires the user to stay in a sleep laboratory. In this work we present a method for detecting sleep patterns based on invisibly installed capacitive proximity sensors integrated into the bed frame. These sensors work with weak electric fields and do not disturb sleep. Using the movements of the sleeping person we are able to provide a continuous analysis of different sleep phases. The method was tested in a prototypical setup over multiple nights.

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Use of Mobile Phones as Intelligent Sensors for Sound Input Analysis and Sleep State Detection

Cited by 61 publications

References 19 publications

Analysis of the sleep quality of elderly people using biomedical signals

Analysis of the sleep quality of elderly people using biomedical signals

Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography

MoviBed - Sleep Analysis Using Capacitive Sensors

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