Ear‐EEG‐based sleep scoring in epilepsy: A comparison with scalp‐EEG

Jørgensen, Sofie Drevsholt; Zibrandtsen, Ivan C; Kjaer, Troels W

doi:10.1111/jsr.12921

Cited by 15 publications

(12 citation statements)

References 22 publications

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“…The AASM defines five different stages of sleep (wake, N1, N2, N3, and REM), whereas the previous R&K guidelines defined seven stages (wake, S1, S2, S3, S4, and REM). Of all the reviewed articles, only 21 performed classification of all the sleep stages defined by either of these guidelines [24,39,42,45,50,58,67,72,76,80,[88][89][90]93,101,102,105,108,109,117,121]. Amongst them, all but three [24,58,150] used EEG signals for classification, where the difference between sleep stages is known to be most obvious.…”

Section: Sleep Stagesmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

118

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Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

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Section: Sleep Stagesmentioning

confidence: 99%

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Imtiaz

2021

Sensors

118

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“…In total, 16% (8/51) of the articles described multiple body positions for sensor attachment. In 8% (4/51) of the studies, sensors were attached to the ear canal and concha [ 52 , 53 , 78 , 79 ]. Additional locations for sensor attachment included around the ear [ 36 ], near the ear adjacent to the mastoid and neck [ 46 ], in the oral cavity at the masseter muscle [ 39 ], and on the head [ 49 ].…”

Section: Resultsmentioning

confidence: 99%

“…The sensor positions used in the primary prevention studies were the ear canal [ 20 , 33 , 38 , 42 , 43 , 72 ], behind the ear [ 35 , 37 ], the concha [ 34 ], the earlobe [ 32 ], the inner ear [ 75 ], and multiple positions [ 36 ]. The sensor positions in the secondary prevention studies were the ear canal [ 21 , 22 , 40 , 44 , 45 , 47 , 50 , 58 , 60 - 65 , 67 , 68 , 71 ], behind the ear [ 51 , 54 - 57 , 59 ], around the ear [ 41 , 66 , 70 ], the earlobe [ 48 , 69 ], and multiple positions [ 39 , 46 , 49 , 52 , 53 , 78 ]. The sensor positions in the tertiary prevention studies were the ear canal [ 18 , 19 , 74 ] and multiple positions [ 76 ].…”

Section: Resultsmentioning

confidence: 99%

“…The use of these devices for secondary prevention can be facilitated by collaboration with clinicians who assess the need for continuous monitoring of specific biomarkers such as EEG, ECG, or acceleration stress. Several studies have focused on the use of earable devices for the monitoring of seizures, brain injuries, sleep disorders, arrhythmias, and myocardial infarction [ 45 , 49 - 51 , 53 - 58 , 63 ]. Clinicians can determine the specific timing of monitoring according to patients’ needs.…”

Section: Discussionmentioning

confidence: 99%

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Health-Related Indicators Measured Using Earable Devices: Systematic Review

Choi¹,

Jeon²,

Kim³

et al. 2022

JMIR Mhealth Uhealth

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Background Earable devices are novel, wearable Internet of Things devices that are user-friendly and have potential applications in mobile health care. The position of the ear is advantageous for assessing vital status and detecting diseases through reliable and comfortable sensing devices. Objective Our study aimed to review the utility of health-related indicators derived from earable devices and propose an improved definition of disease prevention. We also proposed future directions for research on the health care applications of earable devices. Methods A systematic review was conducted of the PubMed, Embase, and Web of Science databases. Keywords were used to identify studies on earable devices published between 2015 and 2020. The earable devices were described in terms of target health outcomes, biomarkers, sensor types and positions, and their utility for disease prevention. Results A total of 51 articles met the inclusion criteria and were reviewed, and the frequency of 5 health-related characteristics of earable devices was described. The most frequent target health outcomes were diet-related outcomes (9/51, 18%), brain status (7/51, 14%), and cardiovascular disease (CVD) and central nervous system disease (5/51, 10% each). The most frequent biomarkers were electroencephalography (11/51, 22%), body movements (6/51, 12%), and body temperature (5/51, 10%). As for sensor types and sensor positions, electrical sensors (19/51, 37%) and the ear canal (26/51, 51%) were the most common, respectively. Moreover, the most frequent prevention stages were secondary prevention (35/51, 69%), primary prevention (12/51, 24%), and tertiary prevention (4/51, 8%). Combinations of ≥2 target health outcomes were the most frequent in secondary prevention (8/35, 23%) followed by brain status and CVD (5/35, 14% each) and by central nervous system disease and head injury (4/35, 11% each). Conclusions Earable devices can provide biomarkers for various health outcomes. Brain status, healthy diet status, and CVDs were the most frequently targeted outcomes among the studies. Earable devices were mostly used for secondary prevention via monitoring of health or disease status. The potential utility of earable devices for primary and tertiary prevention needs to be investigated further. Earable devices connected to smartphones or tablets through cloud servers will guarantee user access to personal health information and facilitate comfortable wearing.

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“…These devices depend on recording electrodes as earpieces specially designed to fit the external ear canal. The idea has been tried for seizure detection in epileptic patients and for sleep recordings in healthy individuals [18]. An ear-EEG placed behind the ear during a seizure was also found to have a temporal waveform and frequency content similar to that of a scalp EEG [19].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

2023

IJIES

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Epilepsy patients might need continuous electroencephalography (EEG) monitoring to help them understand their own condition's improvements or their doctors to determine the frequency of the seizures. In this study, we demonstrated how a low-cost, portable EEG headband could be used to detect absence seizures in epilepsy patients. The method used is Support Vector Machine (SVM) to separate the initial limit and Machine Learning with Tensorflow to predict its confidence level. Then, we tried to test our method on 2 other patients to see its measurement divergence. Furthermore, the Tensorflow library has been applied and developed to train and classify the data, which is also one of the novelty approaches in this study. From the result, closed eyes can be detected from open-eye conditions with more than 96 % accuracy and a loss of only 2.35%. The findings demonstrate the feasibility of detecting absence seizures using only two electrodes which were TP9 and TP10 of Muse headband which is also positioned in the ear like an ear-EEG. Overall, the study successfully developed a novel method utilizing a low-cost headband to provide affordable health system access.

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Ear‐EEG‐based sleep scoring in epilepsy: A comparison with scalp‐EEG

Cited by 15 publications

References 22 publications

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Health-Related Indicators Measured Using Earable Devices: Systematic Review

Implementation of Closing Eyes Detection with Ear Sensor of Muse EEG Headband using Support Vector Machine Learning

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