Hearables: Feasibility and Validation of In-Ear Electrocardiogram

Hammour, Ghena; Metin, Yarici,; Rosenberg, Wilhelm von; Mandic, Danilo P.

doi:10.1109/embc.2019.8857547

Cited by 25 publications

(25 citation statements)

References 10 publications

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“…With growing popularity of so called Hearables [23], the ear has come to light as a attractive location for wearable sensors, with its prominence in the ear-EEG [24] [25] and ear-ECG domains [26]. Similarly, the in-ear location is rapidly emerging as a favourable site for wearable photoplethysmography and by extension pulse oximetry, due to many benefits over commonly used finger probes.…”

Section: B In-ear Photoplethysmographymentioning

confidence: 99%

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD

Davies

Bachtiger

Williams

et al. 2022

IEEE Trans. Biomed. Eng.

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An ability to extract detailed spirometry-like breathing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

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Section: B In-ear Photoplethysmographymentioning

confidence: 99%

Wearable In-Ear PPG: Detailed Respiratory Variations Enable Classification of COPD

Davies

Bachtiger

Williams

et al. 2022

IEEE Trans. Biomed. Eng.

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“…Reported uses of earable devices were diet-related activity monitoring (9/51, 18% of the articles) [ 19 , 32 - 39 ]; brain status monitoring (7/51, 14% of the articles) [ 40 - 45 , 78 ]; cardiovascular disease (CVD) monitoring (5/51, 10% of the articles) [ 46 - 50 ]; central nervous system (CNS) disease monitoring and diagnosis (5/51, 10% articles) [ 51 - 54 , 79 ]; head injury monitoring (4/51, 8% of the articles) [ 55 - 58 ]; and monitoring of heart status [ 59 - 61 ], respiration [ 20 , 62 ], and sleep disorders [ 63 , 64 ] (7/51, 14% of the articles). Earable devices were used to monitor multiple diseases and health conditions in 16% (8/51) of the studies, namely, brain, cardiac, and respiratory functions [ 65 ]; cardiovascular status, sweating, and motion [ 66 ]; HR and breathing rate [ 67 ]; respiration and posture [ 21 ]; metabolic functions in relation to fever, insomnia, fatigue, and depression [ 22 ]; gait classification [ 68 ]; cardiovascular, metabolic, and mental disorders, including stress and pain response [ 69 ]; and chronic stress, cognitive dysfunctions, depression, and CVD [ 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…EEG was used for monitoring in 22% (11/51) of the studies [ 40 , 41 , 43 - 45 , 51 - 54 , 63 , 78 ], body movements were monitored in 12% (6/51) of the studies [ 18 - 21 , 33 , 75 ], and body temperature was monitored in 10% (5/51) of the studies [ 22 , 64 , 71 - 73 ]. Photoplethysmography (PPG) was used for monitoring in 8% (4/51) of the studies [ 32 , 48 , 60 , 69 ], acceleration stress was monitored in 8% (4/51) of the studies [ 55 - 58 ], and ECG was used for monitoring in 6% (3/51) of the studies [ 49 , 50 , 59 ]. A total of 25% (13/51) of the articles reported the monitoring of multiple exposure- and disease-related biomarkers (ie, EEG outputs, breathing signals, and mechanical plethysmography [MPG] outputs [ 65 ]; ECG outputs, lactate levels, and head acceleration [ 66 ]; ECG, ballistocardiography, and PPG outputs [ 46 ]; PPG and bioacoustics outputs and vibrations [ 36 ]; PPG and bioacoustics outputs [ 34 ]; ear canal shape, electromyography [EMG] outputs, and occlusal force [ 39 ]; acceleration stress, body temperature, and HR variability [HRV] [ 68 ]; EEG, electro-oculography, and EMG outputs [ 42 ]; EEG and ECG outputs [ 70 ]; ear canal shape, muscle movement, and acoustic signals [ 35 ]; EMG outputs, ear canal pressure, and muscle movement [ 38 ]; PPG outputs and air pressure [ 61 ]; and body potential, EMG outputs, and capacitance [ 74 ]).…”

Section: Resultsmentioning

confidence: 99%

“…Another sensor combination was optical plus acoustic or mechanical sensors [ 21 , 34 - 36 ]. In total, 51% (26/51) of the articles described devices for the ear canal, which was the primary device location, used in almost half of the studies [ 18 - 22 , 33 , 38 , 40 , 42 - 45 , 47 , 50 , 58 , 60 - 65 , 67 , 68 , 71 , 72 , 74 ]. The devices were positioned behind the ear in 16% (8/51) of the studies [ 35 , 37 , 51 , 54 - 57 , 59 ] and around the ear [ 41 , 66 , 70 ] or on the earlobe [ 32 , 48 , 69 ] in 12% (6/51) of the studies.…”

Section: Resultsmentioning

confidence: 99%

“…Three different sensor types (electric, photo, and a combined sensor [ 46 ]) and four different biomarkers (ECG, PPG, EPWs, and combined biomarkers) were used to measure CVD-related outcomes in 10% (5/51) of the studies. In total, 4% (2/51) of the articles reported the use of an electric sensor for ECG detection [ 49 , 50 ], 2% (1/51) of the articles described the use of a photosensor for PPG detection [ 48 ], and another article (1/51, 2%) described the use of an electrical sensor for EPW detection [ 47 ].…”

Section: Resultsmentioning

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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