An Active Self-Driven Piezoelectric Sensor Enabling Real-Time Respiration Monitoring

Rasheed, Ahmed; Iranmanesh, Emad; Li, Weiwei; Xu, Yangbing; Zhou, Qi; Ou, Hai; Wang, Kai

doi:10.3390/s19143241

Cited by 23 publications

(14 citation statements)

References 31 publications

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“…This, in turn, leads to a high false positive result and a failure in the exclusion of low-risk patients [26]. Among newly proposed methods of OSA screening are photoplethysmography [27], ballistocardiography [28], piezoelectric sensors [29], accelerometers [30], audio signals [31], [32], and radar-based systems with non-contact sensing for sleep apnea and sleep body position, respectively: [33], [34]. Most methods which could be used for home detection of OSA were recently reviewed by Mendonca et al [11].…”

Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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This study aims to evaluate the accuracy of a wireless tracheal audio sensor including the sleep body position measurement, as a possible tool to screen for obstructive sleep apnea (OSA) and also to distinguish among "positional" and "non-positional" sleep apnea patients. 30 adult subjects were asked to sleep naturally for a single night in the sleep laboratory, having simultaneous polysomnography (PSG) as a reference. The results were compared using, e.g., Pearson's and Lin's correlation coefficients, Bland-Altman plots, mean absolute error, intercept of the fitted linear model etc. We found the thresholding approach performed on normalized audio energy data achieved mean absolute error of 5.7, and average difference was −2.0. Pearson's and Lin's R coefficients were 0.92 and 0.70, respectively. The qualitative approach reached 86% accuracy (sensitivity of 96%, and specificity of 76%) when setting a binary border at the level of AHI=15 (combined normal + mild versus combined moderate + severe cases). We also checked the distribution of apneas depending on the body position. Our sensor showed a mean ± SE difference between supine apnea index and non-supine apnea index of 8.3 ± 3.2, while PSG reference of 11.2 ± 3.8. The proposed sensor might be a good complement for home sleep studies, being less disturbing and allowing for longitudinal observations and reliably showing positional OSA. PSG is the gold standard in diagnosing OSA; however, it requires to spend a night in a lab with medical staff, it provides short observation, the cost of the study is very high, and it is less suitable for children. This is why a reliable screening method is needed in sleep medicine. INDEX TERMS Obstructive sleep apnea, apnea-hypopnea index, home sleep study, tracheal sounds, audio processing, supine sleep, non-supine sleep

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Section: Discussionmentioning

confidence: 99%

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

2020

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“…Other technologies such as PENG [110][111][112][113][114] and electromagnetic generator (EMG) [115][116][117][118][119] have the capability of harvesting respiration energy and realize self-powered biomedical system as well. However, the TENGs still stands out with unique merits.…”

Section: Comparison With Other Technologiesmentioning

confidence: 99%

Respiration‐driven triboelectric nanogenerators for biomedical applications

Long

Yang

et al. 2020

EcoMat

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As a fundamental and ubiquitous body motion, respiration offers a large amount of biomechanical energy with an average power up to the Watt level through movements of multiple muscles. The energy from respiration featured with excellent stability, accessibility and continuality inspires the design and engineering of biomechanical energy harvesting devices, such as triboelectric nanogenerators (TENGs), to realize human‐powered electronics. This review article is thus dedicated to the emerging respiration‐driven TENG technology, covering fundamentals, applications, and perspectives. Specifically, the human breathing mechanics are first introduced serving as the base for the developments of TENG devices with different configurations. Biomedical applications including electrical energy generation, healthcare monitoring, air filtration, gas sensing, electrostimulation, and powering implantable medical devices are then analyzed focusing on the design‐application relationships. At last, current developments are summarized and critical challenges for driving these intriguing developments toward practical applications are discussed together with promising solutions.

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“…ITH the development of the modern medical technology, people pay more and more attention to physical health related parameters, such as respiration rate (RR) [1]. The human RR is normally detected by electronic sensor (typically piezoelectric sensor) and optical sensor [2][3][4][5][6][7]. Compared with the traditional piezoelectric sensor, the optical fiber sensor technique has many advantages such as the high sensitivity, resistence to corrosion and immunity to electromagnetic interference.…”

Section: Introductionmentioning

confidence: 99%

Wearable Optical Fiber Sensor Based on a Bend Singlemode-Multimode-Singlemode Fiber Structure for Respiration Monitoring

Pang

Liu

et al. 2021

IEEE Sensors J.

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Respiration rate (RR) is an important information related to human physiological health. A wearable optical fiber sensor for respiration monitoring based on a bend singlemode-multimodesinglemode (SMS) fiber structure, which is highly sensitive to bend, is firstly proposed and experimentally demonstrated. The sensor fastened by an elastic belt on the abdomen of a person will acquire the respiration signal when the person breaths, which will introduce front and back movement of the abdomen, and thus bend of SMS fiber structure. Short-time Fourier transform (STFT) method is employed for signal processing to extract characteristic information of both the time and frequency domain of the measured waveform, which provides accurate RR measurement. Six different SMS fiber sensors have been tested by six individuals and the experimental results demonstrated that the RR signals can be effectively monitored among different individuals, where an average Pearson Correlation Coefficient of 0.88 of the respiration signal has been achieved, which agrees very well with that of commercial belt respiration sensor. The proposed technique can provide a new wearable and portable solution for monitoring of respiratory with advantage of easy fabrication and robust to environment.

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An Active Self-Driven Piezoelectric Sensor Enabling Real-Time Respiration Monitoring

Cited by 23 publications

References 31 publications

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Joint Apnea and Body Position Analysis for Home Sleep Studies Using a Wireless Audio and Motion Sensor

Respiration‐driven triboelectric nanogenerators for biomedical applications

Wearable Optical Fiber Sensor Based on a Bend Singlemode-Multimode-Singlemode Fiber Structure for Respiration Monitoring

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