Noncontact Monitoring of Cardiorespiratory Activity by Electromagnetic Coupling

Teichmann, Daniel; Foussier, Jérôme; Jia, Jinping; Leonhardt, Steffen; Walter, Marian

doi:10.1109/tbme.2013.2248732

Cited by 60 publications

(37 citation statements)

References 21 publications

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“…The load of the secondary coil consists of a conductive part ( R body ) and a capacitive part ( C body ) that model tissue conductivity and permittivity, respectively Using this model and applying analytical signals (here, sinusoidal), the complex reflected coil impedance can be expressed by [15]:

Z_{coil, r} = \frac{U_{coil}}{I_{coil}} = R_{coil} + j ω L_{coil} - \frac{ω^{3} C_{body} M_{12}^{2}}{j ω C_{body} (R_{body} + j ω L_{eddy}) + 1} ​

…”

Section: Methodsmentioning

confidence: 99%

“…The coil is part of a Colpitts oscillator. The frequency change due to a variation in coil impedance can be measured by a frequency counter realized by using the counter input of a microcontroller (MSP430F5435A, Texas Instruments, Dallas, TX, USA) as described in [15]. The oscillator together with the microcontroller is placed on a flexible, one side-printed circuit board (with a physical dimension of 37 × 50 mm).…”

Section: Methodsmentioning

confidence: 99%

“…A similar (but rigid) system for use on a chair was introduced by our research group in [15]. To the authors' knowledge, the system described here is the first wearable and textile-integrated version of a magnetic eddy current induction or electromagnetic coupling sensor.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The MAIN Shirt: A Textile-Integrated Magnetic Induction Sensor Array

Teichmann

Kühn²,

Leonhardt

et al. 2014

Sensors

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A system is presented for long-term monitoring of respiration and pulse. It comprises four non-contact sensors based on magnetic eddy current induction that are textile-integrated into a shirt. The sensors are technically characterized by laboratory experiments that investigate the sensitivity and measuring depth, as well as the mutual interaction between adjacent pairs of sensors. The ability of the device to monitor respiration and pulse is demonstrated by measurements in healthy volunteers. The proposed system (called the MAIN (magnetic induction) Shirt) does not need electrodes or any other skin contact. It is wearable, unobtrusive and can easily be integrated into an individual's daily routine. Therefore, the system appears to be a suitable option for long-term monitoring in a domestic environment or any other unsupervised telemonitoring scenario.

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Z_{coil, r} = \frac{U_{coil}}{I_{coil}} = R_{coil} + j ω L_{coil} - \frac{ω^{3} C_{body} M_{12}^{2}}{j ω C_{body} (R_{body} + j ω L_{eddy}) + 1} ​

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

The MAIN Shirt: A Textile-Integrated Magnetic Induction Sensor Array

Teichmann

Kühn²,

Leonhardt

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The measuring principles are based on phase difference between exciting coil and receiving coil. Teichmann et al in [15,16] discussed the capacitive or inductive coupling for recording cardiorespiratory activity. The proposed system contains inductive sensor head and capacitive sensor head.…”

Section: Introductionmentioning

confidence: 99%

Cardiopulmonary Signal Detection Based on Magnetic Induction

et al. 2017

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Cardiopulmonary signal contains vital and rich physiological information, which is very useful for clinical diagnosis and home healthcare. Convectional monitoring methods still need uncomfortable sensors or are expensive for nonprofessional common consumers. In this paper, we proposed a cardiopulmonary signal detection method based on electromagnetic induction. Based on thoracic volume variation affecting the biological impedance, which can be detected by magnetic induction, our idea is to develop a not complicated and practical measuring platform and exploit the intrinsic properties of cardiopulmonary signals with respiratory and heart rate. We have some theoretical analysis about the relationship of thoracic volume with biological impedance, sensor-head parameters, and the optimal measuring position. Then a whole measuring system has been designed and evaluated. The respiratory and heart rate obtained from the proposed method are not significantly different from the reference method (noncontact BCG).

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“…Different methods are used to increase the accuracy of monitor response. One of these methods relies on hardware development to achieve better noise tolerance [11,12]. Another approach is to improve signal quality in software by designing better digital filters for high and low noise suppression [13,14].…”

Section: Introductionmentioning

confidence: 99%

Sensor fusion methods for reducing false alarms in heart rate monitoring

Borges

Brusamarello

2015

J Clin Monit Comput

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Automatic patient monitoring is an essential resource in hospitals for good health care management. While alarms caused by abnormal physiological conditions are important for the delivery of fast treatment, they can be also a source of unnecessary noise because of false alarms caused by electromagnetic interference or motion artifacts. One significant source of false alarms is related to heart rate, which is triggered when the heart rhythm of the patient is too fast or too slow. In this work, the fusion of different physiological sensors is explored in order to create a robust heart rate estimation. A set of algorithms using heart rate variability index, Bayesian inference, neural networks, fuzzy logic and majority voting is proposed to fuse the information from the electrocardiogram, arterial blood pressure and photoplethysmogram. Three kinds of information are extracted from each source, namely, heart rate variability, the heart rate difference between sensors and the spectral analysis of low and high noise of each sensor. This information is used as input to the algorithms. Twenty recordings selected from the MIMIC database were used to validate the system. The results showed that neural networks fusion had the best false alarm reduction of 92.5 %, while the Bayesian technique had a reduction of 84.3 %, fuzzy logic 80.6 %, majority voter 72.5 % and the heart rate variability index 67.5 %. Therefore, the proposed algorithms showed good performance and could be useful in bedside monitors.

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Noncontact Monitoring of Cardiorespiratory Activity by Electromagnetic Coupling

Cited by 60 publications

References 21 publications

The MAIN Shirt: A Textile-Integrated Magnetic Induction Sensor Array

The MAIN Shirt: A Textile-Integrated Magnetic Induction Sensor Array

Cardiopulmonary Signal Detection Based on Magnetic Induction

Sensor fusion methods for reducing false alarms in heart rate monitoring

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