Evaluation of Electromagnetic Noise Emitted from Light-Emitting Diode (LED) Lamps and Compatibility with Wireless Medical Telemetry Service

Ishida, Kai; Wu, Ifong; Gotoh, Kunihito; Matsumoto, Yasushi

doi:10.1587/transcom.2019hmp0003

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…We considered Gaussian noise to be BGN. According to a previous report, WMT employs a narrow band reception of 12.5 kHz; hence, most unwanted emissions generated from LED devices are band-limited to this bandwidth and are approximated as Gaussian noise 7 . Both the WMT signal and Gaussian noise were inputs to the SDR receiver through a hybrid coupler.…”

Section: Data Collection and Labelingmentioning

confidence: 99%

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Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning

Ishida

2023

Sci Rep

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In this study, we developed a novel machine-learning model to estimate the carrier-to-noise ratio (CNR) of wireless medical telemetry (WMT) using time-domain waveform data measured by a low-cost software-defined radio. With automatic estimation of CNR, the management of the electromagnetic environment of WMT can be made easier. Therefore, we proposed a machine-learning method for estimating CNR. According to the performance evaluation results by 5-segment cross-validation on 704 types of measured data, CNR was estimated with 99.5% R-square and 0.844 dB mean absolute error using a gradient boosting regression tree. The gradient boosting decision tree classifiers predicted if the CNR exceeded 30 dB with 99.5% accuracy. The proposed method is effective for investigating electromagnetic environments in clinical settings.

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Section: Data Collection and Labelingmentioning

confidence: 99%

“…We used Gaussian noise to increase BGN. In our previous study, we approximated most LED noise as Gaussian owing to band-limitation effect of the WMT receiver 7 . We intended to increase BGN as the effect of LED noise on WMT.…”

Section: Adequacy and Limitationsmentioning

confidence: 99%

Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning

Ishida

2023

Sci Rep

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“…We considered Gaussian noise to be BGN. According to a previous report, WMT employs a narrow band reception of 12.5 kHz; hence, most unwanted emissions generated from LED devices are band-limited to this bandwidth and are approximated as Gaussian noise [5]. Both the WMT signal and Gaussian noise were input to the SDR receiver through a hybrid coupler.…”

Section: Data Collection and Labelingmentioning

confidence: 99%

“…We used Gaussian noise to increase BGN. As described earlier, we can approximate most LED noise as Gaussian [5]. However, some LED noise has sequential or impulsive characteristics, with a cycle of 50 Hz derived from the Japanese mains power supply, even in the band-limited reception [25].…”

Section: Limitationsmentioning

confidence: 99%

“…The degradation of WMT signal power may occur in the following situations: the transmitter was located at a null point or far away from the receiving antenna or the aging degradation of radio frequency components, which include receiving antennas, coaxial cables, connectors, and ampli ers. However, electrical devices, especially switched-mode power supplies installed in light-emitting diode (LED) devices, may radiate unwanted emissions and increase BGN [5]. In addition, to promote the safe introduction and operation of WMT, it is necessary to consider effective frequency allocation, range of access, invasive radio waves from out of hospitals, and interference by intermodulation [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Novel Estimation Technique for the Carrier-to-Noise Ratio of Wireless Medical Telemetry Using Software-Defined Radio with Machine-Learning

Ishida

2022

Preprint

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In this study, we developed a novel machine-learning model to estimate the carrier-to-noise ratio (CNR) of wireless medical telemetry (WMT) using time-domain waveform data measured by a low-cost software-defined radio. If the CNR can be estimated automatically, the management of the electromagnetic environment of WMT can be easier. Therefore, we proposed a machine-learning method for estimating CNR. According to the performance evaluation results by 5-segment cross-validation on 704 types of measured data, CNR was estimated with 99.5% R-square and 0.844 dB mean absolute error using a gradient boosting regression tree. The gradient boosting decision tree classifiers predicted whether the CNR exceeded 30 dB with 99.5% accuracy. The proposed method is effective for investigating electromagnetic environments in clinical settings.

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Electromagnetic Compatibility of 400 MHz Radio Communications in Hospitals: Safety Management of Wireless Medical Telemetry

Ishida

Gototh

et al. 2020

J Med Syst

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Evaluation of Electromagnetic Noise Emitted from Light-Emitting Diode (LED) Lamps and Compatibility with Wireless Medical Telemetry Service

Cited by 10 publications

References 13 publications

Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning

Novel estimation technique for the carrier-to-noise ratio of wireless medical telemetry using software-defined radio with machine-learning

Novel Estimation Technique for the Carrier-to-Noise Ratio of Wireless Medical Telemetry Using Software-Defined Radio with Machine-Learning

Electromagnetic Compatibility of 400 MHz Radio Communications in Hospitals: Safety Management of Wireless Medical Telemetry

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