IEEE Journal of Translational Engineering in Health and Medicine (Jan 2023)

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses

  • Shaofei Ying,
  • Lin Wang,
  • Yahui Zhao,
  • Maolin Ma,
  • Qin Ding,
  • Jiaxin Xie,
  • Dezhong Yao,
  • Srinjoy Mitra,
  • Mingyi Chen,
  • Tiejun Liu

DOI
https://doi.org/10.1109/JTEHM.2023.3248621
Journal volume & issue
Vol. 11
pp. 211 – 222

Abstract

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Objective: A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography. Methods and procedures: The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit $\Sigma $ - $\Delta $ analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups’ data were analyzed. Results: Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were $0.89 \mu $ Vpp, 1.25 GN), 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p $>$ 0.05). Conclusion: The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data. Clinical impact: The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.

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