Evidence map›Paper›PMID 37960581›Full record

ArticleSensors (Basel, Switzerland)2023

Minimally Invasive Hypoglossal Nerve Stimulator Enabled by ECG Sensor and WPT to Manage Obstructive Sleep Apnea.

Fen Xia, Hanrui Li, Yixi Li, Xing Liu, Yankun Xu, Chaoming Fang, Qiming Hou, Siyu Lin, Zhao Zhang, Jie Yang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 3 institutions in 2 countries.

Fen XiaZhejiang University, Hangzhou 310024, China.ORCID 0000-0002-6570-0011
Hanrui LiCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.ORCID 0000-0001-6447-4211
Yixi LiState Key Laboratory of Superlattices, Microstructures Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100045, China.
Xing LiuCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.
Yankun XuCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.ORCID 0000-0002-4410-4106
Chaoming FangCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.
Qiming HouCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.
Siyu LinCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.
Zhao ZhangSAMA Labs, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Department of Electrical and Computer Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Jie YangCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.
Mohamad SawanCenBRAIN Laboratory, School of Engineering, Westlake University, Hangzhou 310024, China.ORCID 0000-0002-4137-7272
Westlake University · CNChinese Academy of Sciences · CNKing Abdullah University of Science and Technology · SA

Funding

Westlake University 10318A992001Zhejiang Key R&D Program 2021C03002Zhejiang Leading Innovative and Entrepreneur Team Introduction Program 2020R01005
6 · The paper itself

Abstract

A hypoglossal nerve stimulator (HGNS) is an invasive device that is used to treat obstructive sleep apnea (OSA) through electrical stimulation. The conventional implantable HGNS device consists of a stimuli generator, a breathing sensor, and electrodes connected to the hypoglossal nerve via leads. However, this implant is bulky and causes significant trauma. In this paper, we propose a minimally invasive HGNS based on an electrocardiogram (ECG) sensor and wireless power transfer (WPT), consisting of a wearable breathing monitor and an implantable stimulator. The breathing external monitor utilizes an ECG sensor to identify abnormal breathing patterns associated with OSA with 88.68% accuracy, achieved through the utilization of a convolutional neural network (CNN) algorithm. With a skin thickness of 5 mm and a receiving coil diameter of 9 mm, the power conversion efficiency was measured as 31.8%. The implantable device, on the other hand, is composed of a front-end CMOS power management module (PMM), a binary-phase-shift-keying (BPSK)-based data demodulator, and a bipolar biphasic current stimuli generator. The PMM, with a silicon area of 0.06 mm2 (excluding PADs), demonstrated a power conversion efficiency of 77.5% when operating at a receiving frequency of 2 MHz. Furthermore, it offers three-voltage options (1.2 V, 1.8 V, and 3.1 V). Within the data receiver component, a low-power BPSK demodulator was ingeniously incorporated, consuming only 42 μW when supplied with a voltage of 0.7 V. The performance was achieved through the implementation of the self-biased phase-locked-loop (PLL) technique. The stimuli generator delivers biphasic constant currents, providing a 5 bit programmable range spanning from 0 to 2.4 mA. The functionality of the proposed ECG- and WPT-based HGNS was validated, representing a highly promising solution for the effective management of OSA, all while minimizing the trauma and space requirements.

Indexed as

Electric Stimulation TherapySleep Apnea, ObstructiveElectrocardiographyHumansHypoglossal NerveProstheses and ImplantsBPSKCNNECGHGNSimplantOSApower management modulerectifierstimulatorWPT

Identifiers

PMID37960581
PMCPMC10648123
OpenAlexW4388132794

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.