Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Jul 3;13(7):703.
doi: 10.3390/bios13070703.

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Ganesh R Naik  1   2 , Paul P Breen  3 , Titus Jayarathna  3 , Benjamin K Tong  4   5 , Danny J Eckert  1   4 , Gaetano D Gargiulo  3   6
Affiliations

Morphic Sensors for Respiratory Parameters Estimation: Validation against Overnight Polysomnography

Ganesh R Naik et al. Biosensors (Basel). .

Abstract

Effective monitoring of respiratory disturbances during sleep requires a sensor capable of accurately capturing chest movements or airflow displacement. Gold-standard monitoring of sleep and breathing through polysomnography achieves this task through dedicated chest/abdomen bands, thermistors, and nasal flow sensors, and more detailed physiology, evaluations via a nasal mask, pneumotachograph, and airway pressure sensors. However, these measurement approaches can be invasive and time-consuming to perform and analyze. This work compares the performance of a non-invasive wearable stretchable morphic sensor, which does not require direct skin contact, embedded in a t-shirt worn by 32 volunteer participants (26 males, 6 females) with sleep-disordered breathing who performed a detailed, overnight in-laboratory sleep study. Direct comparison of computed respiratory parameters from morphic sensors versus traditional polysomnography had approximately 95% (95 ± 0.7) accuracy. These findings confirm that novel wearable morphic sensors provide a viable alternative to non-invasively and simultaneously capture respiratory rate and chest and abdominal motions.

Keywords: heart rate; morphic sensor; polysomnography; respiratory rate; wearables.

PubMed Disclaimer

Conflict of interest statement

Co-authors P.P.B., T.J. and G.D.G. are minority shareholders of Medical Monitoring Solutions (MMS) Pty. Ltd. MMS holds the rights to commercialize the morphic sensor assessed in the paper. However, MMS had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Electro-resistive band (ERB) assembly. (Top) ERB. (Left) Front-end PCB. (Right) Wiring loom, clasps, and straps.
Figure 2
Figure 2
Third-generation ERB morphic sensors fully assembled in a t-shirt (image courtesy of Medical Monitoring Solutions Pty Ltd., Sydney, Australia).
Figure 3
Figure 3
An example of respiratory rate (RR) plot of morphic sensor and airflow (polysomnography): third axis showing RR instantaneous RR for morphic sensor and airflow (polysomnography).
Figure 4
Figure 4
Bland–Altman plots—RR. Difference between windowed morphic sensor data and polysomnography airflow breaths/min plotted against the average value of the two methods. Solid horizontal lines indicate mean difference and upper/lower limits of agreement; sd: standard deviation. Note: Please refer to Figure S1 for the Bland–Altman plot of unprocessed (raw) data.
Figure 5
Figure 5
Bland–Altman plots—IBI variability. Difference between windowed morphic sensor data and polysomnography airflow breaths/min plotted against the average value of the two methods. Solid horizontal lines indicate mean difference and upper/lower limits of agreement; sd: standard deviation. Note: Please refer to Figure S2 for the Bland–Altman plot of unprocessed (raw) data.
Figure 6
Figure 6
Regression plot of the mean RR of morphic sensors and polysomnography airflow (breaths/min). The dotted lines indicate the regression line.
Figure 7
Figure 7
Regression plot of the mean IBI variability of morphic sensors and polysomnography airflow (breaths/min). The dotted lines indicate the regression line.
See this image and copyright information in PMC

References

    1. Huynh T.P., Haick H. Autonomous flexible sensors for health monitoring. Adv. Mater. 2018;30:1802337. doi: 10.1002/adma.201802337. - DOI - PubMed
    1. Cheng M., Zhu G., Zhang F., Tang W.-L., Jianping S., Yang J.-Q., Zhu L.-Y. A review of flexible force sensors for human health monitoring. J. Adv. Res. 2020;26:53–68. doi: 10.1016/j.jare.2020.07.001. - DOI - PMC - PubMed
    1. Zazoum B., Batoo K.M., Khan M.A.A. Recent advances in flexible sensors and their applications. Sensors. 2022;22:4653. doi: 10.3390/s22124653. - DOI - PMC - PubMed
    1. Huhn S., Axt M., Gunga H.-C., Maggioni M.A., Munga S., Obor D., Sié A., Boudo V., Bunker A., Sauerborn R. The impact of wearable technologies in health research: Scoping review. JMIR Mhealth Uhealth. 2022;10:e34384. doi: 10.2196/34384. - DOI - PMC - PubMed
    1. Guay P., Gorgutsa S., LaRochelle S., Messaddeq Y. Wearable Contactless Respiration Sensor Based on Multi-Material Fibers Integrated into Textile. Sensors. 2017;17:1050. doi: 10.3390/s17051050. - DOI - PMC - PubMed