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Dave D, Vyas K, Branan K, McKay S, DeSalvo DJ, Gutierrez-Osuna R, Cote GL, Erraguntla M. Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry. J Diabetes Sci Technol 2024; 18:351-362. [PMID: 35927975 PMCID: PMC10973850 DOI: 10.1177/19322968221116393] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes. METHODS In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model. RESULTS The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone. CONCLUSIONS Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.
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Affiliation(s)
- Darpit Dave
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
| | - Kathan Vyas
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Kimberly Branan
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Siripoom McKay
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital Clinical Care Center, Houston, TX, USA
| | - Daniel J. DeSalvo
- Baylor College of Medicine, Houston, TX, USA
- Texas Children’s Hospital Clinical Care Center, Houston, TX, USA
| | - Ricardo Gutierrez-Osuna
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | - Gerard L. Cote
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | - Madhav Erraguntla
- Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA
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Namkoong M, McMurray J, Branan K, Hernandez J, Gandhi M, Ida-Oze S, Cote G, Tian L. Contact pressure-guided wearable dual-channel bioimpedance device for continuous hemodynamic monitoring. Adv Mater Technol 2024; 9:2301407. [PMID: 38665229 PMCID: PMC11044990 DOI: 10.1002/admt.202301407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Indexed: 04/28/2024]
Abstract
Wearable devices for continuous monitoring of arterial pulse waves have the potential to improve the diagnosis, prognosis, and management of cardiovascular diseases. These pulse wave signals are often affected by the contact pressure between the wearable device and the skin, limiting the accuracy and reliability of hemodynamic parameter quantification. Here, we report a continuous hemodynamic monitoring device that enables the simultaneous recording of dual-channel bioimpedance and quantification of pulse wave velocity (PWV) used to calculate blood pressure (BP). Our investigations demonstrate the effect of contact pressure on bioimpedance and PWV. The pulsatile bioimpedance magnitude reached its maximum when the contact pressure approximated the mean arterial pressure of the subject. We employed PWV to continuously quantify BP while maintaining comfortable contact pressure for prolonged wear. The mean absolute error and standard deviation of the error compared to the reference value were determined to be 0.1 ± 3.3 mmHg for systolic BP, 1.3 ± 3.7 mmHg for diastolic BP, and -0.4 ± 3.0 mmHg for mean arterial pressure when measurements were conducted in the lying down position. This research demonstrates the potential of wearable dual-bioimpedance sensors with contact pressure guidance for reliable and continuous hemodynamic monitoring.
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Affiliation(s)
- Myeong Namkoong
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Justin McMurray
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Kimberly Branan
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Joanna Hernandez
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Mishika Gandhi
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Samuel Ida-Oze
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Gerard Cote
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Limei Tian
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
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Namkoong M, Baskar B, Singh L, Guo H, McMurray J, Branan K, Rahman MS, Hsiao CT, Kuriakose J, Hernandez J, Arikan AA, Garza-Rivera LE, Coté GL, Tian L. Add-on soft electronic interfaces for continuous cuffless blood pressure monitoring. Adv Mater Technol 2023; 8:2300158. [PMID: 37701636 PMCID: PMC10495086 DOI: 10.1002/admt.202300158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Indexed: 09/14/2023]
Abstract
Continuous monitoring of arterial blood pressure is clinically important for the diagnosis and management of cardiovascular diseases. Soft electronic devices with skin-like properties show promise in a wide range of applications, including the human-machine interface, the Internet of things, and health monitoring. Here, we report the use of add-on soft electronic interfaces to address the connection challenges between soft electrodes and rigid data acquisition circuitry for bioimpedance monitoring of cardiac signals, including heart rate and cuffless blood pressure. Nanocomposite films in add-on electrodes provide robust electrical and mechanical contact with the skin and the rigid circuitry. We demonstrate bioimpedance sensors composed of add-on electrodes for continuous blood pressure monitoring with high accuracy. Specifically, the bioimpedance collected with add-on nanocomposite electrodes shows a signal-to-noise ratio of 37.0 dB, higher than the ratio of 25.9 dB obtained with standard silver/silver chloride (Ag/AgCl gel) electrodes. Although the sample set is low, the continuously measured systolic and diastolic blood pressure offer accuracy of -2.0 ± 6.3 mmHg and -4.3 ± 3.9 mmHg, respectively, confirming the grade A performance based on the IEEE standard. These results show promise in bioimpedance measurements with add-on soft electrodes for cuffless blood pressure monitoring.
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Affiliation(s)
- Myeong Namkoong
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Balaji Baskar
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Lakhvir Singh
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Heng Guo
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Justin McMurray
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Kimberly Branan
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Md. Saifur Rahman
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Chin-To Hsiao
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Josh Kuriakose
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Joanna Hernandez
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | | | | | - Gerard L. Coté
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
| | - Limei Tian
- Department of Biomedical Engineering, and Center for Remote Health Technologies and Systems, Texas A&M University, College Station, TX 77843, USA
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