An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that...

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Autores principales: Hatim Z. Almarzouki, Hemaid Alsulami, Ali Rizwan, Mohammed S. Basingab, Hatim Bukhari, Mohammad Shabaz
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Publicado: Hindawi Limited 2021
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Acceso en línea:https://doaj.org/article/9bb55f923119497f959c76a51bd10b91
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spelling oai:doaj.org-article:9bb55f923119497f959c76a51bd10b912021-11-08T02:37:06ZAn Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors2040-230910.1155/2021/1233166https://doaj.org/article/9bb55f923119497f959c76a51bd10b912021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1233166https://doaj.org/toc/2040-2309In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.Hatim Z. AlmarzoukiHemaid AlsulamiAli RizwanMohammed S. BasingabHatim BukhariMohammad ShabazHindawi LimitedarticleMedicine (General)R5-920Medical technologyR855-855.5ENJournal of Healthcare Engineering, Vol 2021 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine (General)
R5-920
Medical technology
R855-855.5
spellingShingle Medicine (General)
R5-920
Medical technology
R855-855.5
Hatim Z. Almarzouki
Hemaid Alsulami
Ali Rizwan
Mohammed S. Basingab
Hatim Bukhari
Mohammad Shabaz
An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
description In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha (p < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta (p < 0.001), alpha (p < 0.01), and beta-1 (p < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.
format article
author Hatim Z. Almarzouki
Hemaid Alsulami
Ali Rizwan
Mohammed S. Basingab
Hatim Bukhari
Mohammad Shabaz
author_facet Hatim Z. Almarzouki
Hemaid Alsulami
Ali Rizwan
Mohammed S. Basingab
Hatim Bukhari
Mohammad Shabaz
author_sort Hatim Z. Almarzouki
title An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
title_short An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
title_full An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
title_fullStr An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
title_full_unstemmed An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors
title_sort internet of medical things-based model for real-time monitoring and averting stroke sensors
publisher Hindawi Limited
publishDate 2021
url https://doaj.org/article/9bb55f923119497f959c76a51bd10b91
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