Natural Brain-Inspired Intelligence for Screening in Healthcare Applications
In recent years, there has been a growing interest in smart e-Health systems to improve people’s quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of disease...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9422731/ |
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| author | Mahdi Naghshvarianjahromi Sumit Majumder Shiva Kumar Narjes Naghshvarianjahromi M. Jamal Deen |
| author_facet | Mahdi Naghshvarianjahromi Sumit Majumder Shiva Kumar Narjes Naghshvarianjahromi M. Jamal Deen |
| author_sort | Mahdi Naghshvarianjahromi |
| collection | DOAJ |
| description | In recent years, there has been a growing interest in smart e-Health systems to improve people’s quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of diseases like Arrhythmia at its early onset that otherwise may become fatal if not detected on time. In this work, we developed a cognitive dynamic system (CDS)-based framework for the smart e-Health system to realize an automatic screening process in the presence of a defective or abnormal dataset. A defective dataset may have poor labeling and/or lack enough training patterns. To mitigate the adverse effect of such a defective dataset, we developed a decision-making system that is inspired by the decision-making processes in humans in case of conflict-of-opinions (CoO). We present a proof-of-concept implementation of this framework to automatically identify people having Arrhythmia from single lead Electrocardiogram (ECG) traces. It is shown that the proposed CDS performs well with the diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, being in good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, respectively. The proposed CDS algorithm can be incorporated in the autonomic computing layer of a smart-e-Health-home platform to achieve a pre-defined degree of screening accuracy in the presence of a defective dataset. |
| first_indexed | 2024-12-17T23:49:23Z |
| format | Article |
| id | doaj.art-89e6f686635140dabdc13d32b5208c7b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T23:49:23Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-89e6f686635140dabdc13d32b5208c7b2022-12-21T21:28:13ZengIEEEIEEE Access2169-35362021-01-019679576797310.1109/ACCESS.2021.30775299422731Natural Brain-Inspired Intelligence for Screening in Healthcare ApplicationsMahdi Naghshvarianjahromi0https://orcid.org/0000-0002-1458-7290Sumit Majumder1https://orcid.org/0000-0003-0517-6008Shiva Kumar2https://orcid.org/0000-0002-9012-2882Narjes Naghshvarianjahromi3https://orcid.org/0000-0002-9644-3564M. Jamal Deen4https://orcid.org/0000-0002-6390-0933Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, CanadaDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON, CanadaDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON, CanadaDepartment of Obstetrics and Gynecology, Jahrom University of Medical Sciences, Jahrom, IranDepartment of Electrical and Computer Engineering, McMaster University, Hamilton, ON, CanadaIn recent years, there has been a growing interest in smart e-Health systems to improve people’s quality-of-life by enhancing healthcare accessibility and reducing healthcare costs. Continuous monitoring of health through the smart e-Health system may enable automatic diagnosis of diseases like Arrhythmia at its early onset that otherwise may become fatal if not detected on time. In this work, we developed a cognitive dynamic system (CDS)-based framework for the smart e-Health system to realize an automatic screening process in the presence of a defective or abnormal dataset. A defective dataset may have poor labeling and/or lack enough training patterns. To mitigate the adverse effect of such a defective dataset, we developed a decision-making system that is inspired by the decision-making processes in humans in case of conflict-of-opinions (CoO). We present a proof-of-concept implementation of this framework to automatically identify people having Arrhythmia from single lead Electrocardiogram (ECG) traces. It is shown that the proposed CDS performs well with the diagnosis errors of 13.2%, 9.9%, 6.6%, and 4.6%, being in good agreement with the desired diagnosis errors of 25%, 10%, 5.9%, and 2.5%, respectively. The proposed CDS algorithm can be incorporated in the autonomic computing layer of a smart-e-Health-home platform to achieve a pre-defined degree of screening accuracy in the presence of a defective dataset.https://ieeexplore.ieee.org/document/9422731/Autonomic decision-making systemautonomic computing layercognitive dynamic system (CDS)cognitive decision making (CDM)non-Gaussian and non-linear environmentNGNLE |
| spellingShingle | Mahdi Naghshvarianjahromi Sumit Majumder Shiva Kumar Narjes Naghshvarianjahromi M. Jamal Deen Natural Brain-Inspired Intelligence for Screening in Healthcare Applications IEEE Access Autonomic decision-making system autonomic computing layer cognitive dynamic system (CDS) cognitive decision making (CDM) non-Gaussian and non-linear environment NGNLE |
| title | Natural Brain-Inspired Intelligence for Screening in Healthcare Applications |
| title_full | Natural Brain-Inspired Intelligence for Screening in Healthcare Applications |
| title_fullStr | Natural Brain-Inspired Intelligence for Screening in Healthcare Applications |
| title_full_unstemmed | Natural Brain-Inspired Intelligence for Screening in Healthcare Applications |
| title_short | Natural Brain-Inspired Intelligence for Screening in Healthcare Applications |
| title_sort | natural brain inspired intelligence for screening in healthcare applications |
| topic | Autonomic decision-making system autonomic computing layer cognitive dynamic system (CDS) cognitive decision making (CDM) non-Gaussian and non-linear environment NGNLE |
| url | https://ieeexplore.ieee.org/document/9422731/ |
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