Intelligent Telehealth System To Support Epilepsy Diagnosis

被引:9
|
作者
Molina, Edward [1 ]
Ernesto, Camilo [2 ]
Torres, Sarmiento [2 ]
Salazar-Cabrera, Ricardo [1 ]
Lopez, Diego M. [1 ]
Vargas-Canas, Rubiel [2 ]
机构
[1] Univ Cauca, Telemat Dept, Popayan, Cauca, Colombia
[2] Univ Cauca, Dept Phys, Popayan, Cauca, Colombia
关键词
electroencephalogram; electronic health record; machine learning; diagnostic support system; EEG; EHR; SEIZURE DETECTION; AUTOMATED DIAGNOSIS; EEG; ELECTROENCEPHALOGRAPHY; DECOMPOSITION; TRANSMISSION; TRANSFORM;
D O I
10.2147/JMDH.S247878
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Availability and opportunity of epilepsy diagnostic services is a significant challenge, especially in developing countries with a low number of neurologists. The most commonly used test to diagnose epilepsy is electroencephalogram (EEG). A typical EEG recording lasts for 20 to 30 minutes; however, a specialist requires much more time to read it. Furthermore, no evidence was found in the literature on open-source systems for the cost-effective management of patient information using electronic health records (EHR) that adequately integrate EEG analysis for automatic identification of abnormal signals. Objective: To develop an integrated open-source EHR system for the management of the patients' personal, clinical, and EEG data, and for automatic identification of abnormal EEG signals. Methods: The core of the system is an EHR and telehealth service based on the OpenMRS platform. On top of that, we developed an intelligent component to automatically detect abnormal segments of EEG tests using machine learning algorithms, as well as a service to annotate and visualize abnormal segments in EEG signals. Finally, we evaluated the intelligent component and the integrated system using precision, recall, and accuracy metrics. Results: The system allowed to manage patients' information properly, store and manage the EEG tests recorded with a medical EEG device, and to detect abnormal segments of signals with a precision of 85.10%, a recall of 97.16%, and an accuracy of 99.92%. Conclusion: Digital health is a multidisciplinary field of research in which artificial intelligence is playing a significant role in boosting traditional health services. Notably, the developed system could significantly reduce the time a neurologist spends in the reading of an EEG for the diagnosis of epilepsy, saving approximately 65-75% of the time consumed. It can be used in a telehealth environment. In this way, the availability and provision of diagnostic services for epilepsy management could be improved, especially in developing countries where the number of neurologists is low.
引用
收藏
页码:433 / 445
页数:13
相关论文
共 50 条
  • [31] An Intelligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19
    V. V. Gribova
    Yu. N. Kul’chin
    M. V. Petryaeva
    D. B. Okun’
    R. I. Kovalev
    E. A. Shalfeeva
    Herald of the Russian Academy of Sciences, 2022, 92 : 511 - 519
  • [32] Intelligent Maintenance Diagnosis System
    Chan, Chu-Chai Henry
    Liu, Gino
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTING AND BIG DATA (ICCBD 2019), 2019, : 1 - 6
  • [33] IDiSSC: Edge-computing-based Intelligent Diagnosis Support System for Citrus Inspection
    Silva, Mateus Coelho
    Ferreira da Silva, Jonathan Cristovao
    Rabelo Oliveira, Ricardo Augusto
    PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS 2021), VOL 1, 2021, : 685 - 692
  • [34] Research on Intelligent Decision Support System for Automobile Fault Diagnosis Based on SWOT Analysis
    Lv, Chao
    PROCEEDINGS OF 2021 2ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INFORMATION SYSTEMS (ICAIIS '21), 2021,
  • [35] An Intelligent System for Medical Decision Support in Differential Diagnosis and Treatment of COVID-19
    Gribova, V. V.
    Kul'chin, Yu N.
    Petryaeva, M., V
    Okun, D. B.
    Kovalev, R., I
    Shalfeeva, E. A.
    HERALD OF THE RUSSIAN ACADEMY OF SCIENCES, 2022, 92 (04) : 511 - 519
  • [36] Intelligent System to support SOAQs Assessment
    Ben Salem, S.
    Belcadhi, L. Cheniti
    Braham, R.
    2013 FOURTH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY AND ACCESSIBILITY (ICTA), 2013,
  • [37] Intelligent decision support system generator
    Shi, Zhenxia
    Lu, Jukang
    Journal of Systems Engineering and Electronics, 1993, 4 (01) : 45 - 52
  • [38] An Intelligent System for decision Support in Bioinformatics
    Fiannaca, Antonino
    La Rosa, Massimo
    Peri, Daniele
    Rizzo, Riccardo
    ERCIM NEWS, 2011, (84): : 35 - 36
  • [39] An intelligent decision support system for IT outsourcing
    Buyukozkan, Gulcin
    Feyzioglu, Orhan
    FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 2006, 4223 : 1303 - 1312
  • [40] A cooperative intelligent decision support system
    Adla, Abdelkader
    Zarate, Pascale
    2006 INTERNATIONAL CONFERENCE ON SERVICE SYSTEMS AND SERVICE MANAGEMENT, VOLS 1 AND 2, PROCEEDINGS, 2006, : 763 - 769