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.
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.
Authors: Kirsten M Fiest; Khara M Sauro; Samuel Wiebe; Scott B Patten; Churl-Su Kwon; Jonathan Dykeman; Tamara Pringsheim; Diane L Lorenzetti; Nathalie Jetté Journal: Neurology Date: 2016-12-16 Impact factor: 9.910
Authors: David B Burkholder; Jeffrey W Britton; Vijayalakshmi Rajasekaran; Rachel R Fabris; Perumpillichira J Cherian; Kristen M Kelly-Williams; Elson L So; Katherine C Nickels; Lily C Wong-Kisiel; Terrence D Lagerlund; Gregory D Cascino; Gregory A Worrell; Elaine C Wirrell Journal: Neurology Date: 2016-03-16 Impact factor: 9.910
Authors: Magaly M Blas; Walter H Curioso; Patricia J Garcia; Mirko Zimic; Cesar P Carcamo; Jesus M Castagnetto; Andres G Lescano; Diego M Lopez Journal: BMJ Open Date: 2011-01-01 Impact factor: 2.692
Authors: Eman Sobhy Elsaid Hussein; Abdullah Mohammed Al-Shenqiti; Reda Mohamed El-Sayed Ramadan Journal: Int J Environ Res Public Health Date: 2022-10-04 Impact factor: 4.614