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 costeffective 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.

Epilepsy diagnosis is a medical care process that requires considerable transformation, mainly in developed countries, to provide efficient and effective care services taking into consideration the low number of available neurologists, especially in rural areas. EEG remains the most common test used to diagnose epilepsy. In recent years, there has been an increase in deep learning techniques to analyze electroencephalograms (EEG) to detect epileptiform events. These types of techniques support the epilepsy diagnostic processes performed by neurologists. There have been several approaches such as biomedical signal processing, analysis of characteristics extracted from the signals, and image analysis to detect epileptiform events. Most of the works reported in the literature, which use images, transformed the signals into a two-dimensional space interpreted as an image. However, only a few of them use the raw EEG image. This paper presents a computational model for detecting epileptiform events from raw EEG images, using convolutional neural networks and a transfer learning approach. To perform this work, 100 pediatric EEGs were collected, noting six characteristics of epileptiform events in each exam: spikes, poly-spikes, spike-and-wave, sharp waves, periodic, and a combination of them. Then, pre-trained convolutional neural networks were used, which, through transfer learning techniques, were retrained to classify possible events. The model’s performance was evaluated in terms of precision, accuracy, and Mathews’ correlation coefficient. The model offered a performance above 95% accuracy for binary classification and above 87% for multi-class classification. These results demonstrated that identifying epileptiform events from raw EEG images combined with deep learning techniques such as transfer learning is feasible. Significance: The proposed method for the evaluation of EEG tests, as a support tool for the diagnosis of epilepsy, can help to reduce the time of reading EEGs, which is very important, especially in developing countries with a limitation of a specialist in neurology.

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