In this paper, we present Smartphone-based Contactless Epilepsy Detector (SmartCED): an Android monitoring application able to diagnose neonatal clonic seizures and warn about their possible occurrences in realtime. SmartCED has, however, wider applicability so that it could also be used on adult patients. The main goal is to implement a wire-free and low-cost epilepsy diagnostic system, executing all the necessary processing directly on the smartphone. Seizures' recognition is based on a well-known statistical criterion, namely Maximum Likelihood (ML). As clonic seizures are characterized by quasi-periodic movements of some body parts, it is possible to detect the presence of a seizure by evaluating this periodicity from the video stream of the smartphone's camera. The heavy computational processing is carried out in the native code (C language) to enhance the performance. SmartCED presents a user-friendly interface in order to extend its use even to unskilled staff. In fact, although it integrates complex software from the technical point of view, the user has just to: start the App, frame the patient, and start monitoring with a simple touch.

SmartCED: An Android application for neonatal seizures detection / Cattani, L.; Saini, H. P.; Ferrari, G.; Pisani, F.; Raheli, R.. - (2016), pp. 1-6. (Intervento presentato al convegno 11th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 tenutosi a University of Sannio, ita) [10.1109/MeMeA.2016.7533708].

SmartCED: An Android application for neonatal seizures detection

Pisani F.;
2016

Abstract

In this paper, we present Smartphone-based Contactless Epilepsy Detector (SmartCED): an Android monitoring application able to diagnose neonatal clonic seizures and warn about their possible occurrences in realtime. SmartCED has, however, wider applicability so that it could also be used on adult patients. The main goal is to implement a wire-free and low-cost epilepsy diagnostic system, executing all the necessary processing directly on the smartphone. Seizures' recognition is based on a well-known statistical criterion, namely Maximum Likelihood (ML). As clonic seizures are characterized by quasi-periodic movements of some body parts, it is possible to detect the presence of a seizure by evaluating this periodicity from the video stream of the smartphone's camera. The heavy computational processing is carried out in the native code (C language) to enhance the performance. SmartCED presents a user-friendly interface in order to extend its use even to unskilled staff. In fact, although it integrates complex software from the technical point of view, the user has just to: start the App, frame the patient, and start monitoring with a simple touch.
2016
11th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016
Signal Processing; Biomedical Engineering; Instrumentation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
SmartCED: An Android application for neonatal seizures detection / Cattani, L.; Saini, H. P.; Ferrari, G.; Pisani, F.; Raheli, R.. - (2016), pp. 1-6. (Intervento presentato al convegno 11th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 tenutosi a University of Sannio, ita) [10.1109/MeMeA.2016.7533708].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1670198
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