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Session: Telemedicine - and Fetal ECG
Session starts: Friday 25 January, 13:00
Presentation starts: 13:45
Room: Lamoraalzaal

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Frank Roberts (Technische Universiteit Eindhoven)
Gabriele Spina (Technische Universiteit Eindhoven)
Constantin Ungureanu (HOBO Heeze)
Oliver Amft (Technische Universiteit Eindhoven)

Abstract:
Patients suffering from epileptic seizures face various difficulties in daily life. In particular, major seizures may lead patients to remain unconscious and thus could result in accidents and situations, where the patient needs external help. Various approaches have been made to build sensor-based monitoring solutions to determine seizure events, e.g. [1, 2]. Most approaches used a single modalities based on physical acceleration of limbs or measured heart activity, which often results in seizure detection errors. In contrast, home monitoring systems, e.g. based on video cameras, cannot cover activities outside and typically have high cost and maintenance needs. Smartphone-based activity monitoring could provide important advantages for a daytime seizure monitoring system: a smartphone could be worn by the patient with additional ubiquitous sensors placed at relevant body positions. By combining the different information sources, the system could robustly identify seizure events, potentially calling for assistance and providing seizure statistics to caregivers. However, existing smartphone-based sensor data processing solutions are highly limited in the type of sensors and algorithms that can be used. Thus patients cannot choose and interoperate sensors, and systems cannot be personalized according to patient needs. In this work we present a new unobtrusive, long-term monitoring system running on Android smartphones that integrates multiple sensing modalities especially suitable for seizure detection. It uses an open source toolbox [3] as processing component to rapidly prototype different classification algorithms. Our system uses a configurable architecture that already supports several sensors from different manufacturers besides different wireless communication protocols (Bluetooth, ANT, etc.) and real-time visualization of the streamed data and results. Due to the low frequencies of epileptic seizures in patients during daytime, the developed framework has been evaluated in a case study where expert actors were asked to simulate epileptic seizures during selected everyday activities such as walking, brushing teeth, shaking hands. In this study an inertial motion sensing unit [4,5] was placed on the wrist and ECG electrodes [4] at the chest. Data were analyzed and used to train a classification algorithm running in real-time on the smartphone. We present in this work our first performance results of using the smartphone-based system for epileptic seizure detection. Our initial tests showed that our framework in combination with the additional multimodal on-body sensors can be used as a flexible choice for recording and detecting epileptic seizures during daytime. While recording acted seizures was sufficient for this initial evaluation of the system, we plan to use the system with patients in the future.