A wearable sensor based multi-criteria-decision-system for real-time seizure detection

• Published in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2017; pp. 2377 - 2380
• Main Authors: Ahmed, Abdullah, Ahmad, Waqas, Khan, Muhammad Jazib, Siddiqui, Shoaib Ahmed, Cheema, Hammad M.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.07.2017
• Subjects: Algorithms; Anomaly detection; Biomedical monitoring; Electrocardiography; Epilepsy; Humans; Monitoring, Physiologic; Real-time systems; Seizures; Sensors; Smartphone; Wearable Electronic Devices; Wrist
• DOI: 10.1109/EMBC.2017.8037334
• ISSN: 1557-170X

This paper presents a wireless, low power and low cost two part wearable for real-time epileptic seizure detection. Using parameters of Electro-cardiograph (ECG), Electro-dermal Activity (EDA), body motion and breathing rate (BR), a novel multi-criteria-decision-system (MCDS) is proposed that reduces false alarms and true negatives. The combination of a chest and hand worn wearable continuously senses these parameters transmitting the data to a smart phone application via BLE 4.0 where long-short-term-memory (LSTM) based anomaly detection algorithms and logistic classifiers decide on the occurrence of the seizure in real time. A 96% precision and 90% recall is achieved through testing on synthetic data.