A wearable sensor based multi-criteria-decision-system for real-time seizure detection
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 reduce...
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Published in | 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Vol. 2017; pp. 2377 - 2380 |
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Main Authors | , , , , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
United States
IEEE
01.07.2017
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Abstract | 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. |
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AbstractList | 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. |
Author | Ahmed, Abdullah Khan, Muhammad Jazib Cheema, Hammad M. Ahmad, Waqas Siddiqui, Shoaib Ahmed |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29060376$$D View this record in MEDLINE/PubMed |
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Snippet | This paper presents a wireless, low power and low cost two part wearable for real-time epileptic seizure detection. Using parameters of Electro-cardiograph... |
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SubjectTerms | Algorithms Anomaly detection Biomedical monitoring Electrocardiography Epilepsy Humans Monitoring, Physiologic Real-time systems Seizures Sensors Smartphone Wearable Electronic Devices Wrist |
Title | A wearable sensor based multi-criteria-decision-system for real-time seizure detection |
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