M2D2: Maximum-Mean-Discrepancy Decoder for Temporal Localization of Epileptic Brain Activities

Autor: Alireza Amirshahi, Anthony Thomas, Amir Aminifar, Tajana Rosing, David Atienza

Publikováno v: IEEE journal of biomedical and health informatics.

Recent years have seen growing interest in leveraging deep learning models for monitoring epilepsy patients based on electroencephalographic (EEG) signals. However, these approaches often exhibit poor generalization when applied outside of the settin

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b2e0f753a8b9acba08547ee61434497
https://pubmed.ncbi.nlm.nih.gov/36136930

Intelligent Edge Biomedical Sensors in the Internet of Things (IoT) Era

Autor: Elisabetta De Giovanni, Farnaz Forooghifar, Gregoire Surrel, Tomas Teijeiro, Miguel Peon, Amir Aminifar, David Atienza Alonso

Publikováno v: Emerging Computing: From Devices to Systems ISBN: 9789811674860

The design of reliable wearable systems for real-time and long-term monitoring presents major challenges, although they are poised as the next frontier of innovation in the context of Internet-of-Things (IoT) to provide personalized healthcare. This

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::173ae3b287c0c24c0de871a9d8dc4fe8
https://doi.org/10.1007/978-981-16-7487-7_13

Secure Cloud Control Using Verifiable Computation

Autor: Rouhollah Mahfouzi, Amir Aminifar, Zebo Peng, Petru Eles, Soheil Samii

Publikováno v: COINS

Security for outsourced control applications can be provided if the physical plant is enabled with a mechanism to verify the control signal received from the cloud. Recent developments in modern cryptography claim the applicability of verifiable comp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1eb5746ec180ca305ed579f94a737fca
https://doi.org/10.1109/coins51742.2021.9524115

Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices

Autor: Sándor Beniczky, David Atienza, Farnaz Forooghifar, Amir Aminifar, Jesper Jeppesen, Tomas Teijeiro, Amin Aminifar

Publikováno v: Forooghifar, F, Aminifar, A, Teijeiro, T, Aminifar, A, Jeppesen, J, Beniczky, S & Atienza, D 2021, Self-Aware Anomaly-Detection for Epilepsy Monitoring on Low-Power Wearable Electrocardiographic Devices . in 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021 ., 9458555, IEEE, 3rd IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2021, Washington, United States, 06/06/2021 . https://doi.org/10.1109/AICAS51828.2021.9458555
AICAS

Low-power wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints concerning time and location, on one hand, and fulfilling long-term tracking, on the other hand. In the case of epileptic seizures

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbb3847c672a1f1efbeba03a7627d925
https://pure.au.dk/portal/da/publications/selfaware-anomalydetection-for-epilepsy-monitoring-on-lowpower-wearable-electrocardiographic-devices(5fd73b0d-0335-4d4e-8d14-96e66f93473b).html

Universal Adversarial Perturbations in Epileptic Seizure Detection

Autor: Amir Aminifar

Publikováno v: IJCNN

Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medici

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::00d3a398e874c7ab7a0848cfdb71200f
https://doi.org/10.1109/ijcnn48605.2020.9206696