Personne

Amir Aminifar

Publications associées (39)

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, José Angel Miranda Calero, Jonathan Dan

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

2024

M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Farnaz Forooghifar, Saleh Baghersalimi

Integrating low-power wearable systems into routine health monitoring is an ongoing challenge. Recent advances in the computation capabilities of wearables make it possible to target complex scenarios by exploiting multiple biosignals and using high-perfor ...

2024

EpilepsyNet: Interpretable Self-Supervised Seizure Detection for Low-Power Wearable Systems

David Atienza Alonso, Amir Aminifar, Renato Zanetti

Epilepsy is one of the most common neurological disorders that is characterized by recurrent and unpredictable seizures. Wearable systems can be used to detect the onset of a seizure and notify family members and emergency units for rescue. The majority of ...

2023

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Saleh Baghersalimi

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

2023

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

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, Anthony Hitchcock Thomas

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 setting in w ...

2022

Personalized seizure signature: An interpretable approach to false alarm reduction for long‐term epileptic seizure detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Dionisije Sopic

Objective Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific ap ...

2022

Real-Time EEG-Based Cognitive Workload Monitoring on Wearable Devices

David Atienza Alonso, Amir Aminifar, Adriana Arza Valdes, Renato Zanetti

Objective: Cognitive workload monitoring (CWM)can enhance human-machine interaction by supporting task execution assistance considering the operator’s cognitive state. Therefore, we propose a machine learning design methodology and a data processing strate ...

2021

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

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Farnaz Forooghifar

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, as t ...

2021

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Saleh Baghersalimi

Epilepsy is one of the most prevalent paroxystic neurological disorders. It is characterized by the occurrence of spontaneous seizures. About 1 out of 3 patients have drug-resistant epilepsy, thus their seizures cannot be controlled by medication. Automati ...

2021

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Amir Aminifar

Editor's notes: The cross-layer approach presented in this article involves co-designing a feedback controller's parameters together with the schedule of an Ethernet network used for communicating state information and control signals.-Samarjit Chakraborty ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

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