Publications by Amir Aminifar | EPFL Graph Search

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, Saleh Baghersalimi, Farnaz Forooghifar, Tomas Teijeiro Campo, Alireza Amirshahi

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

DP-ACT: Decentralized Privacy-Preserving Asymmetric Digital Contact Tracing

Amir Aminifar, Mathias Josef Payer

Digital contact tracing substantially improves the identification of high-risk contacts during pandemics. Despite several attempts to encourage people to use digital contact-tracing applications by developing and rolling out decentralized privacy-preservin ...

2024

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

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

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

Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable Systems

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

In healthcare, data privacy of patients regulations prohibits data from being moved outside the hospital, preventing international medical datasets from being centralized for AI training. Federated learning (FL) is a data privacy-focused method that trains ...

2023

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

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

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

A Self-Aware Epilepsy Monitoring System for Real-Time Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Leila Cammoun, Farnaz Forooghifar

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. ...

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 strat ...

2022

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

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

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

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

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

Personalized Real-Time Federated Learning for Epileptic Seizure Detection

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

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

Real-Time Personalized Atrial Fibrillation Prediction on Multi-Core Wearable Sensors

David Atienza Alonso, Amir Aminifar, Adriana Arza Valdes, Elisabetta De Giovanni, Miguel Peon Quiros

In the recent Internet-of-Things (IoT) era where biomedical applications require continuous monitoring of relevant data, edge computing keeps gaining more and more importance. These new architectures for edge computing include multi-core and parallel compu ...

2021

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

David Atienza Alonso, Amir Aminifar, Damián Pascual Ortiz, Alireza Amirshahi

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-thr ...

2021

Robust Epileptic Seizure Detection on Wearable Systems with Reduced False-Alarm Rate

David Atienza Alonso, Amir Aminifar, Renato Zanetti

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can ...

IEEE2020

Self-Aware Machine Learning for Multimodal Workload Monitoring During Manual Labor on Edge Wearable Sensors

David Atienza Alonso, Amir Aminifar, Adriana Arza Valdes, Farnaz Forooghifar, Giulio Masinelli

The design of reliable wearable technologies for real-time and long-term monitoring presents a major challenge. Self-awareness is a promising solution that enables the system to monitor itself in interaction with the environment and to manage its resources ...

2020

Security-aware Routing and Scheduling for Control Applications on Ethernet TSN Networks

Amir Aminifar

Today, it is common knowledge in the cyber-physical systems domain that the tight interaction between the cyber and physical elements provides the possibility of substantially improving the performance of these systems that is otherwise impossible. On the ...

ASSOC COMPUTING MACHINERY2020

Noise-Resilient and Interpretable Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Anthony Hitchcock Thomas

Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over ...

IEEE2020

Resource-Aware Distributed Epilepsy Monitoring Using Self-Awareness From Edge to Cloud

David Atienza Alonso, Amir Aminifar, Farnaz Forooghifar

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applica ...

2019

Multi-Modal Acute Stress Recognition Using Off-the-Shelf Wearable Devices

David Atienza Alonso, Amir Aminifar, Fabio Isidoro Tiberio Dell'Agnola, Adriana Arza Valdes, Victoriano Montesinos Canovas

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this pap ...

IEEE2019