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Amir Aminifar

Cette personne a quitté l’EPFL

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Publications associées (37)

Page 2 sur 2

Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems

David Atienza Alonso, Amir Aminifar, Dionisije Sopic

A considerable portion of government health-care spending is allocated to the continuous monitoring of patients suffering from cardiovascular diseases, particularly myocardial infarction (MI). Wearable devices present a cost-effective means of monitoring p ...

2018

Noise-Resilient and Interpretable Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar

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

Optimization of Message Encryption for Real-Time Applications in Embedded Systems

Amir Aminifar

Today, security can no longer be treated as a secondary issue in embedded and cyber-physical systems. Therefore, one of the main challenges in these domains is the design of secure embedded systems under stringent resource constraints and real-time require ...

2018

Self-Aware Wearable Systems in Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Farnaz Forooghifar

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. ...

2018

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

David Atienza Alonso, Leila Cammoun, Leila Faiza Ben Hamida Cammoun, Amir Aminifar, 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

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

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

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

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi

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

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, Saleh Baghersalimi, Farnaz Forooghifar

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

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

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

Control-Quality Driven Design of Embedded Control Systems with Stability Guarantees

Amir Aminifar

Today, the majority of control applications in embedded systems, e.g., in the automotive domain, are implemented as software tasks on shared platforms. Ignoring implementation impacts during the design of embedded control systems results in complex timing ...

2018

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

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

A wearable system for real-time detection of epileptic seizures

David Atienza Alonso, Amir Aminifar, Dionisije Sopic, Renato Zanetti

A wearable system for epileptic seizure detection, comprising an eyeglasses frame, with a left arm and a right arm configured to rest over the ears of an intended person wearing the eyeglasses, a first pair of electrodes located in the left arm, and a seco ...

2019

Tailoring SVM Inference for Resource-Efficient ECG-Based Epilepsy Monitors

David Atienza Alonso, Leila Cammoun, Giovanni Ansaloni, Amir Aminifar

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits ...

IEEE2019

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

Mathias Josef Payer, Amir Aminifar

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

Anomalies in Scheduling Control Applications and Design Complexity

Amir Aminifar

Today, many control applications in cyberphysical systems are implemented on shared platforms. Such resource sharing may lead to complex timing behaviors and, in turn, instability of control applications. This paper highlights a number of anomalies demonst ...

Ieee2017