Publications de Amir Aminifar | EPFL Graph Search

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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 ...

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 ...

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 ...

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortali ...

Increasing internet connectivity poses an existential threat for cyber-physical systems. Securing these safety-critical systems becomes an important challenge. Cyber-physical systems often comprise several control applications that are implemented on share ...

Today, epilepsy is one of the most common chronic diseases affecting more than 65 million people worldwide and is ranked number four after migraine, Alzheimer’s disease, and stroke. Despite the recent advances in anti-epileptic drugs, one-third of the epil ...

Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of ...

In the context of wearable medical systems, resources are scarce while performance requirements are high. Traditional sampling strategies create large amounts of data, which hinders the device's battery lifetime. However, energy savings are possible when r ...

Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals ...

In spite of the progress in management of Atrial Fibrillation (AF), this arrhythmia is one of the major causes of stroke and heart failure. The progression of this pathology from a silent paroxysmal form (PAF) into a sustained AF can be prevented by predic ...

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 ...

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 ...

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 ...

Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowin ...

Real-time communication over Ethernet is becoming important in various application areas of cyber-physical systems such as industrial automation and control, avionics, and automotive networking. Since such applications are typically time critical, Ethernet ...

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 ...

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 ...

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. ...

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. ...

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 ...

Amir Aminifar

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Personalized Real-Time Federated Learning for Epileptic Seizure Detection

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

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

A Self-Learning Methodology for Epileptic Seizure Detection with Minimally Supervised Edge Labeling

Butterfly Attack: Adversarial Manipulation of Temporal Properties of Cyber-Physical Systems

e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

Event-Triggered Sensing for High-Quality and Low-Power Cardiovascular Monitoring Systems

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

A Patient-Specific Methodology for Prediction of Paroxysmal Atrial Fibrillation Onset

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

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

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

Real-time classification technique for early detection and prevention of myocardial infarction on wearable devices

Stability-Aware Integrated Routing and Scheduling for Control Applications in Ethernet Networks

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

Noise-Resilient and Interpretable Epileptic Seizure Detection

Self-Aware Wearable Systems in Epileptic Seizure Detection

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

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