Publications by Amir Aminifar | EPFL Graph Search

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Amir Aminifar

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

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

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

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

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

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

A wearable system for real-time detection of epileptic seizures

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

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

Anomalies in Scheduling Control Applications and Design Complexity