Farnaz Forooghifar

Biographie

She began her professional education as a Bachelor of Science student at September 2010 in electrical engineering, at University of Tehran where she promoted her knowledge of electrical engineering and digital circuit design skills. Then, she decided to put her focus on research and narrow down to a specific issue in electrical engineering. Hence, she continued her education as a Master of Science student under supervision of Prof. Zain Navabi at University of Tehran from September 2014 to June 2017. Due to her interest in digital systems design and optimization and also low power systems, she concentrated on approximate computing methods in her M.Sc. thesis. She started her PhD in electrical engineering at EPFL from August 2017 under supervision of Prof. David Atienza at ESL laboratory and she is now working on "Design of Event-Driven Multi-Parametric Bio-Signal Monitoring Systems" as her PhD thesis.

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https://people.epfl.ch/farnaz.forooghifar?lang=fr

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Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices

Farnaz Forooghifar

Remote health monitoring has attracted a lot of attention over the past decades to provide the opportunity for early detection of pathological health conditions. This early detection improves the quality of life for the patients and significantly reduces the burden on their family members. Moreover, this improvement reduces the socioeconomic consequences that are caused due to the disability of patients to work despite their health conditions. Wearable technologies offer a promising solution in pervasive health monitoring by relaxing the constraints concerning time and location. There are two major challenges in developing these technologies. The first challenge is real-time monitoring of the patients to fulfill early detection of life-threatening conditions using advanced machine learning techniques. As a solution for this issue, in this thesis, I considered a paradigm shift toward self-aware approaches and frameworks, which resulted in less energy consumption and longer battery lifetime, hence enabling ambulatory patient monitoring. I provided the possibility of switching between energy-efficient and high-performance modes in these systems. The second challenge is improving performance while guaranteeing the accuracy of the system. In this work, I have addressed this second challenge by combining multi-parametric bio-signals analysis and personalizing the detection for each patient. Throughout this thesis, I focused on epileptic seizure detection systems as a principal case study to analyze and evaluate the proposed techniques.

EPFL2022

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

Amir Aminifar, David Atienza Alonso, 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 the attacks infrequently occur, using an anomaly detection approach reduces the need to record long hours of data for each patient before detecting the successive coming seizures. In this work, by combining the concepts of self-aware system and anomaly detection, we propose an energy-efficient system to detect epileptic seizures on single-lead electrocardiographic signals, which is personalized after analyzing the first seizure of the patient. This system, then, uses a simple anomaly-detection model, whenever the model is deemed reliable, and uses a more complex model otherwise. We show that after the personalization, the number of patients, for which the method provides high sensitivity, can reach 26 out of 43 patients with the false alarm rate (FAR) of 4 alarms/day. Thus, the number of responders to the system is increased by 24%, while the FAR is only increased by one alarm/day, compared to the system that just uses the simple model. This benefit occurs while the system complexity decreases by 27.7% compared to the complex model. After adding the two-level (simple and complex) anomaly-detection, the complexity is tuned between 72.3% and 37.6% of the complex model. Similarly, the sensitivity is tuned between 66.5% and 60.3%.

2021

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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 more efficiently. In this work, we aim to utilize the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. Specifically, we consider cognitive workload detection during manual labor as a case study to illustrate the impact of our proposed technique in wearable technologies. Our multimodal machine-learning algorithm is able to detect cognitive workload during manual labor with a performance of 81.75%. By adopting the notion of self-awareness, we achieve an improvement of 27.6% in energy consumption, with less than 6% of performance loss.

2020

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