Francesca D. Faraci

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Senior Lecturer, Head of BioSignal Processing Specialization Unit

SUPSI

Switzerland

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About

Francesca D. Faraci is a Senior Lecturer and the Head of the BioSignal Processing Specialization Unit at SUPSI in Switzerland. Her research focuses on bio-signal processing, artificial intelligence in healthcare, and automated sleep scoring, as evidenced by her recent publications. Notable works include studies on ECG signal generation, algorithmic bias quantification, and the integration of AI with clinical practices.

Recent Grants

Grant: Close

CUOREMA Immersive Bio & Behavioural Feedback for a better heart health

Open Date: 2021-04-01

Close Date: 2024-03-01

Grant: Close

AP - DD Autoplay for Differential Diagnosis

Open Date: 2019-01-01

Close Date: 2020-06-01

Grant: Close

SPAS

Open Date: 2018-07-01

Close Date: 2022-03-01

Grant: Close

AutoPlay Phase 1

Open Date: 2017-01-01

Close Date: 2019-12-01

Grant: Close

AutoPlay - Phase 0

Open Date: 2015-01-01

Close Date: 2015-05-01

Articles (14)

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2025

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2021

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2021

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2021

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2021

Collaborators (6)

Florence Marcelle Aellen

University of Bern

SWITZERLAND

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Alessandro Puiatti

Head of Digital Health research area

University of Applied Sciences and Arts Northwestern Switzerland

SWITZERLAND

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Eun Kyoung Choe

University of Maryland

UNITED STATES

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Giulio Conte

Head of Service

SWITZERLAND

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Serena Caverzasio

SIB Swiss Institute of Bioinformatics

SWITZERLAND

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Athina Tzovara

Professor

University of Bern

SWITZERLAND

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Francesca D. Faraci

Research Interests

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About

Recent Grants

Articles (14)

Synthetic ECG signals generation: A scoping review

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review

Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study

Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare

Framework for Algorithmic Bias Quantification and its Application to Automated Sleep Scoring

U-Sleep’s resilience to AASM guidelines

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Deep Generative Models: The winning key for large and easily accessible ECG datasets?

Identification of Brugada syndrome based on P-wave features: an artificial intelligence-based approach

The SleepFit Tablet Application for Home-Based Clinical Data Collection in Parkinson Disease: User-Centric Development and Usability Study

Addressing bias in big data and AI for health care: A call for open science

DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model With Uncertainty Estimates

Wearables in the home-based assessment of abnormal movements in Parkinson’s disease: a systematic review of the literature

Collaborators (6)

Florence Marcelle Aellen

Alessandro Puiatti

Eun Kyoung Choe

Giulio Conte

Serena Caverzasio

Athina Tzovara

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