101 citations · 578 across the 13 of their papers we have counts for
14 papers
Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep
Maria Laura Ferster, Giulia Da Poian, Kiran Menachery +6
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhanc…
Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation
Gaetano Scebba, Jia Zhang, Sabrina Catanzaro +4
Chronic wounds significantly impact quality of life. If not properly managed, they can severely deteriorate. Image-based wound analysis could aid in objectively assessing the wound…
Remote health monitoring and diagnosis in the time of COVID-19
Joachim A. Behar, Chengyu Liu, Kevin Kotzen +13
Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SA…
Covariance Intersection to Improve the Robustness of the Photoplethysmogram Derived Respiratory Rate
Jia Zhang, Gaetano Scebba, Walter Karlen
Respiratory rate (RR) can be estimated from the photoplethysmogram (PPG) recorded by optical sensors in wearable devices. The fusion of estimates from different PPG features has le…
Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea
Gaetano Scebba, Giulia Da Poian, Walter Karlen
Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with ne…
A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data
Patrick Schwab, Walter Karlen
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's mo…
CXPlain: Causal Explanations for Model Interpretation under Uncertainty
Patrick Schwab, Walter Karlen
Feature importance estimates that inform users about the degree to which given inputs influence the output of a predictive model are crucial for understanding, validating, and inte…
Forecasting intracranial hypertension using multi-scale waveform metrics
Matthias Hüser, Adrian Kündig, Walter Karlen +2
Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively,…
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Patrick Schwab, Lorenz Linhardt, Stefan Bauer +2
Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as health…
PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data
Patrick Schwab, Walter Karlen
Parkinson's disease is a neurodegenerative disease that can affect a person's movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson's disease by perfo…
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks
Patrick Schwab, Lorenz Linhardt, Walter Karlen
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Coun…
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Patrick Schwab, Emanuela Keller, Carl Muroi +3
Patients in the intensive care unit (ICU) require constant and close supervision. To assist clinical staff in this task, hospitals use monitoring systems that trigger audiovisual a…
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab, Djordje Miladinovic, Walter Karlen
Knowledge of the importance of input features towards decisions made by machine-learning models is essential to increase our understanding of both the models and the underlying dat…
Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks
Patrick Schwab, Gaetano Scebba, Jia Zhang +2
With tens of thousands of electrocardiogram (ECG) records processed by mobile cardiac event recorders every day, heart rhythm classification algorithms are an important tool for th…