Search Results for author: Walter Karlen

Found 12 papers, 5 papers with code

Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep

no code implementations4 Mar 2022 Maria Laura Ferster, Giulia Da Poian, Kiran Menachery, Simon J. Schreiner, Caroline Lustenberger, Angelina Maric, Reto Huber, Christian Baumann, Walter Karlen

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 enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD).

Benchmarking EEG +1

Multispectral Video Fusion for Non-contact Monitoring of Respiratory Rate and Apnea

no code implementations21 Apr 2020 Gaetano Scebba, Giulia Da Poian, Walter Karlen

The algorithm independently addresses the RR estimation and apnea detection tasks.

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data

no code implementations2 Jan 2020 Patrick Schwab, Walter Karlen

Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms.

CXPlain: Causal Explanations for Model Interpretation under Uncertainty

2 code implementations NeurIPS 2019 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 interpreting machine-learning models.

BIG-bench Machine Learning Feature Importance

Forecasting intracranial hypertension using multi-scale waveform metrics

no code implementations25 Feb 2019 Matthias Hüser, Adrian Kündig, Walter Karlen, Valeria De Luca, Martin Jaggi

Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours.

Time Series Analysis

Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

1 code implementation3 Feb 2019 Patrick Schwab, Lorenz Linhardt, Stefan Bauer, Joachim M. Buhmann, Walter Karlen

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 healthcare, economics and public policy.

counterfactual Model Selection

Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks

1 code implementation ICLR 2019 Patrick Schwab, Lorenz Linhardt, Walter Karlen

However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.

counterfactual Counterfactual Inference

PhoneMD: Learning to Diagnose Parkinson's Disease from Smartphone Data

no code implementations1 Oct 2018 Patrick Schwab, Walter Karlen

One factor that contributes to misdiagnoses is that the symptoms of Parkinson's disease may not be prominent at the time the clinical assessment is performed.

Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks

1 code implementation6 Feb 2018 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 data.

Feature Importance

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

no code implementations17 Oct 2017 Patrick Schwab, Gaetano Scebba, Jia Zhang, Marco Delai, Walter Karlen

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 the continuous monitoring of patients at risk.

General Classification Test

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