AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs

peer reviewedEarly diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects

Tensor-based Blind Source Separation for Structured EEG-fMRI Data Fusion

In this thesis, we devise advanced signal processing techniques which integrate the multimodal data stemming from simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which are two complementary medical imaging modalities to monitor brain (dys)function. We focus on their application in refractory epilepsy, wherein some brain cells undergo hypersynchronous activity, leading to seizures that cannot be suppressed by medication. In such cases, EEG-fMRI can aid a presurgical evaluation of the patient, to infer the location in the brain where epileptic discharges originate. We develop data fusion approaches based on representations of the data as tensors ('multidimensional arrays') to capture the rich, complex nature of EEG and fMRI, and to exploit their attractive properties for data mining. Our experiments show that these customized coupled tensor decompositions are not only able to extract components that model the temporal, spatial and spectral profiles of epileptic discharges, but also to estimate the variable functional relationship between EEG and fMRI, i.e., neurovascular coupling. Clinical validation shows that these novel techniques produce complementary sets of biomarkers, which assist the characterization and presurgical planning for epilepsy.status: publishe

EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses

OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech envelope based on the EEG, and finally uses this envelope to inform a multichannel speech separation and denoising algorithm. RESULTS: Strong suppression of interfering (unattended) speech and background noise is achieved, while the attended speech is preserved. Furthermore, EEG-based auditory attention detection (AAD) is shown to be robust to the use of noisy speech signals. CONCLUSIONS: Our results show that AAD-based speaker extraction from microphone array recordings is feasible and robust, even in noisy acoustic environments, and without access to the clean speech signals to perform EEG-based AAD. SIGNIFICANCE: Current research on AAD always assumes the availability of the clean speech signals, which limits the applicability in real settings. We have extended this research to detect the attended speaker even when only microphone recordings with noisy speech mixtures are available. This is an enabling ingredient for new brain-computer interfaces and effective filtering schemes in neuro-steered hearing prostheses. Here, we provide a first proof of concept for EEG-informed attended speaker extraction and denoising.This paper is published in IEEE Transactions on Biomedical Engineering (2016) and is under copyright. Please cite this paper as: S. Van Eyndhoven, T. Francart, and A. Bertrand, "EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses", IEEE Trans. Biomedical Engineering, vol. x(x), pp. x-x, 2016status: publishe

Flexible Fusion of Electroencephalography and Functional Magnetic Resonance Imaging: Revealing Neural-Hemodynamic Coupling Through Structured Matrix-Tensor Factorization

© EURASIP 2017. Simultaneous recording of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) has gained wide interest in brain research, thanks to the highly complementary spatiotemporal nature of both modalities. We propose a novel technique to extract sources of neural activity from the multimodal measurements, which relies on a structured form of coupled matrix-tensor factorization (CMTF). In a datasymmetric fashion, we characterize these underlying sources in the spatial, temporal and spectral domain, and estimate how the observations in EEG and fMRI are related through neurovascular coupling. That is, we explicitly account for the intrinsically variable nature of this coupling, allowing more accurate localization of the neural activity in time and space. We illustrate the effectiveness of this approach, which is shown to be robust to noise, by means of a simulation study. Hence, this provides a conceptually simple, yet effective alternative to other data-driven analysis methods in event-related or restingstate EEG-fMRI studies.status: publishe

Flexible hemodynamic coupling in EEG-fMRI: circumventing the GLM by structured coupled matrix-tensor factorization

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Identifying Stable Components of Matrix/Tensor Factorizations via Low-Rank Approximation of Inter-Factorization Similarity

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Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI

Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy influences. Subsequently, we use EEG-correlated fMRI to find the brain regions in which the BOLD signal fluctuation corresponds to the filtered signals' time-varying power (after convolving with the hemodynamic response function), and validate the identified regions by quantitatively comparing them to ground-truth maps of the (resected or hypothesized) ictal onset zone. We validate the performance of this novel predictor vs. that of commonly used unitary or power-weighted predictors and a recently introduced connectivity-based metric, on a cohort of 12 patients with refractory epilepsy. Results: The novel predictor, derived from the filtered EEG signals, allowed the detection of the ictal onset zone in a larger percentage of epileptic patients (92% vs. at most 83% for the other predictors), and with higher statistical significance, compared to existing predictors. At the same time, the new method maintains maximal specificity by not producing false positive activations in healthy controls. Significance: The findings of this study advocate for the use of the MWF to maximize the signal-to-noise ratio of IED-like events in the interictal EEG, and subsequently use time-varying power as a sensitive predictor of the BOLD signal, to localize the ictal onset zone.status: publishe

Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone with EEG-Correlated fMRI

To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis.</p