arXiv research

Study develops algorithm to detect subtle respiratory events from PSG recordings.

System predicts respiratory failure up to 8 hours early.

Paper presents a deep learning framework for classifying respiratory anomalies and lung diseases from sound recordings.

Machine learning detects and diagnoses coughs for respiratory infections.

Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful stimuli. Thus, accurate sleep arousal detection can lead to an enhanced understanding of the underlying causes and influencing the assessment…

After birth, extremely preterm infants often require specialized respiratory management in the form of invasive mechanical ventilation (IMV). Protracted IMV is associated with detrimental outcomes and morbidities. Premature extubation, on the other hand, would necessitate reintubation which is risky, technically challe…

Deep CNN-RNN model classifies breathing sounds for respiratory disease diagnosis.

This research improves EEG-based MI-BCI systems to be more resilient to emotional arousal.

Project uses GANs to recognize facial expressions and emotions from-the-wild with dual model approach.

Respiratory infections and chronic respiratory diseases impose a heavy health burden worldwide. Coughing is one of the most common symptoms of many such infections, and can be indicative of flare-ups of chronic respiratory diseases. Whether at a clinical or public health level, the capacity to identify bouts of coughin…

In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database. Our model structure is composed of multiple dense convolutional units (…

Aff-Wild database expands facial expression recognition to real-world conditions.

This paper is an attempt to separate cardiac and respiratory signals from an electrical bio-impedance (EBI) dataset. For this two well-known algorithms, namely Principal Component Analysis (PCA) and Independent Component Analysis (ICA), were used to accomplish the task. The ability of the PCA and the ICA methods first …

Sleep disorders are implicated in a growing number of health problems. In this paper, we present a signal-processing/machine learning approach to detecting arousals in the multi-channel polysomnographic recordings of the Physionet/CinC Challenge2018 dataset. Methods: Our network architecture consists of two components.…

StarGAN model generates and recognizes emotions from facial expressions.

Deep transfer learning boosts single-EEG arousal detection accuracy.

Project creates new dataset for 'in the wild' emotions recognition.

The study recovers airflow from thoracic and abdominal movements using advanced signal processing.

Music FaderNets learns high-level musical qualities from low-level attributes.

DeCom predicts post-COVID RSV timing and intensity with NPI consideration.

Improved sleep apnea detection using sensor fusion and backward shortcut connections.

We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering based approaches and propose a new model based on deep learning. We compare the performance of both approaches on a database containing 18,000 …

We prove that a graph G is asymptotically isomorphic to the ray if and only if G is uniformly spherically bounded and is of bounded local degrees. This problem arouse in combinatorics and was posed in [3] (Problem 10.1).

GRU-D detects age-specific missing patterns in vital signs.

Lung segmentation from abnormal CXRs using data imputation.

Develops a risk score to assist ECMO planning for critically ill patients with viral or unspecified pneumonia.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clin…

New model recognizes emotions with missing modalities, improving accuracy.

In this research, an emotion recognition system is developed based on valence/arousal model using electroencephalography (EEG) signals. EEG signals are decomposed into the gamma, beta, alpha and theta frequency bands using discrete wavelet transform (DWT), and spectral features are extracted from each frequency band. P…

Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.

AI4COVID-19 app diagnoses COVID-19 from cough samples.

This short paper describes our solution to the 2018 IEEE World Congress on Computational Intelligence One-Minute Gradual-Emotional Behavior Challenge, whose goal was to estimate continuous arousal and valence values from short videos. We designed four base regression models using visual and audio features, and then use…

In this paper we propose a fusion approach to continuous emotion recognition that combines visual and auditory modalities in their representation spaces to predict the arousal and valence levels. The proposed approach employs a pre-trained convolution neural network and transfer learning to extract features from video …

This paper automates mining of COVID-19 scholarly articles using machine learning.

This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge. We provide the results for audio and video processing on subject (in)dependent evaluations. On the provided Development set, we achieved 0.343 Concordance Correlation Coefficien…

Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) …

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation …

This research improves emotion detection from speech, enhancing CCC by 30%.

First ABAW 2020 Competition analyzes affective behavior tasks.

Deep learning framework detects emotions from EEG data.

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via …

Model improves emotion recognition using multiple physiological signals.

1-D CNNs classify pupil size variations in scotopic conditions.

A deep neural network detects sleep events in polysomnograms with high accuracy.

We found a way to code meanders and show they are idempotent.

Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical mic…

Model estimates lung well-aerated volume from CT images, independent of patient and imaging parameters.

Nonconvex optimization problems arise in different research fields and arouse lots of attention in signal processing, statistics and machine learning. In this work, we explore the accelerated proximal gradient method and some of its variants which have been shown to converge under nonconvex context recently. We show th…