78 research outputs found
Riemannian Prediction of Anatomical Diagnoses in Congenital Heart Disease based on 12-lead ECGs
Congenital heart disease (CHD) is a relatively rare disease that affects
patients at birth and results in extremely heterogeneous anatomical and
functional defects. 12-lead ECG signal is routinely collected in CHD patients
because it provides significant biomarkers for disease prognosis. However,
developing accurate machine learning models is challenging due to the lack of
large available datasets. Here, we suggest exploiting the Riemannian geometry
of the spatial covariance structure of the ECG signal to improve
classification. Firstly, we use covariance augmentation to mix samples across
the Riemannian geodesic between corresponding classes. Secondly, we suggest to
project the covariance matrices to their respective class Riemannian mean to
enhance the quality of feature extraction via tangent space projection. We
perform several ablation experiments and demonstrate significant improvement
compared to traditional machine learning models and deep learning on ECG time
series data.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Intention Detection of Gait Adaptation in Natural Settings
Gait adaptation is an important part of gait analysis and its neuronal origin
and dynamics has been studied extensively. In neurorehabilitation, it is
important as it perturbs neuronal dynamics and allows patients to restore some
of their motor function. Exoskeletons and robotics of the lower limbs are
increasingly used to facilitate rehabilitation as well as supporting daily
function. Their efficiency and safety depends on how well can sense the human
intention to move and adapt the gait accordingly. This paper presents a gait
adaptation scheme in natural settings. It allows monitoring of subjects in more
realistic environment without the requirement of specialized equipment such as
treadmill and foot pressure sensors. We extract gait characteristics based on a
single RBG camera whereas wireless EEG signals are monitored simultaneously. We
demonstrate that the method can not only successfully detect adaptation steps
but also detect efficiently whether the subject adjust their pace to higher or
lower speed
Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols
Traditional methods of training a Brain-Computer Interface (BCI) on motor imagery (MI) data generally involve multiple intensive sessions. The initial sessions produce simple prompts to users, while later sessions additionally provide realtime feedback to users, allowing for human adaptation to take place. However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved. To solve this problem, we propose an adaptive BCI training framework which will update the classifier in real time to provide more accurate feedback to the user on 4-class motor imagery data. This framework will require only one session to fully train a BCI to a given subject. Three variations of an adaptive Riemannian BCI were implemented and compared on data from both our own recorded datasets and the commonly used BCI Competition IV Dataset 2a. Results indicate that the fastest and least computationally expensive adaptive BCI was able to correctly classify motor imagery data at a rate 5.8% higher than when using a standard protocol with limited data. In addition it was confirmed that the adaptive BCI automatically improved its performance as more data became available
Comparison of Brain Networks based on Predictive Models of Connectivity
In this study we adopt predictive modelling to identify simultaneously
commonalities and differences in multi-modal brain networks acquired within
subjects. Typically, predictive modelling of functional connectomes from
structural connectomes explores commonalities across multimodal imaging data.
However, direct application of multivariate approaches such as sparse Canonical
Correlation Analysis (sCCA) applies on the vectorised elements of functional
connectivity across subjects and it does not guarantee that the predicted
models of functional connectivity are Symmetric Positive Matrices (SPD). We
suggest an elegant solution based on the transportation of the connectivity
matrices on a Riemannian manifold, which notably improves the prediction
performance of the model. Randomised lasso is used to alleviate the dependency
of the sCCA on the lasso parameters and control the false positive rate.
Subsequently, the binomial distribution is exploited to set a threshold
statistic that reflects whether a connection is selected or rejected by chance.
Finally, we estimate the sCCA loadings based on a de-noising approach that
improves the estimation of the coefficients. We validate our approach based on
resting-state fMRI and diffusion weighted MRI data. Quantitative validation of
the prediction performance shows superior performance, whereas qualitative
results of the identification process are promising.Comment: 7 pages, 4 figure
Towards Explainable, Privacy-Preserved Human-Motion Affect Recognition
Human motion characteristics are used to monitor the progression of neurological diseases and mood disorders. Since perceptions of emotions are also interleaved with body posture and movements, emotion recognition from human gait can be used to quantitatively monitor mood changes. Many existing solutions often use shallow machine learning models with raw positional data or manually extracted features to achieve this. However, gait is composed of many highly expressive characteristics that can be used to identify human subjects, and most solutions fail to address this, disregarding the subject's privacy. This work introduces a novel deep neural network architecture to disentangle human emotions and biometrics. In particular, we propose a cross-subject transfer learning technique for training a multi-encoder autoencoder deep neural network to learn disentangled latent representations of human motion features. By disentangling subject biometrics from the gait data, we show that the subject's privacy is preserved while the affect recognition performance outperforms traditional methods. Furthermore, we exploit Guided Grad-CAM to provide global explanations of the model's decision across gait cycles. We evaluate the effectiveness of our method to existing methods at recognizing emotions using both 3D temporal joint signals and manually extracted features. We also show that this data can easily be exploited to expose a subject's identity. Our method shows up to 7% improvement and highlights the joints with the most significant influence across the average gait cycle
Improving ECG Classification Interpretability Using Saliency Maps
Cardiovascular disease is a large worldwide healthcare issue; symptoms often present suddenly with minimal warning. The electrocardiogram (ECG) is a fast, simple and reliable method of evaluating the health of the heart, by measuring electrical activity recorded through electrodes placed on the skin. ECGs often need to be analyzed by a cardiologist, taking time which could be spent on improving patient care and outcomes.Because of this, automatic ECG classification systems using machine learning have been proposed, which can learn complex interactions between ECG features and use this to detect abnormalities. However, algorithms built for this purpose often fail to generalize well to unseen data, reporting initially impressive results which drop dramatically when applied to new environments. Additionally, machine learning algorithms suffer a ‘black-box’ issue, in which it is difficult to determine how a decision has been made. This is vital for applications in healthcare, as clinicians need to be able to verify the process of evaluation in order to trust the algorithm.This paper proposes a method for visualizing model decisions across each class in the MIT-BIH arrhythmia dataset, using adapted saliency maps averaged across complete classes to determine what patterns are being learned. We do this by building two algorithms based on state-of-the-art models. This paper highlights how these maps can be used to find problems in the model which could be affecting generalizability and model performance. Comparing saliency maps across complete classes gives an overall impression of confounding variables or other biases in the model, unlike what would be highlighted when comparing saliency maps on an ECG-by-ECG basis
Consensus of state of the art mortality prediction models: From all-cause mortality to sudden death prediction
Worldwide, many millions of people die suddenly and unexpectedly each year,
either with or without a prior history of cardiovascular disease. Such events
are sparse (once in a lifetime), many victims will not have had prior
investigations for cardiac disease and many different definitions of sudden
death exist. Accordingly, sudden death is hard to predict.
This analysis used NHS Electronic Health Records (EHRs) for people aged
50 years living in the Greater Glasgow and Clyde (GG\&C) region in 2010
(n = 380,000) to try to overcome these challenges. We investigated whether
medical history, blood tests, prescription of medicines, and hospitalisations
might, in combination, predict a heightened risk of sudden death.
We compared the performance of models trained to predict either sudden death
or all-cause mortality. We built six models for each outcome of interest: three
taken from state-of-the-art research (BEHRT, Deepr and Deep Patient), and three
of our own creation. We trained these using two different data representations:
a language-based representation, and a sparse temporal matrix.
We used global interpretability to understand the most important features of
each model, and compare how much agreement there was amongst models using Rank
Biased Overlap. It is challenging to account for correlated variables without
increasing the complexity of the interpretability technique. We overcame this
by clustering features into groups and comparing the most important groups for
each model. We found the agreement between models to be much higher when
accounting for correlated variables.
Our analysis emphasises the challenge of predicting sudden death and
emphasises the need for better understanding and interpretation of machine
learning models applied to healthcare applications
Controllable Chest X-Ray Report Generation from Longitudinal Representations
Radiology reports are detailed text descriptions of the content of medical
scans. Each report describes the presence/absence and location of relevant
clinical findings, commonly including comparison with prior exams of the same
patient to describe how they evolved. Radiology reporting is a time-consuming
process, and scan results are often subject to delays. One strategy to speed up
reporting is to integrate automated reporting systems, however clinical
deployment requires high accuracy and interpretability. Previous approaches to
automated radiology reporting generally do not provide the prior study as
input, precluding comparison which is required for clinical accuracy in some
types of scans, and offer only unreliable methods of interpretability.
Therefore, leveraging an existing visual input format of anatomical tokens, we
introduce two novel aspects: (1) longitudinal representation learning -- we
input the prior scan as an additional input, proposing a method to align,
concatenate and fuse the current and prior visual information into a joint
longitudinal representation which can be provided to the multimodal report
generation model; (2) sentence-anatomy dropout -- a training strategy for
controllability in which the report generator model is trained to predict only
sentences from the original report which correspond to the subset of anatomical
regions given as input. We show through in-depth experiments on the MIMIC-CXR
dataset how the proposed approach achieves state-of-the-art results while
enabling anatomy-wise controllable report generation.Comment: Accepted to the Findings of EMNLP 202
- …