241 research outputs found
Assessment of electrocardiograms with pretraining and shallow networks
Objective: Clinical Decision Support Systems normally resort to annotated signals for the automatic assessment of ECG signals. In this paper we put forward a new method for the assessment of normal/abnormal heart function from raw ECG signals (i.e. signals without annotation) based on shallow neural networks with pretraining.
Methodology: this paper resorts to a prospective clinical study that took place at Hospital Cll´inic in Barcelona, Spain. This study took place in 2010-2012 and recruited 1390 patients. For each patient we recorded a 12-lead ECG and diagnosis was conducted by the Cardiology service at the same hospital. Two datasets were produced, the first contained the automatically annotated version of all input signals and the second contained the raw signals obtained from the ECG.
Results: The new method was tested through crossvalidation with a cohort of 200 test patients. Performance was compared for both annotated and raw datasets. For the annotated dataset and a shallow network with pretraining we obtained an accuracy of 0.8639, a sensitivity of 0.9560 and specificity of 0.7143. The raw dataset yielded an accuracy of 0.8426, a sensitivity of 0.8977 and a specificity of 0.7785.
Conclusion: Shallow networks with pretraining automatically obtain a representation of the input data without resorting to any annotation and thus simplify the process of assessing normality of ECG signals. Despite the fact that sensitivity has decreased, accuracy is not much lower than that obtained with standard methods. Specificity is improved with the new method. These results open up a promising line of research for the automatic assessment of ECG signals.Peer ReviewedPostprint (published version
Towards broader application of deep learning methods to the automated analysis of electrocardiograms
Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data
Infant sleep is critical to brain and behavioral development. Prior studies
on infant sleep/wake classification have been largely limited to reliance on
expensive and burdensome polysomnography (PSG) tests in the laboratory or
wearable devices that collect single-modality data. To facilitate data
collection and accuracy of detection, we aimed to advance this field of study
by using a multi-modal wearable device, LittleBeats (LB), to collect audio,
electrocardiogram (ECG), and inertial measurement unit (IMU) data among a
cohort of 28 infants. We employed a 3-branch (audio/ECG/IMU) large scale
transformer-based neural network (NN) to demonstrate the potential of such
multi-modal data. We pretrained each branch independently with its respective
modality, then finetuned the model by fusing the pretrained transformer layers
with cross-attention. We show that multi-modal data significantly improves
sleep/wake classification (accuracy = 0.880), compared with use of a single
modality (accuracy = 0.732). Our approach to multi-modal mid-level fusion may
be adaptable to a diverse range of architectures and tasks, expanding future
directions of infant behavioral research.Comment: Preprint for APSIPA202
Deep Models for Engagement Assessment With Scarce Label Information
Task engagement is defined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overfitting. In this paper, we proposed two deep models (i.e., a deep classifier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were fine-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training
A Comprehensive Study on Pain Assessment from Multimodal Sensor Data
Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed
Joint optimization of a -VAE for ECG task-specific feature extraction
Electrocardiography is the most common method to investigate the condition of
the heart through the observation of cardiac rhythm and electrical activity,
for both diagnosis and monitoring purposes. Analysis of electrocardiograms
(ECGs) is commonly performed through the investigation of specific patterns,
which are visually recognizable by trained physicians and are known to reflect
cardiac (dis)function. In this work we study the use of -variational
autoencoders (VAEs) as an explainable feature extractor, and improve on its
predictive capacities by jointly optimizing signal reconstruction and cardiac
function prediction. The extracted features are then used for cardiac function
prediction using logistic regression. The method is trained and tested on data
from 7255 patients, who were treated for acute coronary syndrome at the Leiden
University Medical Center between 2010 and 2021. The results show that our
method significantly improved prediction and explainability compared to a
vanilla -VAE, while still yielding similar reconstruction performance.Comment: Conference paper, 10 pages, 3 figures, 1 tabl
A new method using deep transfer learning on ECG to predict the response to cardiac resynchronization therapy
Background: Cardiac resynchronization therapy (CRT) has emerged as an
effective treatment for heart failure patients with electrical dyssynchrony.
However, accurately predicting which patients will respond to CRT remains a
challenge. This study explores the application of deep transfer learning
techniques to train a predictive model for CRT response. Methods: In this
study, the short-time Fourier transform (STFT) technique was employed to
transform ECG signals into two-dimensional images. A transfer learning approach
was then applied on the MIT-BIT ECG database to pre-train a convolutional
neural network (CNN) model. The model was fine-tuned to extract relevant
features from the ECG images, and then tested on our dataset of CRT patients to
predict their response. Results: Seventy-one CRT patients were enrolled in this
study. The transfer learning model achieved an accuracy of 72% in
distinguishing responders from non-responders in the local dataset.
Furthermore, the model showed good sensitivity (0.78) and specificity (0.79) in
identifying CRT responders. The performance of our model outperformed clinic
guidelines and traditional machine learning approaches. Conclusion: The
utilization of ECG images as input and leveraging the power of transfer
learning allows for improved accuracy in identifying CRT responders. This
approach offers potential for enhancing patient selection and improving
outcomes of CRT
Self-supervised learning-based general laboratory progress pretrained model for cardiovascular event detection
The inherent nature of patient data poses several challenges. Prevalent cases
amass substantial longitudinal data owing to their patient volume and
consistent follow-ups, however, longitudinal laboratory data are renowned for
their irregularity, temporality, absenteeism, and sparsity; In contrast,
recruitment for rare or specific cases is often constrained due to their
limited patient size and episodic observations. This study employed
self-supervised learning (SSL) to pretrain a generalized laboratory progress
(GLP) model that captures the overall progression of six common laboratory
markers in prevalent cardiovascular cases, with the intention of transferring
this knowledge to aid in the detection of specific cardiovascular event. GLP
implemented a two-stage training approach, leveraging the information embedded
within interpolated data and amplify the performance of SSL. After GLP
pretraining, it is transferred for TVR detection. The proposed two-stage
training improved the performance of pure SSL, and the transferability of GLP
exhibited distinctiveness. After GLP processing, the classification exhibited a
notable enhancement, with averaged accuracy rising from 0.63 to 0.90. All
evaluated metrics demonstrated substantial superiority (p < 0.01) compared to
prior GLP processing. Our study effectively engages in translational
engineering by transferring patient progression of cardiovascular laboratory
parameters from one patient group to another, transcending the limitations of
data availability. The transferability of disease progression optimized the
strategies of examinations and treatments, and improves patient prognosis while
using commonly available laboratory parameters. The potential for expanding
this approach to encompass other diseases holds great promise.Comment: published in IEEE Journal of Translational Engineering in Health &
Medicin
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