1,720,997 research outputs found
Parameter estimation based on scale-dependent algebraic expressions and scale-space fitting
No full textWe present our results of applying wavelet theory to the classic problem of estimating the unknown parameters of a model function subject to noise. The model function studied in this context is a generalization of the second-order Gaussian derivative of which the Gaussian function is a special case. For all five model parameters (amplitude, width, location, baseline, undershoot-size), scale-dependent algebraic expressions are derived. Based on this analytical framework, our first method estimates all parameters by substituting into a given expression numerically obtained values, such as the zero-crossings of the multiscale decompositions of the noisy input signal, using Gaussian derivative wavelets. Our second method takes these estimates as starting values for iterative least-squares optimization to fit our algebraic zero-crossing model to observed numeric zero-crossings in scale-space. For evaluation, we apply our method together with three reference methods to the three-parameter Gaussian model function. The results show that our method is on average 3.7 times more accurate than the respective best reference method for signal-to-noise ratios (SNR) from −10 to 70 dB, using a synthetic test scenario proposed by a competitor. For our full five-parameter model, we investigate overall estimation error as well as per-parameter error and per-parameter uncertainty as a function of SNR and various noise models, including correlated noise. To demonstrate practical effectiveness and relevance, we apply our method to the well-studied problem of QRS complex delineation in electrocardiography signals. Out-of-the-box results show a performance comparable to the best algorithms known to date, without relying on problem-specific heuristic decision rules
Fusion of Features with Neural Networks for Prediction of Secondary Neurological Outcome after Cardiac Arrest
No full textAs contribution to the 2023 George B. Moody challenge,
we – team “BrAInstorm” – aimed for fusing semantic
features based on medical knowledge with an endto-
end residual neural network to predict the secondary
neurological outcome after successful resuscitation. More
precisely, we fused numerical (e.g. age) and categorical
(e.g. gender) information as well as features extracted
from biosignals: We extracted absolute and relative power
bands, coupling, and coherence from standard electroencephalography
(EEG) frequency bands. To investigate the
interplay between heart and brain, we computed deceleration
capacity (DC) from electrocardiograms (ECGs). In
contrast to these semantic features, we adapted a residual
neural network based on agnostic features which are derived
from the training data. The network architecture was
originally developed for classification of ECGs and was
adjusted to the challenge EEG data. The best metric scores
were reached using only the neural network, demonstrating
the complexity of outcome prediction and effectiveness
of end-to-end methods. We received a challenge score of
0.57±0.15 during 5-fold cross validation on training data
and 0.448 on the hidden validation data. On the hidden
test data we received a final score of 0.68 (rank 8 of 36)
Heart rate monitoring in ultra-high-field MRI using frequency information obtained from video signals of the human skin compared to electrocardiography and pulse oximetry
No full textVideos of the human skin contain subtle color variations associated with the blood volume pulse. This remote photoplethysmography signal can be used for heart rate monitoring and represents an alternative to signals obtained from contact-based hardware. We developed an algorithm that estimates the heart rate in real-time from photoplethysmography signals and evaluate its performance in the context of ultra-high-field magnetic resonance imaging. We compare its accuracy to heart rate values estimated from electrocardiography and finger pulse oximetry triggers, obtained from MR vendor-provided hardware. For eight subjects, two experiments are conducted with the patient table outside and inside a 7 Tesla scanner. During both 5 min setups, heart rates from the algorithm and contact-based methods are stored. Their comparison suggests technical feasibility of the contactless method but that it is inferior in accuracy compared to contact-based hardware and that low heart rates (≤50 beats per minute) and adequate illumination are major challenges for practical feasibility
Completing the Cabrera Circle: Deriving adaptable leads from ECG limb leads by combining constraints with a correction factor
No full textAbstract Objective. We present a concept for processing 6-lead
electrocardiography (ECG) signals which can be applied to
various use cases in quantitative electrocardiography.
Approach. Our work builds upon the mathematics of the well-
known Cabrera sequence which is a re-sorting of the six limb
leads (I, II, III, aVR, aVL, aVF) into a clockwise and
physiologically-interpretable order. By deriving correction
factors for harmonizing lead strengths and choosing an
appropriate basis for the leads, we extend this concept towards
what we call the “Cabrera Circle” based on a mathematically
sound foundation.
Main results. To demonstrate the practical effectiveness and
relevance of this concept, we analyze its suitability for
deriving interpolated leads between the six limb leads and a
“radial” lead which both can be useful for specific use cases.
We focus on the use cases of i) determination of the electrical
heart axis by proposing a novel interactive tool for
reconstructing the heart’s vector loop and ii) improving
accuracy in time of automatic R-wave detection and T-wave
delineation in 6-lead ECG. For the first use case, we derive an
equation which allows projections of the 2-dimensional vector
loops to arbitrary angles of the Cabrera Circle. For the second
use case, we apply several state-of-the-art algorithms to a
freely- available 12-lead dataset (Lobachevsky University
Database). Out-of-the-box results show that the derived radial
lead outperforms the other limb leads (I, II, III, aVR, aVL,
aVF) by improving F1 scores of R-peak and T-peak detection by
0.61 and 2.12, respectively. Results of on- and offset
computations are also improved but on a smaller scale.
Significance. In summary, the Cabrera Circle offers a
methodology that might be useful for quantitative
electrocardiography of the 6-lead subsystem—especially in the
digital age
Hunting Bunnies: Comparison of XAI methods for detection of right bundle branch blocks in 12-lead electrocardiograms
No full textTDSpy: An open-source implementation of time delay stability analysis
No full texthttp://dx.doi.org/10.13039/501100001663 Volkswagen Foundationhttp://dx.doi.org/10.13039/501100010570 Niedersächsisches Ministerium für Wissenschaft und KulturOpen-Access-Publikationsfonds 202
Benchmarking the Impact of Noise on Deep Learning-Based Classification of Atrial Fibrillation in 12-Lead ECG
Full text linkElectrocardiography analysis is widely used in various clinical applications and Deep Learning models for classification tasks are currently in the focus of research. Due to their data-driven character, they bear the potential to handle signal noise efficiently, but its influence on the accuracy of these methods is still unclear. Therefore, we benchmark the influence of four types of noise on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead electrocardiograms. We use a subset of a publicly available dataset (PTB-XL) and use the metadata provided by human experts regarding noise for assigning a signal quality to each electrocardiogram. Furthermore, we compute a quantitative signal-to-noise ratio for each electrocardiogram. We analyze the accuracy of the Deep Learning model with respect to both metrics and observe that the method can robustly identify atrial fibrillation, even in cases signals are labelled by human experts as being noisy on multiple leads. False positive and false negative rates are slightly worse for data being labelled as noisy. Interestingly, data annotated as showing baseline drift noise results in an accuracy very similar to data without. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods that might not need preprocessing as many conventional methods do
Explainable Artificial Intelligence on Biosignals for Clinical Decision Support
No full textLower Saxony Vorab of the Volkswagen Foundation and the Ministry for Science and Culture of Lower SaxonyInstationsausschuss beim Gemeinsamen Bundesausschuss (G-BA
Assessment of Driver's Stress using Multimodal Biosignals and Regularized Deep Kernel Learning
No full textIn this work, we classify the stress state of car drivers using multimodal physiological signals and regularized deep kernel learning. Using a driving simulator in a controlled environment, we acquire electrocardiography (ECG), electrodermal activity (EDA), photoplethysmography (PPG), and respiration rate (RESP) from N = 10 healthy drivers in experiments of 25min duration with different stress states (5min resting, 10min driving, 10min driving + answering cognitive questions). We manually remove unusable segments and approximately 4h of data remain. Multimodal time and frequency features are extracted and employed to regularized deep kernel machine learning based on a fusion framework. Task-specific representations of different physiological signals are combined using intermediate fusion. Subsequently, the fused multimodal features are fed a support vector machine (SVM) and a random forest (RF) for stress classification. The experimental results show that the proposed approach can discriminate between stress states. The combination of PPG and ECG using RF as classifier yields the highest F1-score of 0.97 in the test set. PPG only and RF yield a maximum F1-score of 0.90. Furthermore, subject-specific cross-validation improves performance. ECG and PPG signals are reliable in classifying the stress state of a car driver. In summary, the proposed framework could be extended to real-time stress state assessment in driving conditions
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