46 research outputs found
Fusing Continuous-valued Medical Labels using a Bayesian Model
With the rapid increase in volume of time series medical data available
through wearable devices, there is a need to employ automated algorithms to
label data. Examples of labels include interventions, changes in activity (e.g.
sleep) and changes in physiology (e.g. arrhythmias). However, automated
algorithms tend to be unreliable resulting in lower quality care. Expert
annotations are scarce, expensive, and prone to significant inter- and
intra-observer variance. To address these problems, a Bayesian
Continuous-valued Label Aggregator(BCLA) is proposed to provide a reliable
estimation of label aggregation while accurately infer the precision and bias
of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic
indicator) estimation from the electrocardiogram using labels from the 2006
PhysioNet/Computing in Cardiology Challenge database. It was compared to the
mean, median, and a previously proposed Expectation Maximization (EM) label
aggregation approaches. While accurately predicting each labelling algorithm's
bias and precision, the root-mean-square error of the BCLA was
11.780.63ms, significantly outperforming the best Challenge entry
(15.372.13ms) as well as the EM, mean, and median voting strategies
(14.760.52ms, 17.610.55ms, and 14.430.57ms respectively with
)
pyPPG: A Python toolbox for comprehensive photoplethysmography signal analysis
Photoplethysmography is a non-invasive optical technique that measures
changes in blood volume within tissues. It is commonly and increasingly used
for in a variety of research and clinical application to assess vascular
dynamics and physiological parameters. Yet, contrary to heart rate variability
measures, a field which has seen the development of stable standards and
advanced toolboxes and software, no such standards and open tools exist for
continuous photoplethysmogram (PPG) analysis. Consequently, the primary
objective of this research was to identify, standardize, implement and validate
key digital PPG biomarkers. This work describes the creation of a standard
Python toolbox, denoted pyPPG, for long-term continuous PPG time series
analysis recorded using a standard finger-based transmission pulse oximeter.
The improved PPG peak detector had an F1-score of 88.19% for the
state-of-the-art benchmark when evaluated on 2,054 adult polysomnography
recordings totaling over 91 million reference beats. This algorithm
outperformed the open-source original Matlab implementation by ~5% when
benchmarked on a subset of 100 randomly selected MESA recordings. More than
3,000 fiducial points were manually annotated by two annotators in order to
validate the fiducial points detector. The detector consistently demonstrated
high performance, with a mean absolute error of less than 10 ms for all
fiducial points. Based on these fiducial points, pyPPG engineers a set of 74
PPG biomarkers. Studying the PPG time series variability using pyPPG can
enhance our understanding of the manifestations and etiology of diseases. This
toolbox can also be used for biomarker engineering in training data-driven
models. pyPPG is available on physiozoo.orgComment: The manuscript was submitted to "Physiological Measurement" on
September 5, 202
Robust peak detection for photoplethysmography signal analysis
Efficient and accurate evaluation of long-term photoplethysmography (PPG)
recordings is essential for both clinical assessments and consumer products. In
2021, the top opensource peak detectors were benchmarked on the Multi-Ethnic
Study of Atherosclerosis (MESA) database consisting of polysomnography (PSG)
recordings and continuous sleep PPG data, where the Automatic Beat Detector
(Aboy) had the best accuracy. This work presents Aboy++, an improved version of
the original Aboy beat detector. The algorithm was evaluated on 100 adult PPG
recordings from the MESA database, which contains more than 4.25 million
reference beats. Aboy++ achieved an F1-score of 85.5%, compared to 80.99% for
the original Aboy peak detector. On average, Aboy++ processed a 1 hour-long
recording in less than 2 seconds. This is compared to 115 seconds (i.e., over
57-times longer) for the open-source implementation of the original Aboy peak
detector. This study demonstrated the importance of developing robust
algorithms like Aboy++ to improve PPG data analysis and clinical outcomes.
Overall, Aboy++ is a reliable tool for evaluating long-term wearable PPG
measurements in clinical and consumer contexts.Comment: 4 pages, 1 figure, 50th Computing in Cardiology conference in
Atlanta, Georgia, USA on 1st - 4th October 202
Case Study: Fetal Breathing Movements as a Proxy for Fetal Lung Maturity Estimation
Premature births can lead to complications, with fetal lung immaturity being
a primary concern. Currently, fetal lung maturity (FLM) requires an invasive
surfactant extraction procedure between the 32nd and 39th weeks of pregnancy.
Unfortunately, there is no non-invasive method for FLM assessment. This work
hypothesized that fetal breathing movement (FBM) and surfactant levels are
inversely coupled and that FBM can serve as a proxy for FLM estimation. To
investigate the correlation between FBM and FLM, antenatal corticosteroid (ACS)
was administered to increase fetal pulmonary surfactant levels in a high-risk
35th-week pregnant woman showing intrauterine growth restriction. Synchronous
sonographic and phonographic measurements were continuously recorded for 25
minutes before and after the ASC treatments. Before the ACS injection, 268
continuous movements FBM episodes were recorded. The number of continuous FBM
episodes significantly decreased to 3, 43, and 79 within 24, 48, and 72 hours,
respectively, of the first injection of ACS, suggesting an inversely coupled
connection between FBM and surfactant level s. Therefore, FBM may serve as a
proxy for FLM estimation. Quantitative confirmation of these findings would
suggest that FBM measurements could be used as a non-invasive and widely
accessible FLM-assessment tool for high-risk pregnancies and routine
examinations.Comment: 4 pages, 3 figures, 50th Computing in Cardiology conference in
Atlanta, Georgia, USA on 1st - 4th October 202
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG
Objective: Machine learning techniques have been used extensively for 12-lead
electrocardiogram (ECG) analysis. For physiological time series, deep learning
(DL) superiority to feature engineering (FE) approaches based on domain
knowledge is still an open question. Moreover, it remains unclear whether
combining DL with FE may improve performance. Methods: We considered three
tasks intending to address these research gaps: cardiac arrhythmia diagnosis
(multiclass-multilabel classification), atrial fibrillation risk prediction
(binary classification), and age estimation (regression). We used an overall
dataset of 2.3M 12-lead ECG recordings to train the following models for each
task: i) a random forest taking the FE as input was trained as a classical
machine learning approach; ii) an end-to-end DL model; and iii) a merged model
of FE+DL. Results: FE yielded comparable results to DL while necessitating
significantly less data for the two classification tasks and it was
outperformed by DL for the regression task. For all tasks, merging FE with DL
did not improve performance over DL alone. Conclusion: We found that for
traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful
improvement over FE, while it improved significantly the nontraditional
regression task. We also found that combining FE with DL did not improve over
DL alone which suggests that the FE were redundant with the features learned by
DL. Significance: Our findings provides important recommendations on what
machine learning strategy and data regime to chose with respect to the task at
hand for the development of new machine learning models based on the 12-lead
ECG
PhysioZoo: The Open Digital Physiological Biomarkers Resource
PhysioZoo is a collaborative platform designed for the analysis of continuous
physiological time series. The platform currently comprises four modules, each
consisting of a library, a user interface, and a set of tutorials: (1)
PhysioZoo HRV, dedicated to studying heart rate variability (HRV) in humans and
other mammals; (2) PhysioZoo SPO2, which focuses on the analysis of digital
oximetry biomarkers (OBM) using continuous oximetry (SpO2) measurements from
humans; (3) PhysioZoo ECG, dedicated to the analysis of electrocardiogram (ECG)
time series; (4) PhysioZoo PPG, designed to study photoplethysmography (PPG)
time series. In this proceeding, we introduce the PhysioZoo platform as an open
resource for digital physiological biomarkers engineering, facilitating
streamlined analysis and data visualization of physiological time series while
ensuring the reproducibility of published experiments. We welcome researchers
to contribute new libraries for the analysis of various physiological time
series, such as electroencephalography, blood pressure, and phonocardiography.
You can access the resource at physiozoo.com. We encourage researchers to
explore and utilize this platform to advance their studies in the field of
continuous physiological time-series analysis.Comment: 4 pages, 2 figure, 50th Computing in Cardiology conference in
Atlanta, Georgia, USA on 1st - 4th October 202
PhysioZoo: A Novel Open Access Platform for Heart Rate Variability Analysis of Mammalian Electrocardiographic Data
Background: The time variation between consecutive heartbeats is commonly referred to as heart rate variability (HRV). Loss of complexity in HRV has been documented in several cardiovascular diseases and has been associated with an increase in morbidity and mortality. However, the mechanisms that control HRV are not well understood. Animal experiments are the key to investigating this question. However, to date, there are no standard open source tools for HRV analysis of mammalian electrocardiogram (ECG) data and no centralized public databases for researchers to access.Methods: We created an open source software solution specifically designed for HRV analysis from ECG data of multiple mammals, including humans. We also created a set of public databases of mammalian ECG signals (dog, rabbit and mouse) with manually corrected R-peaks (>170,000 annotations) and signal quality annotations. The platform (software and databases) is called PhysioZoo.Results: PhysioZoo makes it possible to load ECG data and perform very accurate R-peak detection (F1 > 98%). It also allows the user to manually correct the R-peak locations and annotate low signal quality of the underlying ECG. PhysioZoo implements state of the art HRV measures adapted for different mammals (dogs, rabbits, and mice) and allows easy export of all computed measures together with standard data representation figures. PhysioZoo provides databases and standard ranges for all HRV measures computed on healthy, conscious humans, dogs, rabbits, and mice at rest. Study of these measures across different mammals can provide new insights into the complexity of heart rate dynamics across species.Conclusion: PhysioZoo enables the standardization and reproducibility of HRV analysis in mammalian models through its open source code, freely available software, and open access databases. PhysioZoo will support and enable new investigations in mammalian HRV research. The source code and software are available on www.physiozoo.com