2 research outputs found
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