ArticleIEEE access : practical innovations, open solutions2019
Noise Detection in Electrocardiogram Signals for Intensive Care Unit Patients.
Article in IEEE access : practical innovations, open solutions, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- MIMIC-III-Ext-PPG, a PPG-based Benchmark Dataset for Cardiovascular and Respiratory Signal Analysis.Scientific data · 2026Article
- End-to-end non-invasive ECG signal generation from PPG signal: a self-supervised learning approach.Frontiers in physiology · 2026Article
- AI modeling photoplethysmography to electrocardiography useful for predicting cardiovascular disease.NPJ digital medicine · 2025Article
- The MSPTDfast photoplethysmography beat detection algorithm: design, benchmarking, and open-source distribution.Physiological measurement · 2025Article
- LieRHRV system for remote lie detection using heart rate variability parameters.Scientific reports · 2024Article
- Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.Sensors (Basel, Switzerland) · 2024Article
- Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias.Cardiovascular digital health journal · 2024Article
- Wearable Sensors as a Preoperative Assessment Tool: A Review.Sensors (Basel, Switzerland) · 2024Review
- Article
- Quantifying deep neural network uncertainty for atrial fibrillation detection with limited labels.Scientific reports · 2022Article
- A two-step pre-processing tool to remove Gaussian and ectopic noise for heart rate variability analysis.Scientific reports · 2022Article
- Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.IEEE transactions on bio-medical engineering · 2022Article
- Detecting beats in the photoplethysmogram: benchmarking open-source algorithms.Physiological measurement · 2022Article
- Enabling Timely Medical Intervention by Exploring Health-Related Multivariate Time Series with a Hybrid Attentive Model.Sensors (Basel, Switzerland) · 2022Article
- An Innovative Machine Learning Approach for Classifying ECG Signals in Healthcare Devices.Journal of healthcare engineering · 2022Article
- Current status and trends in researches based on public intensive care databases: A scientometric investigation.Frontiers in public health · 2022Article
- Signal Quality Assessment of a Novel ECG Electrode for Motion Artifact Reduction.Sensors (Basel, Switzerland) · 2021Article
- Feasibility of atrial fibrillation detection from a novel wearable armband device.Cardiovascular digital health journal · 2021Article
- Article
- Novel Density Poincaré Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions.IEEE transactions on bio-medical engineering · 2021Article
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5 authors.
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Abstract
Long term electrocardiogram (ECG) signals recorded in an intensive care unit (ICU) are often corrupted by severe motion and noise artifacts (MNA), which may lead to many false alarms including inaccurate detection of atrial fibrillation (AF). We developed an automated method to detect MNA from ECG recordings in the Medical Information Mart for Intensive Care (MIMIC) III database. Since AF detection is often based on identification of irregular RR intervals derived from the QRS complexes, the main design focus of our MNA detection algorithm was to identify corrupted QRS complexes of the ECG signals. The MNA in the MIMIC III database contain not only motion-induced noise, but also a plethora of non-ECG waveforms, which must also be automatically identified. Our algorithm is designed to first discriminate between ECG and non-ECG waveforms using both time and spectral-domain properties. For the segments of data containing ECG waveforms, a time-frequency spectrum and its sub-band decomposition approach were used to identify MNA, and high frequency noise ECG segments, respectively. The algorithm was tested on data from 35 subjects in normal sinus rhythm and 25 AF subjects. The proposed method is shown to accurately discriminate between segments that contained real ECG waveforms and those that did not, even though the latter were numerous in some subjects. In addition, we found a significant reduction (> 94%) in false positive detection of AF in normal subjects when our MNA detection algorithm was used. Without using it, we inaccurately detected AF owing to the MNA.
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