Advanced Methods And Tools for ECG Data Analysis

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Machine Learning and Decision Support in Critical Care (2016, background): “Disagreements may be exacerbated by significant noise contamination due to motion artifacts, electrode contact noise, and baseline drift [120].”
Contact-Based Methods for Measuring Respiratory Rate (2019, background): “Specifically, changes in the orientation of the hearth electrical axis related to the electrodes and modification of the thoracic impedance cause the baseline wander and amplitude modulation of the ECG signal [237].”
A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG (2015, methodology): “length of this window was selected based on the width of a normal QRS complex: 100 ms ± 20 ms [19].”
A Wearable Smartphone-Based Platform for Real-Time Cardiovascular Disease Detection Via Electrocardiogram Processing (2010, background): “which manifests as distortions with an amplitude larger than the P wave [13].”
Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review (2017, methodology): “The ECG can be measured using low-cost circuitry and electrodes (typically applied to the thorax) [20].”
Label Noise-Tolerant Hidden Markov Models for Segmentation: Application to ECGs (2011, methodology): “This section (i) quickly reviews ECG segmentation and the use of HMMs in this context and (ii) details the methodology used for the experiments in Section 6.”