ECG Signal Quality During Arrhythmia and Its Application to False Alarm Reduction

By J. Behar, J. Oster, Qiao Li, G. Clifford. IEEE Transactions on Biomedical Engineering, 2013

303 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Medical Laboratory Technology, Surgery

View Paper: https://www.semanticscholar.org/paper/8ea2e49d1ec2c8573d6575be63bc97daf13a179c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. M. Sabarimalai Manikandan
2. Shamim Nemati
3. G. Clifford
4. Marzyeh Ghassemi
5. Barathram Ramkumar
6. David A. Clifton
7. Udit Satija

In Surgery

1. Shamim Nemati
2. G. Clifford
3. Marzyeh Ghassemi
4. M. Sabarimalai Manikandan
5. Barathram Ramkumar
6. Alistair E. W. Johnson
7. Udit Satija

Citations in the corpus

• Logistic Regression-HSMM-Based Heart Sound Segmentation (2016, unknown): “This is equivalent to methods employed by other authors, with a smaller window size [34], [35].”
• Machine Learning and Decision Support in Critical Care (2016, background): “[53] highlighted the impact of rhythm type on signal quality, noting that SQIs must be tailored to a variety of arrhythmias and calling for more labeled training data to facilitate this task.”
• Real-Time Signal Quality-Aware ECG Telemetry System for IoT-Based Health Care Monitoring (2017, methodology): “The SQA method includes two major steps: 1) feature extraction and signal quality ratings using heuristic rules [19] and 2) machine learning approaches [20].”