Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep apnoea

By Philip de Chazal, Conor Heneghan, Elaine Sheridan, Richard B. Reilly, Philip Nolan, Mark J. O'Malley. IEEE Transactions on Biomedical Engineering, 2003

431 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience, Physiology

View Paper: https://www.semanticscholar.org/paper/a807078ceb96ca7c66009a6a80480bbbc1395c52

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. Marimuthu Swami Palaniswami
2. S. Van Huffel

In Cognitive Neuroscience

1. Marimuthu Swami Palaniswami
2. S. Van Huffel

Citations in the corpus

• A Novel Algorithm for the Automatic Detection of Sleep Apnea From Single-Lead ECG (2015, methodology): “For instance, for purely automatic algorithms based on single-lead ECG, accuracies of about 85% to 90% were reported in [6] and [8] on the Physionet dataset.”
• Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG (2011, methodology): “A transformation was applied where appropriate so that the histogram of the transformed feature approximates more closely a Gaussian distribution [ 13 ].”
• Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings (2009, methodology): “model [22], classification and regression tree method [11], Bayesian hierarchical model [23] have been used for automatic recognition of OSAS subjects based on the selected subset of”