DeepAI AI Chat
Log In Sign Up

Unsupervised ensembling of multiple software sensors: a new approach for electrocardiogram-derived respiration using one or two channels

by   John Malik, et al.
Duke University

While several electrocardiogram-derived respiratory (EDR) algorithms have been proposed to extract breathing activity from a single-channel ECG signal, conclusively identifying a superior technique is challenging. We propose viewing each EDR algorithm as a software sensor that records the breathing activity from the ECG signal, and ensembling those software sensors to achieve a higher quality EDR signal. We refer to the output of the proposed ensembling algorithm as the ensembled EDR. We test the algorithm on a large scale database of 116 whole-night polysomnograms and compare the ensembled EDR signal with four respiratory signals recorded from four different hardware sensors. The proposed algorithm consistently improves upon other algorithms, and we envision its clinical value and its application in future healthcare.


page 1

page 2

page 3

page 4


Extract fetal ECG from single-lead abdominal ECG by de-shape short time Fourier transform and nonlocal median

The multiple fundamental frequency detection problem and the source sepa...

Deep Learning for ECG Segmentation

We propose an algorithm for electrocardiogram (ECG) segmentation using a...

Nonlinear and statistical analysis of ECG signals from Arrhythmia affected cardiac system through the EMD process

The human heart is a complex system exhibiting stochastic nature, as ref...

Representing and Denoising Wearable ECG Recordings

Modern wearable devices are embedded with a range of noninvasive biomark...

Smart Application for Fall Detection Using Wearable ECG Accelerometer Sensors

Timely and reliable detection of falls is a large and rapidly growing fi...