Time-frequency coherence analysis of atrial fibrillation termination during procainamide administration

1997 ◽  
Vol 25 (6) ◽  
pp. 975-984 ◽  
Author(s):  
Eric G. Lovett ◽  
Kristina M. Ropella
Keyword(s):  
Atrial Fibrillation ◽  
Coherence Analysis ◽  
Time Frequency ◽  
Frequency Coherence

scholarly journals Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pablo Armañac-Julián ◽  
David Hernando ◽  
Jesús Lázaro ◽  
Candelaria de Haro ◽  
Rudys Magrans ◽  
...  
Keyword(s):  
Heart Rate ◽  
Mechanical Ventilation ◽  
Spontaneous Breathing Trial ◽  
Sinus Arrhythmia ◽  
Time Frequency ◽  
Cardiopulmonary Coupling ◽  
Cardiac Pumping ◽  
Successful Weaning ◽  
Frequency Coherence ◽  
Subspace Projections

AbstractThe ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients’ readiness, there is still around 15–20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation –being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.

Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment

2021 ◽  
Vol 5 (2) ◽  
pp. 1-19
Author(s):  
Hanbin Zhang ◽  
Li Zhu ◽  
Viswam Nathan ◽  
Jilong Kuang ◽  
Jacob Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Early Detection ◽  
Daily Living ◽  
Research Study ◽  
Heart Rhythm ◽  
Living Environment ◽  
User Engagement ◽  
Free Living ◽  
Time Frequency

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.

scholarly journals Quasi-Light-Storage based on time-frequency coherence

2009 ◽  
Vol 17 (18) ◽  
pp. 15790 ◽  
Author(s):  
Stefan Preußler ◽  
Kambiz Jamshidi ◽  
Andrzej Wiatrek ◽  
Ronny Henker ◽  
Christian-Alexander Bunge ◽  
...  
Keyword(s):  
Light Storage ◽  
Time Frequency ◽  
Frequency Coherence
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