Adaptive Parameter Estimation, Modeling and Patient-Specific Classification of Electrocardiogram Signals
abstract: Adaptive processing and classification of electrocardiogram (ECG) signals are important in eliminating the strenuous process of manually annotating ECG recordings for clinical use. Such algorithms require robust models whose parameters can adequately describe the ECG signals. Although diff...
Other Authors: | Edla, Shwetha Reddy (Author) |
---|---|
Format: | Doctoral Thesis |
Language: | English |
Published: |
2012
|
Subjects: | |
Online Access: | http://hdl.handle.net/2286/R.I.16031 |
Similar Items
-
Automated arrhythmia detection from electrocardiogram signal using stacked restricted Boltzmann machine model
by: Saroj Kumar Pandey, et al.
Published: (2021-05-01) -
Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals
by: Ahmad O. Aseeri
Published: (2021-06-01) -
Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram
by: Ji, Z., et al.
Published: (2023) -
Electrocardiogram Classification Based on Faster Regions with Convolutional Neural Network
by: Yinsheng Ji, et al.
Published: (2019-06-01) -
Comparison of Motion Artefact Reduction Methods and the Implementation of Adaptive Motion Artefact Reduction in Wearable Electrocardiogram Monitoring
by: Xiang An, et al.
Published: (2020-03-01)