2nd IET International Conference on Intelligent Signal Processing 2015 (ISP) 2015
DOI: 10.1049/cp.2015.1781
|View full text |Cite
|
Sign up to set email alerts
|

Arrhythmias discrimination based on fractional order system and KNN classifier

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
13
0

Year Published

2018
2018
2024
2024

Publication Types

Select...
4
2
1

Relationship

0
7

Authors

Journals

citations
Cited by 10 publications
(13 citation statements)
references
References 0 publications
0
13
0
Order By: Relevance
“…Ref. [47] describes an approach to do the same to obtain five parameters from a QRS complex signal to differentiate between healthy and three types of arrhythmic signals using a k nearest neighbor classifier.…”
Section: Biomedical Applicationsmentioning
confidence: 99%
See 3 more Smart Citations
“…Ref. [47] describes an approach to do the same to obtain five parameters from a QRS complex signal to differentiate between healthy and three types of arrhythmic signals using a k nearest neighbor classifier.…”
Section: Biomedical Applicationsmentioning
confidence: 99%
“…Although the publications by [43][44][45][46][47][48] have already been categorized as preprocessing, they also serve as a hybrid machine learning and fractional dynamics approach. Specifically, as authors employed the transfer function from a fractional order model to first, model a signal and then to obtain the functions parameters or the corresponding error and signal energy of the model as features for classification.…”
Section: Machine Learning and Fractional Dynamicsmentioning
confidence: 99%
See 2 more Smart Citations
“…Afterwards, a constant number of points that account for 0.278 seconds is selected before and after each extracted R-point . Therefore, a final beat of 200 sample points or 0.556 seconds is resulted, which is considered sufficient to capture most of the heartbeat information [39].…”
Section: Beats Extractionmentioning
confidence: 99%