Analysis of heart rate variability signal in meditation using second-order difference plot

2011 ◽  
Vol 109 (11) ◽  
pp. 114703 ◽  
Author(s):  
Damodar Prasad Goswami ◽  
Dewaki Nandan Tibarewala ◽  
Dilip Kumar Bhattacharya
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Second Order ◽  
Order Difference ◽  
Difference Plot

Deep learning with 3D-second order difference plot on respiratory sounds

2018 ◽  
Vol 45 ◽  
pp. 58-69 ◽  
Author(s):  
Gokhan Altan ◽  
Yakup Kutlu ◽  
Adnan Özhan Pekmezci ◽  
Serkan Nural
Keyword(s):  
Deep Learning ◽  
Second Order ◽  
Respiratory Sounds ◽  
Order Difference ◽  
Difference Plot

Extended Central Tendency Measure and difference plot for heart rate variability analysis

2019 ◽  
Vol 74 ◽  
pp. 33-40
Author(s):  
Rascius-Endrigho A.U. Belfort ◽  
Sara P.C. Treccossi ◽  
João L.F. Silva ◽  
Valdir G. Pillat ◽  
Celso B.N. Freitas ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Heart Rate Variability Analysis ◽  
Central Tendency ◽  
Central Tendency Measure ◽  
Variability Analysis ◽  
Difference Plot

scholarly journals A Classification System to Detect Congestive Heart Failure Using Second-Order Difference Plot of RR Intervals

2009 ◽  
Vol 2009 ◽  
pp. 1-7 ◽  
Author(s):  
R. A. Thuraisingham
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Success Rate ◽  
Classification System ◽  
Nearest Neighbor ◽  
Second Order ◽  
Rr Intervals ◽  
Order Difference ◽  
Holter Monitor ◽  
Difference Plot

A classification system to detect congestive heart failure (CHF) patients from normal (N) patients is described. The classification procedure uses thek-nearest neighbor algorithm and uses features from the second-order difference plot (SODP) obtained from Holter monitor cardiac RR intervals. The classification system which employs a statistical procedure to obtain the final result gave a success rate of 100% to distinguish CHF patients from normal patients. For this study the Holter monitor data of 36 normal and 36 CHF patients were used. The classification system using standard deviation of RR intervals also performed well, although it did not match the 100% success rate using the features from SODP. However, the success rate for classification using this procedure for SDRR was many fold higher compared to using a threshold. The classification system in this paper will be a valuable asset to the clinician, in the detection congestive heart failure.

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