| Title |
A Hybrid Algorithm for Prediction of Varying Heart Rate Motion in Computer-Assisted Beating Heart Surgery
|
|---|---|
| Published in |
Journal of Medical Systems, September 2018
|
| DOI | 10.1007/s10916-018-1059-6 |
| Pubmed ID | |
| Authors |
Saeed Mansouri, Farzam Farahmand, Gholamreza Vossoughi, Alireza Alizadeh Ghavidel |
| Abstract |
An essential requirement for performing robotic assisted surgery on a freely beating heart is a prediction algorithm which can estimate the future trajectory of the heart in the varying heart rate (HR) conditions of real surgery with a high accuracy. In this study, a hybrid amplitude modulation- (AM) and autoregressive- (AR) based algorithm was developed to enable estimating the global and local oscillations of the beating heart, raised from its major and minor physiological activities. The AM model was equipped with an estimator of the heartbeat frequency to compensate for the HR variations. The RMS of the prediction errors of the hybrid algorithm was in the range of 165-361 μm for the varying HR motion, 21% less than that of the single AM model. With the capability of providing highly accurate predictions in a wide range of HR variation, the hybrid model is promising for practical use in robotic assisted beating heart surgery. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 7 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 57% |
| Researcher | 2 | 29% |
| Other | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 5 | 71% |
| Medicine and Dentistry | 1 | 14% |
| Psychology | 1 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 September 2018.
All research outputs
#20,533,292
of 23,103,436 outputs
Outputs from Journal of Medical Systems
#1,017
of 1,165 outputs
Outputs of similar age
#293,713
of 337,432 outputs
Outputs of similar age from Journal of Medical Systems
#22
of 29 outputs
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