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Pulse rhythm

Pulse rhythm

Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac muscle...

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Related Experiment Video

Updated: May 8, 2026

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

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Published on: January 8, 2013

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic Assisted Beating Heart Surgery.

E Erdem Tuna¹, Timothy J Franke, Ozkan Bebek

¹Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.

IEEE Transactions on Robotics : a Publication of the IEEE Robotics and Automation Society

|August 27, 2013

Summary

Robotic surgery can stabilize beating hearts using Active Relative Motion Canceling (ARMC). New prediction algorithms improve tracking accuracy by estimating future heart motion, crucial for surgical precision.

Keywords:

Surgical robotics active relative motion canceling beating heart surgery prediction algorithm signal estimation

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Last Updated: May 8, 2026

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Published on: September 16, 2009

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Area of Science:

Robotics
Medical Engineering
Control Systems

Background:

Minimally invasive surgery requires precise control of robotic instruments.
Operating on a beating heart presents significant challenges due to cardiac motion.
Active Relative Motion Canceling (ARMC) is a technique to stabilize robotic surgical tools relative to heart surface points.

Purpose of the Study:

To develop and evaluate novel prediction algorithms for Active Relative Motion Canceling (ARMC) in robotic assisted beating heart surgery.
To assess the impact of predictor parameters and heart rate variations on tracking performance.
To compare the proposed algorithms against an Extended Kalman Filter for one-step prediction and tracking accuracy.

Main Methods:

Implementation and study of two least-square based prediction algorithms utilizing adaptive filters.
Method 1: Linear system relation assumption for future motion prediction.
Method 2: Independent parametrization for each point over the prediction horizon.

Main Results:

Evaluation of predictor performance using a 3-DOF test-bed and in-vivo motion data.
Analysis of tracking accuracy under constant and varying heart rates.
Comparison of proposed methods with Extended Kalman Filter (EKF) for prediction and tracking.

Conclusions:

The proposed least-square based prediction algorithms demonstrate potential for enhancing ARMC in robotic heart surgery.
Understanding the influence of predictor parameters and heart rate is key to optimizing tracking performance.
These algorithms offer a viable alternative for achieving precise robotic control during beating heart procedures.