Rate-adaptive pacemaker controlled by motion and respiratory rate using neuro-fuzzy algorithm

Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which cont...

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Bibliographic Details
Published in:Medical & biological engineering & computing Vol. 39; no. 6; pp. 694 - 699
Main Authors: Shin, J. W., Yoon, J. H., Yoon, Y. R.
Format: Journal Article
Language:English
Published: Heidelberg Springer 01.11.2001
Springer Nature B.V
Subjects:
Adaptation, Physiological
Algorithms
Biological and medical sciences
Diseases of the cardiovascular system
Fuzzy control
Fuzzy Logic
Heart rate
Humans
Medical sciences
Motion
Nursing
Pacemaker, Artificial
Pacemakers
Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects)
Respiratory Mechanics
Respiratory system
Sensors
Table lookup
Performance evaluation
Physical exercise
Human
Motion
Instrumentation therapy
Screening test
Heart rate
Instrumental stimulation
Accuracy
Fuzzy algorithm
Pacemaker
Respiratory frequency
Circulatory system
Automatic monitoring
Inference rule
ISSN:0140-0118, 1741-0444
Online Access:Get full text
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Summary:Rate-adaptive pacemakers use information from sensors to change the rate of heart stimulation. Until now, fuzzy-pacemaker algorithms have been used to combine inputs from sensors to improve heart rate control, but they have been difficult to implement. In this paper, a pacemaker algorithm which controlled heart rate adaptively by motion and respiratory rate was studied. After chronotropic assessment exercise protocol (CAEP) tests were performed to collect activity and respiratory rate signals, the intrinsic heart rate was inferred from these two signals by a neuro-fuzzy method. For 10 subjects the heart rate inference, using the neuro-fuzzy algorithm, gave 52.4% improved accuracy in comparison with the normal fuzzy table look-up method. The neuro-fuzzy method was applied to a real pacemaker by reduced mapping of the neuro-fuzzy look-up table.
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ISSN:0140-0118
1741-0444
DOI:10.1007/BF02345444