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ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features

af Lakshmi Devi R
ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features af Lakshmi Devi R
Bag om ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features

"ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features" by Lakshmi Devi R. is an innovative and comprehensive study on the use of machine learning algorithms for the diagnosis of cardiac arrhythmia using electrocardiogram (ECG) and photoplethysmogram (PPG) signal features. Through detailed research and analysis, Lakshmi Devi R. highlights the potential benefits of machine learning in accurately detecting various types of cardiac arrhythmias. The author also discusses the significance of using both ECG and PPG signals to improve the accuracy of the diagnosis. Whether you are a healthcare professional looking for new approaches to diagnosing cardiac arrhythmias or a researcher interested in the latest advancements in the field, "ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features" is an essential resource. Order your copy today and discover how machine learning can play a vital role in improving the accuracy and efficiency of cardiac arrhythmia diagnosis.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9786801031627
  • Indbinding:
  • Paperback
  • Sideantal:
  • 184
  • Udgivet:
  • 3. april 2023
  • Størrelse:
  • 152x11x229 mm.
  • Vægt:
  • 276 g.
  • 8-11 hverdage.
  • 10. december 2024
På lager
Forlænget returret til d. 31. januar 2025

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"ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features" by Lakshmi Devi R. is an innovative and comprehensive study on the use of machine learning algorithms for the diagnosis of cardiac arrhythmia using electrocardiogram (ECG) and photoplethysmogram (PPG) signal features.
Through detailed research and analysis, Lakshmi Devi R. highlights the potential benefits of machine learning in accurately detecting various types of cardiac arrhythmias. The author also discusses the significance of using both ECG and PPG signals to improve the accuracy of the diagnosis.
Whether you are a healthcare professional looking for new approaches to diagnosing cardiac arrhythmias or a researcher interested in the latest advancements in the field, "ML-Based Cardiac Arrhythmia Diagnosis using ECG and PPG Signal Features" is an essential resource.
Order your copy today and discover how machine learning can play a vital role in improving the accuracy and efficiency of cardiac arrhythmia diagnosis.

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