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  Published Paper Details:

  Paper Title

Predictive Modeling Of Cardiac Abnormalities

  Authors

  Ramesh Shahabadkar,  R N Mahima,  Revin Andrade,  Shilpi Kumari,  Ujjawal Rathore

  Keywords

Electrical signals; ARM Cortex M4; real-time flexibility; Single functionality; Internet of Things; Irregular heartbeat.

  Abstract


Electrical signals in the heart are recorded by the electrocardiogram (ECG). The electrocardiogram is a diagnostic tool for heart diseases such as heart arrhythmias, sleep apnea, and myocardial infarction. To prioritize the detection of abnormalities in ECG data, this research presents a technology identified using machine learning algorithms. The final model that emerges is a method defined by regular heart rate analysis. The model, which is expected to reduce power consumption by more than 50%, is used in ARM Cortex M4-based embedded devices. The system is well-prepared for clinical use due to its real-time adaptation, single processing, reduced complexity and interpretation. It is an Internet of Things (IoT)-enabled wearable edge sensor that provides the necessary sensitivity and accuracy by consuming very little power.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A4512

  Paper ID - 258001

  Page Number(s) - n120-n128

  Pubished in - Volume 12 | Issue 4 | April 2024

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Ramesh Shahabadkar,  R N Mahima,  Revin Andrade,  Shilpi Kumari,  Ujjawal Rathore,   "Predictive Modeling Of Cardiac Abnormalities", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.n120-n128, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A4512.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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