Proceedings

R Peak-based Algorithm for Bradycardia and Tachycardia Detection

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Heart disease or cardiovascular disease is the deadliest disease in the world which claims about 17.9 million lives globally every year. Patients who suffer from cardiovascular disease are usually more severe than the patients who don’t know how to manage their lifestyle. Hence, it needs to improve the people’s lifestyle by providing smart reminders or self -diagnosis actions using the wearable technology and health applications. It is expected that the prevalence of cardiovascular disease will decrease with the introduction of lifestyle management tools and health technology applications. The health technology device that is used to treat cardiovascular diseases is electrocardiogram. In the study, ECG (electrocardiogram) algorithm will be developed for detecting bradycardia and tachycardia will be developed using Python programming. The target of this study is to find the BPM (beat per minute) of ECG using R peak-based algorithm and will be compared with the data from PhysioNet. The methodology that will be done in this study are gather the 12 CUDB patients from the PhysioNet, the sampling frequency that will be used is 250 Hz, and will be recorded for 6minutes and divided into 6 segments, each of segment contains 1 minute, the next will be do the filtering the ECG data, R peak detection & BPM detection using simple algorithm, calculate the percent error, and confusion matrix. Based on the results, the developed algorithm can detect bradycardia and tachycardia with the help of amplitude and frequency parameters. The different percent error between PhysioNet’s result and the predicted algorithm’s result was heavily affected by the number of highcut and height, and the results obtained from the confusion matrix state that the accuracy of the results was 91.94%, precision was 92.78%, the recall was 93.64%, and the f1-score was 93.21%. in conclusion, all of the results obtained have an excellent confusion matrix analysis.

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Availability

Detail Information

Series Title

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Call Number

EP BI003

Publisher

i3L, Jakarta : i3L, Jakarta., 2023

Collation

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Language

English

ISBN/ISSN

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Classification

NONE

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

ECG
Algorithm
cardiovascular
bradycardia
tachycardia
BPM
PhysioNet

Specific Detail Info

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Statement of Responsibility

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Other version/related

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