ECG Feature Extraction

Annam, Jagadeeswara Rao; Sujatha, R.; Somala, Jayaprada; Prasad, Gutta V. S. N. R. V.; Satyala, Narayana; Surampudi, Bapi Raju

doi:10.1007/978-3-030-78284-9_8

Cited by 1 publication

(2 citation statements)

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“…PT interval itself is the interval from point P to point T. Bpm is the patient's heart rate every minute. The formula for Interval PT and Bpm [11] is shown by ( 4) and (5).…”

Section: B)mentioning

confidence: 99%

“…Until now, there have been many studies that discuss the classification of this pattern, but there are still problems in determining the best features to recognize and classify the pattern of the heart rate. There are many types of feature extraction used in ECG research, such as the wavelet transform coefficient feature [3], [4] frequency-based features [5], and Hermit polynomials [6]. Most of them use the time domain representation and frequency of the ECG signal as the feature.…”

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram signals classification using random forest method for web-based smart healthcare

Sari,

Madona,

Kusryanto

et al. 2023

IJAAS

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<span>Coronary heart is the highest cause of death in Indonesia reaching 26%. Therefore, to prevent the high mortality rate of coronary heart disease (CHD), early detection of CHD can be carried out. One way is to examine the electrocardiogram/electrocardiograph (ECG) recording. ECG hardware has been made in previous studies to record ECG signals. ECG research is an important study because it can detect cardiovascular disease. Cardiovascular diseases can be classified as arrhythmic diseases. Arrhythmia is a disorder that occurs in the heart rhythm. The method used to recognize and classify ECG signal patterns is the R-R interval (RRI) method. In this study, the ECG signal is classified as normal and abnormal. Abnormal means that a person has a heart rhythm disorder. The classification method used is random forest. The advantage of the random forest classifier is that it can handle noise and missing values and can handle large amounts of data. The accuracy of the ECG signal classification using the Random forest method is 96%. The contribution of this research is that early detection of heart rhythm disorders using an ECG can be monitored through the smart healthcare web.</span>

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“…PT interval itself is the interval from point P to point T. Bpm is the patient's heart rate every minute. The formula for Interval PT and Bpm [11] is shown by ( 4) and (5).…”

Section: B)mentioning

confidence: 99%