Arrhythmia Classification using DWT-Coefficient Energy Ratios

Mahgoub, Asma; Tanveer, Adeen; Qidwai, Uvais

doi:10.1109/iecbes.2018.8626726

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…A cardiac arrhythmia is when the heart beats erratically [129]. The primary reason for an arrhythmia can be a heart rate that is abnormally high, abnormally low, or irregular [130]. This happens due to abnormal signals from the heart.…”

Section: Cardiac Arrhythmia Detectionmentioning

confidence: 99%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

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Heart rate variability (HRV) has emerged as an essential non-invasive tool for understanding cardiac autonomic function over the last few decades. This can be attributed to the direct connection between the heart’s rhythm and the activity of the sympathetic and parasympathetic nervous systems. The cost-effectiveness and ease with which one may obtain HRV data also make it an exciting and potential clinical tool for evaluating and identifying various health impairments. This article comprehensively describes a range of signal decomposition techniques and time-series modeling methods recently used in HRV analyses apart from the conventional HRV generation and feature extraction methods. Various weight-based feature selection approaches and dimensionality reduction techniques are summarized to assess the relevance of each HRV feature vector. The popular machine learning-based HRV feature classification techniques are also described. Some notable clinical applications of HRV analyses, like the detection of diabetes, sleep apnea, myocardial infarction, cardiac arrhythmia, hypertension, renal failure, psychiatric disorders, ANS Activity of Patients Undergoing Weaning from Mechanical Ventilation, and monitoring of fetal distress and neonatal critical care, are discussed. The latest research on the effect of external stimuli (like consuming alcohol) on autonomic nervous system (ANS) activity using HRV analyses is also summarized. The HRV analysis approaches summarized in our article can help future researchers to dive deep into their potential diagnostic applications.

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Section: Cardiac Arrhythmia Detectionmentioning

confidence: 99%

A Review of Methods and Applications for a Heart Rate Variability Analysis

Nayak,

Pradhan,

Mohanty

et al. 2023

Algorithms

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show abstract

“…A classification algorithm was proposed based on the features extracted from discrete wavelet transform (DWT) using ECG signals. Each ECG signal was decomposed using multilevel wavelet decomposition [10]. Then, approximate and details coefficients were calculated for each level.…”

Section: Relate Workmentioning

confidence: 99%

Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition

Sarshar¹,

Abdossalehi²

2021

RCES

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Today, cardiovascular disease has become an epidemic. Statistics show that one person dies every 33 seconds due to cardiovascular disease. It is estimated that 33% of men and 10% of women have a heart attack before the age of 60. Arrhythmias are abnormal beats that cause the heart to beat too fast or too slow to pump. Automatic electrocardiogram analysis is critical to the diagnosis and treatment of heart patients. There are several learning methods for analyzing ECG signals to diagnose arrhythmias. In the proposed method, the heart rate signals are decomposed into different sub bands using the Tunable Q-Factor Wavelet Transform (TQWT) method, then the features are extracted and modified using classification with the aim of better classifying and separating data in the process of identifying the clinical features of the class. They are classified so that normal people and people with cardiac arrhythmias can be distinguished from their ECG signals. The results showed that the proposed method classifies the ECG signal with 99.25% accuracy. Since accuracy in diagnosing cardiac arrhythmias in medicine is a vital and important factor, so the proposed method can be very effective for the decision of cardiologists.

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PPG-ECG Conversion and Classification for Cardiac Activity Diagnosis

Diani,

Boukadoum

2024

2024 22nd IEEE Interregional NEWCAS Conference (NEWCAS)

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Arrhythmia Classification using DWT-Coefficient Energy Ratios

Cited by 4 publications

References 7 publications

A Review of Methods and Applications for a Heart Rate Variability Analysis

A Review of Methods and Applications for a Heart Rate Variability Analysis

Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition

PPG-ECG Conversion and Classification for Cardiac Activity Diagnosis

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