Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Aydin, Seda Guzel; Güler, Hasan

doi:10.17706/ijcee.2016.8.4.272-279

Cited by 11 publications

(2 citation statements)

References 16 publications

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“…Various things can affect HRV, including the in uence of the autonomic nervous system, tness, Body Mass Index (BMI), blood volume returning to the heart (venous return), respiration, arrhythmia, etc. [9]. In this study, it is being presented the development of a simple, a low-cost ECG solution with an integrated system in it, which can identify R-R intervals and average HR by using the time domain's HRV method.…”

Section: Introductionmentioning

confidence: 99%

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

Oktivasari¹,

Ishartati²,

Riandini³

et al. 2021

Preprint

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A simple system, a low-cost, fully automated, and design for monitoring RR interval Electrocardiography (ECG) signal described in this paper. The platform, named Simple Low-Cost Electrocardiography System (SLES), is capable of monitoring RR interval and R peaks in 3 lead standards. The system is in .exe format, so it can be easily installed on a computer. The system's goal is to design a fully integrated system for measuring a characteristic of Heart Rate Variability (HRV) parameters for various applications in heart signal research and education. The ECG signal is analog filtered and amplified and processed from analog to digital. Eventually, the ECG signal will be shown on the monitor after digital filtering. The data obtained from the ECG will accurately reflect the status of human heart health. The system has the benefits of small volume, low power consumption, low cost, and real-time operation. All design and development reports, files, and system software will be given non-commercial use online on https://github.com/oktivasari.

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Section: Introductionmentioning

confidence: 99%

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

Oktivasari¹,

Ishartati²,

Riandini³

et al. 2021

Preprint

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“…The biggest challenge in the health domain is to identify the key features that cause heart disease. The aim of this study is to define the key characteristics that are used to diagnose heart disease in order to solve the heart attack problem in a safe society [4]. In the healthcare industry, effective and reliable automated heart disease prediction systems can be useful for heart disease prediction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Computational Rough Set Based Feature Extraction For Heart Disease Analysis

Prabha¹,

Senthil²,

Lazha³

et al. 2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

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Cardiovascular disease is the most difficult disease to diagnose in the medical field. The diagnosis is often contingent on a judgment based on the grouping of vast amounts of clinical and pathological data. As a result of this complication, a number of researchers have based their efforts on determining the most cost-effective and accurate way to predict heart disease.In the case of heart disease, an accurate diagnosis at an early stage is critical, since time is of the essence when heart disease is detected at an inopportune time. Machine learning has evolved in recent years with a plethora of accurate and supporting resources in the medical domain, and it has offered the best support for predicting disease with proper training and research. The main goal of this study is to use a rough computational intelligence approach to find specific heart disease features among a large number of features. The output of the proposed feature selection method outperforms that of conventional feature selection approaches. The rough computation approach's output is evaluated using various heart disease data sets and checked using real-time data sets.

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Feature Selection for HRV to Optimized Meticulous Presaging of Heart Disease Using LSTM Algorithm

Aggarwal

Kumar

2022

Lecture Notes in Electrical Engineering

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Heart Rate Variability (HRV) Based Feature Extraction for Congestive Heart Failure

Cited by 11 publications

References 16 publications

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

Development of Low-Cost ECG System for Comprehensive Monitoring Heart Function

A Novel Computational Rough Set Based Feature Extraction For Heart Disease Analysis

Feature Selection for HRV to Optimized Meticulous Presaging of Heart Disease Using LSTM Algorithm

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