Work

Diagnosis of cardiac diseases using spectral features and machine learning

Year: 2024

Type: conference-paper

Abstract: This study presents combination of spectral analysis method, generalized discriminant analysis (GDA), and the extreme learning machine (LELM) classifier algorithms which provide a robust method for prediction of cardiac diseases. Firstly, spectral approaches, has been applied for features extraction from heart rate variability (HRV) signal. The proposed GDA +ELM model was fitted with the features derived from approaches for the classification model of congestive heart disease and coronary artery disease. The standard database of HRV signal was collected from Physio net ATM's HRV source for training and validating predictive models for cardiac diseases.

Source: International Conference on Information Management & Machine Intelligence (ICIMMI)

DOI: 10.1145/3745812.3745853

Authors: Shashi Kant Tiwari, Shweta Sinha, Karamjit Kaur Sekhon, Ram Sewak Singh

Institutions: Amity University Gurugram, Adama Science and Technology University

Language: English

Topic: Heart Rate Variability and Autonomic Control

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4413915542

FWCI:

Cites: 23

Cited by:

Related to: 10