4.3 Article

An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning

Related references

Note: Only part of the references are listed.
Article Physiology

Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning

Yunendah Nur Fuadah et al.

Summary: Cardiovascular disorders, particularly AF and CHF, are leading causes of mortality worldwide. This study proposes a method to improve the accuracy of diagnosing these disorders by extracting features from ECG signals using the discrete wavelet transform and Hjorth descriptor. By optimizing different classifier algorithms, the classification accuracy of AF, CHF, and NSR conditions was significantly improved compared to previous studies.

FRONTIERS IN PHYSIOLOGY (2022)

Article Multidisciplinary Sciences

Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction

Shahadat Uddin et al.

Summary: This paper studies different variants of the k-nearest neighbour (KNN) algorithm and compares their performance in disease prediction. By implementing and experimenting on eight datasets, the study found that accuracy values ranged from 64.22% to 83.62%, with Hassanaat KNN showing the highest accuracy. The study also proposes a relative performance index based on accuracy, precision, and recall measures, identifying Hassanaat KNN as the best performing variant.

SCIENTIFIC REPORTS (2022)

Article Biology

An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds

Prabal Datta Barua et al.

Summary: This study developed an accurate sound classification model for the diagnosis of valvular heart disease using dual symmetric tree pattern and multilevel discrete wavelet transform, achieving excellent classification performance on a large prospective heart sound dataset at a low computational cost.

COMPUTERS IN BIOLOGY AND MEDICINE (2022)

Article Computer Science, Information Systems

The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification

Jorge Oliveira et al.

Summary: Cardiac auscultation is a cost-effective technique used for detecting and identifying heart conditions. However, the application of computer-assisted decision systems based on auscultation is limited due to the lack of large publicly available datasets. To address this issue, a team has prepared the largest pediatric heart sound dataset, including detailed descriptions of cardiac murmurs.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2022)

Article Engineering, Biomedical

Automated detection of abnormal heart sound signals using Fano-factor constrained tunable quality wavelet transform

Nidhi Kalidas Sawant et al.

Summary: Manual interpretation of heart sounds is unreliable, but automated systems incorporating AI and signal processing tools can improve disease detection sensitivity and reduce subjectivity. A novel method using TQWT for automated binary classification of heart sounds achieved high accuracy, validated through cross-validation and SMOTE for balanced data sets. This developed model can be integrated into digital stethoscopes to assist clinicians in diagnosing abnormal heart sounds.

BIOCYBERNETICS AND BIOMEDICAL ENGINEERING (2021)

Article Chemistry, Multidisciplinary

Research on Segmentation and Classification of Heart Sound Signals Based on Deep Learning

Yi He et al.

Summary: The study utilized CNN for segmentation and classification of heart sound signals, achieving high accuracy and performance through data preprocessing and the application of deep learning networks.

APPLIED SCIENCES-BASEL (2021)

Article Computer Science, Information Systems

Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals

Turker Tuncer et al.

Summary: This study aimed to diagnose heart valve diseases and normal heart sounds using machine learning, proposing an automated classification method based on PCG signals. By combining a novel feature generator and decomposition model, high accuracy heart sound classification was achieved, providing a new approach for diagnosing heart valve diseases.

INFORMATION SCIENCES (2021)

Article Environmental Sciences

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

James Magidi et al.

Summary: This study utilized random forest algorithm on Google Earth Engine platform to process and classify irrigated areas in Mpumalanga Province, Africa using NDVI to differentiate between irrigated and rainfed areas. Assessment of irrigated areas in 2019 and 2020, along with the impact of Covid-19 pandemic on agriculture, helped in evaluating changes in irrigated areas in smallholder farming areas.

REMOTE SENSING (2021)

Article Medicine, General & Internal

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

Mehmet Ali Kobat et al.

Summary: A computer-aided diagnostic system was developed to differentiate between normal, COVID-19, and HF patients using cough sounds, achieving high accuracy through DNA pattern recognition technology.

DIAGNOSTICS (2021)

Article Chemistry, Multidisciplinary

Methods for Improving Deep Learning-Based Cardiac Auscultation Accuracy: Data Augmentation and Data Generalization

Yoojin Jeong et al.

Summary: The study focuses on improving the performance of cardiac auscultation through data augmentation and generalization, showing an overall improvement in sensitivity, specificity, and F1-score.

APPLIED SCIENCES-BASEL (2021)

Review Physics, Multidisciplinary

Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Wei Chen et al.

Summary: This paper discusses the importance of automated heart sound classification in the diagnosis of cardiovascular diseases and the current application and challenges of deep learning methods in this field. The study focuses on analyzing CNN and RNN methods developed over the past five years, with the goal of improving the accuracy of heart sound classification.

ENTROPY (2021)

Article Computer Science, Interdisciplinary Applications

Hyperparameter Tuning for Machine Learning Algorithms Used for Arabic Sentiment Analysis

Enas Elgeldawi et al.

Summary: Machine learning models are being utilized across a variety of disciplines to address complex problems. Proper hyperparameter tuning is essential for achieving higher accuracy in machine learning classifiers. In this study, various hyperparameter tuning techniques were compared in optimizing the accuracy of different machine learning algorithms, with Bayesian Optimization proving to be the most effective in an Arabic sentiment classification task.

INFORMATICS-BASEL (2021)

Article Engineering, Multidisciplinary

How many Mel-frequency cepstral coefficients to be utilized in speech recognition? A study with the Bengali language

Md. Rakibul Hasan et al.

Summary: This study found that the best performance was achieved with 24/25 MFCCs, suggesting that the optimal number of MFCCs should be 25. Increasing the number of MFCCs improves classification metrics with lower computational burden.

JOURNAL OF ENGINEERING-JOE (2021)

Article Chemistry, Multidisciplinary

Classification of Heart Sounds Using Convolutional Neural Network

Fan Li et al.

APPLIED SCIENCES-BASEL (2020)

Article Biotechnology & Applied Microbiology

Machine Learning for the Diagnosis of Orthodontic Extractions: A Computational Analysis Using Ensemble Learning

Yasir Suhail et al.

BIOENGINEERING-BASEL (2020)

Article Engineering, Biomedical

Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network

Palani Thanaraj Krishnan et al.

PHYSICAL AND ENGINEERING SCIENCES IN MEDICINE (2020)

Article Health Care Sciences & Services

Classifying Heart Sounds Using Images of Motifs, MFCC and Temporal Features

Diogo Marcelo Nogueira et al.

JOURNAL OF MEDICAL SYSTEMS (2019)

Review Chemistry, Multidisciplinary

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Diana C. Toledo-Perez et al.

APPLIED SCIENCES-BASEL (2019)

Article Computer Science, Artificial Intelligence

Heart sound classification based on scaled spectrogram and tensor decomposition

Wenjie Zhang et al.

EXPERT SYSTEMS WITH APPLICATIONS (2017)

Article Biophysics

An open access database for the evaluation of heart sound algorithms

Chengyu Liu et al.

PHYSIOLOGICAL MEASUREMENT (2016)

Article Computer Science, Artificial Intelligence

Automatic diagnosis of septal defects based on tunable-Q wavelet transform of cardiac sound signals

Shivnarayan Patidar et al.

EXPERT SYSTEMS WITH APPLICATIONS (2015)

Article Engineering, Biomedical

Monitoring Cardiac Stress Using Features Extracted From S1 Heart Sounds

Jonathan Herzig et al.

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING (2015)

Article Computer Science, Artificial Intelligence

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

Shivnarayan Patidar et al.

EXPERT SYSTEMS WITH APPLICATIONS (2014)

Article Engineering, Biomedical

Segmentation of cardiac sound signals by removing murmurs using constrained tunable-Q wavelet transform

Shivnarayan Patidar et al.

BIOMEDICAL SIGNAL PROCESSING AND CONTROL (2013)

Editorial Material Medicine, General & Internal

An argument for reviving the disappearing skill of cardiac auscultation

Donald Clark et al.

CLEVELAND CLINIC JOURNAL OF MEDICINE (2012)