Automated Diagnosis System for Outpatients and Inpatients With Cardiovascular Diseases

Karboub, Kaouter; Tabaa, Mohamed; Monteiro, Fabrice; Dellagi, Sofiène; Moutaouakkil, Fouad; Abbas, Dandache

doi:10.1109/jsen.2020.3019668

Cited by 13 publications

(2 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feedforward neural network was used for preprocessing and classification of ECG signals. Classification of left bundle branch block, right bundle branch block, premature ventricular beats, premature atrial beats, premature lymph node beats, and normal beats was implemented in the MIT-BIH database [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Arrhythmias are a relatively common type of cardiovascular disease. Most cardiovascular diseases are often accompanied by arrhythmias. In clinical practice, an electrocardiogram (ECG) can be used as a primary diagnostic tool for cardiac activity and is commonly used to detect arrhythmias. Based on the hidden and sudden nature of the MIT-BIH ECG database signal and the small-signal amplitude, this paper constructs a hybrid model for the temporal correlation characteristics of the MIT-BIH ECG database data, to learn the deep-seated essential features of the target data, combine the characteristics of the information processing mechanism of the arrhythmia online automatic diagnosis system, and automatically extract the spatial features and temporal characteristics of the diagnostic data. First, a combination of median filter and bandstop filter is used to preprocess the data in the ECG database with individual differences in ECG waveforms, and there are problems of feature inaccuracy and useful feature omission which cannot effectively extract the features implied behind the massive ECG signals. Its diagnostic algorithm integrates feature extraction and classification into one, which avoids some bias in the feature extraction process and provides a new idea for the automatic diagnosis of cardiovascular diseases. To address the problem of feature importance variability in the temporal data of the MIT-BIH ECG database, a hybrid model is constructed by introducing algorithms in deep neural networks, which can enhance its diagnostic efficiency.

show abstract

Section: Related Workmentioning

confidence: 99%

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Yan

Zhang

2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…Although with a higher computational cost, Manhattan, Chebyshev or Mahalonobis distances can also be used. Recent developed methods used KNN for ECG arrhythmia classification [35], [88]- [90], [101].…”

Section: K Nearest Neighbourmentioning

confidence: 99%

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Cretu

Tindale

Abbod

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

Cardiac arrhythmia is a condition caused by an impaired electrical conduction of the heart, resulting in irregular rhythms that can increase the risk of stroke or even lead to sudden cardiac deaths. Luckily, bio-signals such as electrocardiogram (ECG) and arterial blood pressure (ABP) can be utilized to assess the health of the patient. This work presents a comprehensive review on the recent machine learning (ML) and deep learning methods applied for arrhythmia classification using both, ECG and ABP signals, including preliminary steps such as pre-processing, feature extraction and feature optimization.

show abstract

Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern

Prasanna,

George,

Subathra

2024

Neural Comput & Applic

View full text Add to dashboard Cite

Automated Diagnosis System for Outpatients and Inpatients With Cardiovascular Diseases

Cited by 13 publications

References 56 publications

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Online Automatic Diagnosis System of Cardiac Arrhythmias Based on MIT-BIH ECG Database

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery

Detection of neurodegenerative diseases using hybrid MODWT and adaptive local binary pattern

Contact Info

Product

Resources

About