Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and Limitations

Mamun, Mohammad Mahbubur Rahman Khan; Alouani, A.T.

doi:10.1007/978-3-030-98015-3_29

Cited by 4 publications

(3 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, the development of machine learning have played a significant role in diagnosing various diseases. One example is the use of ECG signals to extract features to classify heart conditions [4]- [6]. An ECG is a quasi-periodical, rhythmical signal produced by the functioning of the heart, acting as the source of bioelectrical events.…”

Section: Introductionmentioning

confidence: 99%

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Fung,

Ghiasi

2023

J Stud Res

View full text Add to dashboard Cite

Myocardial Infarction (MI) is one of the leading causes of death. Electrocardiogram (ECG) is a non-invasive tool that is commonly used as a diagnostic tool to assess cardiac conditions. A dataset consisting ECG signals of healthy individuals and MI patients was subjected to pre-processing techniques like normalization and application of a bandpass filter. R-R peak intervals from the pre-processed ECG signals are extracted to generate the respiratory signal. The features extracted from the respiratory signal are used to predict MI. The objective of this study is to evaluate the potential of ECG derived respiratory signal (EDR) in predicting MI by utilizing machine learning techniques like random forest, linear regression, Convolutional Neural Network(CNN), Multilayer perceptron(MLP). The results of the study will examine the feasibility of using EDR in predicting MI and provide insight into the most effective machine learning technique for this application. This study will contribute to the development of new and efficient prediction methods for MI patients.

show abstract

Section: Introductionmentioning

confidence: 99%

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Fung,

Ghiasi

2023

J Stud Res

View full text Add to dashboard Cite

show abstract

“…However, there are still some challenges keeping the model or algorithms from being used in clinical settings, such as accuracy, reliability, consistency, interpretability, etc. [7][8][9][10][11][12]. Unless these shortcomings are solved, the experimental accuracy or good performance metrics found by the researchers with a chosen dataset will not impact real life.…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Khan Mamun,

Elfouly

2023

ASI

View full text Add to dashboard Cite

Contemporary methods used to interpret the electrocardiogram (ECG) signal for diagnosis or monitoring are based on expert knowledge and rule-centered algorithms. In recent years, with the advancement of artificial intelligence, more and more researchers are using deep learning (ML) and deep learning (DL) with ECG data to detect different types of cardiac issues as well as other health problems such as respiration rate, sleep apnea, and blood pressure, etc. This study presents an extensive literature review based on research performed in the last few years where ML and DL have been applied with ECG data for many diagnoses. However, the review found that, in published work, the results showed promise. However, some significant limitations kept that technique from implementation in reality and being used for medical decisions; examples of such limitations are imbalanced and the absence of standardized dataset for evaluation, lack of interpretability of the model, inconsistency of performance while using a new dataset, security, and privacy of health data and lack of collaboration with physicians, etc. AI using ECG data accompanied by modern wearable biosensor technologies has the potential to allow for health monitoring and early diagnosis within reach of larger populations. However, researchers should focus on resolving the limitations.

show abstract

“…In contrast, most datasets were not created for this purpose. Since feature numbers cannot be increased or modified, starting with a dataset having a significantly high number of features that are not only relevant but also nonredundant is essential [51]. In group-(d), the input in the figure consists of biomedical signals.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Mamun

Elfouly

2023

Bioengineering

View full text Add to dashboard Cite

Heart disease is a significant public health problem, and early detection is crucial for effective treatment and management. Conventional and noninvasive techniques are cumbersome, time-consuming, inconvenient, expensive, and unsuitable for frequent measurement or diagnosis. With the advance of artificial intelligence (AI), new invasive techniques emerging in research are detecting heart conditions using machine learning (ML) and deep learning (DL). Machine learning models have been used with the publicly available dataset from the internet about heart health; in contrast, deep learning techniques have recently been applied to analyze electrocardiograms (ECG) or similar vital data to detect heart diseases. Significant limitations of these datasets are their small size regarding the number of patients and features and the fact that many are imbalanced datasets. Furthermore, the trained models must be more reliable and accurate in medical settings. This study proposes a hybrid one-dimensional convolutional neural network (1D CNN), which uses a large dataset accumulated from online survey data and selected features using feature selection algorithms. The 1D CNN proved to show better accuracy compared to contemporary machine learning algorithms and artificial neural networks. The non-coronary heart disease (no-CHD) and CHD validation data showed an accuracy of 80.1% and 76.9%, respectively. The model was compared with an artificial neural network, random forest, AdaBoost, and a support vector machine. Overall, 1D CNN proved to show better performance in terms of accuracy, false negative rates, and false positive rates. Similar strategies were applied for four more heart conditions, and the analysis proved that using the hybrid 1D CNN produced better accuracy.

show abstract

Automatic Detection of Heart Diseases Using Biomedical Signals: A Literature Review of Current Status and Limitations

Cited by 4 publications

References 115 publications

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

Utilising Machine Learning to Predict Myocardial Infarction by Electrocardiogram Derived Respiration

AI-Enabled Electrocardiogram Analysis for Disease Diagnosis

Detection of Cardiovascular Disease from Clinical Parameters Using a One-Dimensional Convolutional Neural Network

Contact Info

Product

Resources

About