LSTM-Based Cardiovascular Disease Detection Using ECG Signal

Rath, Adyasha; Mishra, Debahuti; Panda, Ganapati

doi:10.1007/978-981-16-1056-1_12

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…For example, for the attribute “exercise time,” values may be “begin on 10 am.” The multiword group technique changes this to “started,” This significantly increases efficiency 38 – 41 …”

Section: Methodsmentioning

confidence: 99%

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Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha¹,

Sharma

2022

J. Electron. Imag.

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. Congenital heart failure (CHF) due to congestion in the blood is a serious cardiac problem correlated with crippling symptoms and leading to a rising death rate, monumental health care spending, and reduced quality of life. Heart disease prevention is among the most crucial functions of any medical system, as many people are prone to heart attacks worldwide. Although several segmentation methods for great vessels and the heart have been proposed in the research, they are not successful when applied to the health records of congenital heart disease. In this proposed work, the thickness and fat accumulation of most arteries are measured and analyzed, and then the measurement is synthesized with the corresponding width of the blood vessels of arteries; this data is used for training purposes in the convolutional neural network with one-off cross-validation and regularization. Using the CNN model, a confusion matrix is created and different statistical parameters such as accuracy sensitivity, specificity, precision, and f-score are generated. The final average accuracy was 97%, precision was 98.13%, and F-score was 98.36%. The results indicate that the CNN-based strategy can distinguish healthy hearts from those with prior cardiovascular disease.

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Section: Methodsmentioning

confidence: 99%

“…The multiword group technique changes this to "started," This significantly increases efficiency. [38][39][40][41]…”

Section: Multiword Groupingmentioning

confidence: 99%

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Sinha¹,

Sharma

2022

J. Electron. Imag.

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“…These devices which is connected with the person sends signal to the server available on the mobile of the patient [31][32][33][34][35]. These servers which are connected to the internet via GPRS or LAN, sends signal to health data to different nodes like health care worker, physician, caregiver, emergency worker, who analyses the result and take appropriate action according to their work scope [36]. Body Area Network Coordinator plays a central role to monitor all those devices activity which follows a star topology.…”

Section: Introductionmentioning

confidence: 99%

A hybrid machine learning approach for heart disease prediction using hyper parameter optimization

Sinha¹,

Sharma

2022

Preprint

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Heart disease is a serious terminal condition in most parts of the world. The acute lack of medical professionals, expertise, and technology to identify important signs. So a smart and efficient model and technology is required to lead early diagnosis of heart disease. The current study proposes a new experience-based method namely HSPUCD (Heart stage prediction using clinical data) to forecast cardiac disorders utilizing hybrid machine learning. Type 4 protocols Cross Validation is used to assess a model's competence on unknown data. Hyper parameters were modified to regulate the suggested model's behavior and performance. The suggested system is based on extensive research. Data pre-processing was utilized to discover the most relevant network traffic and generate the initial candidate collection of feature characteristics. The suggested model additionally uses automated feature selection to extract features from a candidate set of features and selects the most relevant subset of features from this candidate set. Crow search algorithm is used to extract data characteristics from the pre-processed data. Finally, an online training classifier is utilized to categorize the data and forecast the result. The proposed strategy greatly increases the system's illness prediction accuracy with less noise and more advanced regression approaches using existing medical datasets. Mathematical results depicts the effectiveness of the proposed approach in comparison with others.

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

Mamun

Alouani

2022

Lecture Notes in Networks and Systems

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LSTM-Based Cardiovascular Disease Detection Using ECG Signal

Cited by 8 publications

References 13 publications

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

Automated detection of coronary artery disease comparing arterial fat accumulation using CNN

A hybrid machine learning approach for heart disease prediction using hyper parameter optimization

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

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