A non-invasive technique of early heart diseases prediction from photoplethysmography signal

Islam, Mohammad Mahfuzul; Ashikuzzaman, Md.; Tabassum, Tahmida; Yusuf, Md. Salah Uddin

doi:10.1109/eict.2017.8275222

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…In Myint, Lim, Wong, Gopalai, and Oo (2014), a non-invasive blood pressure (BP) monitoring system is proposed using pulse wave velocity (PWV) and pulse transit time (PTT) measured from PPG signals acquired from finger and wrist. In Islam, Ashikuzzaman, Tabassum, and Yusuf (2017), a non-invasive technique is proposed for heart disease estimation that extracts PPG from human facial video. Heart rate is estimated through the fast Fourier transform (FFT) analysis.…”

Section: Related Workmentioning

confidence: 99%

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

et al. 2021

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Heart diseases are a major cause of human casualties each year. An accurate and efficient diagnosis is essential to minimize their risk. This paper presents a system for the classification of multiple cardiac disorders based on pulse plethysmographic (PuPG) signal analysis. In particular, the work focuses on the detection and classification of ischemic and rheumatic heart diseases using proposed 1D local ternary patterns of PuPG signals. The proposed methodology is applied on a self‐collected dataset consisting of 250 PuPG signals from 50 normal/healthy subjects, 140 signals from 28 ischemic patients, and 180 signals from 36 rheumatic patients. The effectiveness of proposed feature descriptors to precisely represent different classes of data is verified by several classifiers namely K‐nearest neighbours (KNN), support vector machines (SVM), and decision tree (DT) with 10‐fold cross‐validation. The proposed methodology achieves the best detection performance with 99% accuracy, 100% sensitivity, and 98% specificity using an SVM classifier with cubic kernel. The comparative analysis demonstrates that the proposed method is more accurate and reliable as compared to several existing works on cardiac disorders classification.

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Section: Related Workmentioning

confidence: 99%

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

et al. 2021

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“…In 2017, Islam et al [24] suggested that the hazardous position of heart diseases worldwide. So, they have suggested earlier detection can help.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient SKNN based approach for heart disease classification

Ansari¹,

Namdeo²

2019

IJATEE

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An efficient span-k-nearest neighbour (SKNN) algorithm has been proposed. It is used for the categorization of heart disease. The objective is to differentiate the data and find the accuracy of detection. The pre-processing is done based on the three attributes combination that is two, three and four with the help of KNN method. Then it is categorized based on five different spans that are 100, 125, 150, 200 and 250. The proposed work is compared with different factors of SKNNso that the proper capability can be explored. The obtained results show that the proposed approach has the significant capability of better classification.

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The Analysis of Machine Learning Techniques for Heart Disease Prediction

Singh¹,

Shrivastava²

2020

Algorithms for Intelligent Systems

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A non-invasive technique of early heart diseases prediction from photoplethysmography signal

Cited by 6 publications

References 8 publications

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

Classification of cardiac disorders using 1D local ternary patterns based on pulse plethysmograph signals

An efficient SKNN based approach for heart disease classification

The Analysis of Machine Learning Techniques for Heart Disease Prediction

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