Using Korotkoff Sounds to Detect the Degree of Vascular Compliance in Different Age Groups

Ramakrishnan, Dushyant

doi:10.7860/jcdr/2016/16225.7198

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Each individual KS signal was decomposed by the WP, and each WP coe cient energy ratio was calculated by (7) and (8). Here, the energy ratio within 50-100 Hz is de ned as the ratio of low frequency energy (e n ); the energy ratio within 100-400 Hz is de ned as high frequency energy e variability of the KS energy ratio and time interval was calculated using ( 9)- (12), in order to study the influence of CHF on KS energy and the time interval.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Ormerod et al [ 11 ] found that QKD parameters were strongly associated with stroke caused by aortic sclerosis, while the relationships between cardiac embolism and small artery occlusion subtypes were not clear. Ramakrishnan [ 12 ] studied the effects of age and gender on the amplitude of KS, suggesting that the amplitude of KS could be used to predict atherosclerosis. These studies have shown that KS indirect features could be used to prove that there is a strong association between arteriosclerosis and cardiovascular disease.…”

Section: Introductionmentioning

confidence: 99%

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A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses

Zhang

Wang

Zhou

et al. 2022

Journal of Healthcare Engineering

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Background. Korotkoff sound (KS) is an important indicator of hypertension when monitoring blood pressure. However, its utility in noninvasive diagnosis of Chronic heart failure (CHF) has rarely been studied. Purpose. In this study, we proposed a method for signal denoising, segmentation, and feature extraction for KS, and a Bayesian optimization-based support vector machine algorithm for KS classification. Methods. The acquired KS signal was resampled and denoised to extract 19 energy features, 12 statistical features, 2 entropy features, and 13 Mel Frequency Cepstrum Coefficient (MFCCs) features. A controlled trial based on the VALSAVA maneuver was carried out to investigate the relationship between cardiac function and KS. To classify these feature sets, the K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), ensemble (EM) classifiers, and the proposed BO-SVM were employed and evaluated using the accuracy (Acc), sensitivity (Se), specificity (Sp), Precision (Ps), and F1 score (F1). Results. The ALSAVA maneuver indicated that the KS signal could play an important role in the diagnosis of CHF. Through comparative experiments, it was shown that the best performance of the classifier was obtained by BO-SVM, with Acc (85.0%), Se (85.3%), and Sp (84.6%). Conclusions. In this study, a method for noise reduction, segmentation, and classification of KS was established. In the measured data set, our method performed well in terms of classification accuracy, sensitivity, and specificity. In light of this, we believed that the methods described in this paper can be applied to the early, noninvasive detection of heart disease as well as a supplementary monitoring technique for the prognosis of patients with CHF.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses

Zhang

Wang

Zhou

et al. 2022

Journal of Healthcare Engineering

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“…In 2015, El Tahlawi et al showed that K-sounds can be used to predict the lesions associated with cardiac coronary arteries (58). In 2016, Ramakrishnan et al used K-sounds methods to assess vascular compliance in different age groups(59).The early stages of hypertension and coronary artery disease do not manifest any subjective clinical symptoms. However, at this time, normal body structures are altered, leading to spasms and contraction of small arteries, which is attributed to early hypertension, ischemia, and remodeling of the myocardium as a result of coronary artery sclerosis(4,60).…”

mentioning

confidence: 99%

Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods

Lin

Jia

Chen

et al. 2022

Front. Cardiovasc. Med.

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Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.

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Advancement on Automatic Blood Pressure Measurement Using Auscultatory Method: A Literature Review

Naufal

Rajab

Setiawan

2019

2019 International Conference on Electrical Engineering and Informatics (ICEEI)

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Using Korotkoff Sounds to Detect the Degree of Vascular Compliance in Different Age Groups

Cited by 4 publications

References 12 publications

A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses

A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses

Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods

Advancement on Automatic Blood Pressure Measurement Using Auscultatory Method: A Literature Review

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