AFC-ECG: An Adaptive Fuzzy ECG Classifier

Lei, Wai Kei; Li, Bing Nan; Ming, Dong; Vai, Mang I

doi:10.1007/978-3-540-70706-6_18

Cited by 24 publications

(10 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That study explored the possibility of combining MSE and PWV in assessing sugar control and progression of vascular pathology in diabetic patients and elderly to allow timely therapeutic intervention. Other than MSE, various tools for non-linear data analysis are available, including Ensemble Empirical Mode Decomposition (EEMD) [17,18], linguistic [19,20] and fractal [21,22] analyses.…”

Section: Discussionmentioning

confidence: 99%

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

Chang

Chen

et al. 2014

Entropy

View full text Add to dashboard Cite

Using 1000 successive points of a pulse wave velocity (PWV) series, we previously distinguished healthy from diabetic subjects with multi-scale entropy (MSE) using a scale factor of 10. One major limitation is the long time for data acquisition (i.e., 20 min). This study aimed at validating the sensitivity of a novel method, short time MSE (sMSE) that utilized a substantially smaller sample size (i.e., 600 consecutive points), in differentiating the complexity of PWV signals both in simulation and in human subjects that were divided into four groups: healthy young (Group 1; n = 24) and middle-aged (Group 2; OPEN ACCESSEntropy 2014, 16 4033 n = 30) subjects without known cardiovascular disease and middle-aged individuals with well-controlled (Group 3; n = 18) and poorly-controlled (Group 4; n = 22) diabetes mellitus type 2. The results demonstrated that although conventional MSE could differentiate the subjects using 1000 consecutive PWV series points, sensitivity was lost using only 600 points. Simulation study revealed consistent results. By contrast, the novel sMSE method produced significant differences in entropy in both simulation and testing subjects.In conclusion, this study demonstrated that using a novel sMSE approach for PWV analysis, the time for data acquisition can be substantially reduced to that required for 600 cardiac cycles (~10 min) with remarkable preservation of sensitivity in differentiating among healthy, aged, and diabetic populations.

show abstract

Section: Discussionmentioning

confidence: 99%

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

Chang

Chen

et al. 2014

Entropy

View full text Add to dashboard Cite

show abstract

“…FCM is one of the most popular algorithms in fuzzy clustering that permits the member of one fuzzy set being the members of other fuzzy sets of the same image and widely applied to medical mages [8,9]. It utilizes a membership function mn  to indicate the degree of membership of the n-th object to the mth cluster in the range of (0,1).…”

Section: Proposed Methodsmentioning

confidence: 99%

Spatial Fuzzy C-Means Clustering based Segmentation of Tumor in Vertebral Column Images

Asanambigai¹,

Sasikala²

2016

IJCA

View full text Add to dashboard Cite

Precise detection and segmentation of tumor that represents uncontrolled growth of tissue, in vertebral column is complex due to vertebrae shapes, gaps in the cortical bone, internal boundaries, as well as the noisy, incomplete or missing information from the MRI or CT scan images, and becomes a challenging task. This paper presents an elegant method using FCM clustering with spatial information for segmenting tumor region of such images. It segments the cluster image corresponding to the largest centroid and uses a few of the morphological operations for removing unwanted regions. It includes the results of three test images for illustrating the goodness of the proposed method. General Terms image processing, segmentationKeywords fuzzy c-means clustering, image segmentation.

show abstract

“…ECG is widely used in the diagnosis of heart diseases, where fuzzy inference systems can be used for effective system analysis (Goel et al, 2016). The research on fuzzy based techniques have further developed through the incorporation of neural networks, such as fuzzy inference networks (Lei et al, 2007), that integrates the benefits of neural networks for better learning and fuzzy sets for better human understanding (Guler & Ubeyli, 2004). In a similar manner, Ceylan et al…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on various Machine Learning Approaches for ECG Analysis

Roopa¹,

Harish²

2017

IJCA

View full text Add to dashboard Cite

Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing most of the cardiac disease. The main objective of this paper is to review the various machine learning approaches for diagnosing Myocardial Infarction (heart attack), differentiate Arrhythmias (heart beat variation), Hypertrophy (increase thickness of the heart muscle) and Enlargement of Heart. Further, we also present various machine learning approaches and compare different methods and results used to analyze the ECG. The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.

show abstract

AFC-ECG: An Adaptive Fuzzy ECG Classifier

Cited by 24 publications

References 5 publications

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

Spatial Fuzzy C-Means Clustering based Segmentation of Tumor in Vertebral Column Images

A Survey on various Machine Learning Approaches for ECG Analysis

Contact Info

Product

Resources

About