D-ECG: A Dynamic Framework for Cardiac Arrhythmia Detection from IoT-Based ECGs

He, Jing; Rong, Jia; Sun, Le; Wang, Hua; Zhang, Yanchun; Ma, Jiangang

doi:10.1007/978-3-030-02925-8_6

Cited by 15 publications

(5 citation statements)

References 53 publications

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“…In the future, we will focus on building a classifier that needs fewer CARs but achieves higher identification performance. In addition, we will also include other physiological signals especially the PPG [21], the ECG [60,61,62,63], and the EEG [64], in order to get a better understanding of the patient’s condition.…”

Section: Discussionmentioning

confidence: 99%

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Liu

Zhou

Wang

et al. 2019

Sensors

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Hypertension is one of the most common cardiovascular diseases, which will cause severe complications if not treated in a timely way. Early and accurate identification of hypertension is essential to prevent the condition from deteriorating further. As a kind of complex physiological state, hypertension is hard to characterize accurately. However, most existing hypertension identification methods usually extract features only from limited aspects such as the time-frequency domain or non-linear domain. It is difficult for them to characterize hypertension patterns comprehensively, which results in limited identification performance. Furthermore, existing methods can only determine whether the subjects suffer from hypertension, but they cannot give additional useful information about the patients’ condition. For example, their classification results cannot explain why the subjects are hypertensive, which is not conducive to further analyzing the patient’s condition. To this end, this paper proposes a novel hypertension identification method by integrating classification and association rule mining. Its core idea is to exploit the association relationship among multi-dimension features to distinguish hypertensive patients from normotensive subjects. In particular, the proposed method can not only identify hypertension accurately, but also generate a set of class association rules (CARs). The CARs are proved to be able to reflect the subject’s physiological status. Experimental results based on a real dataset indicate that the proposed method outperforms two state-of-the-art methods and three common classifiers, and achieves 84.4%, 82.5% and 85.3% in terms of accuracy, precision and recall, respectively.

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Section: Discussionmentioning

confidence: 99%

Unobtrusive Mattress-Based Identification of Hypertension by Integrating Classification and Association Rule Mining

Liu

Zhou

Wang

et al. 2019

Sensors

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“…Although both accuracy and precision depict the closeness of measurement to an actual value, precision reflects the reproducible measurements even if they are far from accepted value. The metrics Se and Sp are derived respectively from the proportion of positive and negative pixels in the ground truth image that are truly identified [68], [69]. A result with high sensitivity and specificity are considered as an accurate segmentation.…”

Section: Resultsmentioning

confidence: 99%

Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs

Pandey

Yin

Wang

et al. 2018

Heliyon

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Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.

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“…Currently, prevalent DES techniques that can be used in this stage include DES-KL [45], DES-KNN [41], KNORA-E [27], KNORA-U [27], KNOP [9], DES-P [45], DES-RRC [44], and META-DES [12]. Extensive experiments have been done to evaluate these DES techniques in our previous work [21]. The results showed that there is no significant difference of their performances.…”

Section: Skewnessmentioning

confidence: 99%