OSA-weigher: an automated computational framework for identifying obstructive sleep apnea based on event phase segmentation

Liu, Fan; Zhou, Xingshe; Wang, Zhu; Ni, Hongbo; Wang, Tianben

doi:10.1007/s12652-018-0787-2

Cited by 10 publications

(6 citation statements)

References 39 publications

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“…Wavelet decomposition: Discrete wavelet transform (DWT) is a signal processing technique that is often employed to extract useful information from non-stationary time series, including physiological signals [14,15]. The utilization of DWT usually has a wonderful advantage that it can decompose the time series into multiple time-frequency resolutions, by which the details of the signal in time and frequency domains can be clearly displayed.…”

Section: Methodsmentioning

confidence: 99%

“…Since BCG signal contains information about the heartbeats, it has been widely employed in diagnosing varieties of cardiovascular diseases, having achieved great successes [12,13]. For example, Liu et al [12,14] put forward a novel obstructive sleep apnea detection method based on the segmentation of BCG signals. To solve the two problems of existing hypertension identification methods mentioned above, this paper proposes a novel BCG-based hypertension identification method, whose key idea is to integrate association rule mining and classification together, aiming to use the association relationship among features to model and identify hypertension pattern.…”

Section: Introductionmentioning

confidence: 99%

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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: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Definition 2. (The dictionary order of two k-itemSets): Given two k-itemSets l 1 and l 2 , they meet the dictionary order with l 1 preceding l 2 if (l 1 [1]…”

Section: Optimizedmentioning

confidence: 99%

“…As an important technique in data mining and pattern analysis, classification has been widely applied to varieties of practical scenarios such as disease prediction [1], anomaly detection [2], object recognition [3], etc. In past years, many kinds of classifiers were designed to solve different application problems.…”

Section: Introductionmentioning

confidence: 99%

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Gui

Liu

et al. 2023

Applied Sciences

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Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively.

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“…Sample Entropy (SampEn): SampEn is an effective metric to improve the approximate entropy method [69], and it characterizes the complexity and regularity of short-time series and has been widely used in bioinformatics [70]. Moreover, SampEn is not sensitive to the noise, making it appropriate for analyzing the EEG data [71].…”

Section: Eeg Sleep Related Featuresmentioning

confidence: 99%

Ischemic Stroke Prediction by Exploring Sleep Related Features

Xie

Wang

et al. 2021

Applied Sciences

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Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

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OSA-weigher: an automated computational framework for identifying obstructive sleep apnea based on event phase segmentation

Cited by 10 publications

References 39 publications

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

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

C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Ischemic Stroke Prediction by Exploring Sleep Related Features

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