Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors

Al-Matouq, Ali; Laleg‐Kirati, Taous‐Meriem; Novara, Carlo; Rabbone, Ivana; Vincent, Tyrone L.

doi:10.1109/tcbb.2019.2905198

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Methods of sparse dictionary learning, which aim to obtain a combination of basic elements that represent the input data sparsely, have several applications in data decomposition, compressed sensing and signal recovery [42,43]. This approach has been applied to the fields of image denoising and classification, video and audio processing [44,45], as well as to medical signals anlaysis, as electroencephalography (EEG), ECG, magnetic resonance imaging (MRI), functional MRI, continuous glucose monitors [46], and ultrasound computer tomography, where different assumptions are used to analyze each signal. ECG signals can also be factorized in coefficients in order to get a dictionary and to use it to extract features to be used in ML-based beat classification schemes [47,48].…”

Section: Introductionmentioning

confidence: 99%

Differential Beat Accuracy for ECG Family Classification Using Machine Learning

et al. 2022

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Holter systems record the electrocardiogram (ECG), which is used to identify beat families according to their origin and severity. Many systems have been proposed using signal conditioning and machine learning (ML) classification algorithms for beat family recognition. However, the design stage of these systems does not always consider the impact that tuning the intermediate blocks has on the beat family classification and the overall accuracy. We propose to use a new index based on the confusion matrices and bootstrap resampling to summarize the global performance for all family beats, so-called differential beat accuracy (DBA), which is obtained as the total number of beats correctly classified in each class minus the total number of beats incorrectly classified. We addressed the sensitivity of the different subblocks when creating a simple beat family classifier consisting of signal preprocessing blocks and a simple k-Nearest Neighbors classifier. The MIT-BIH Arrhythmia database was used for this purpose, following existing literature on the field. We benchmarked two implementations, one for biclass classification (supraventricular vs. non-supraventricular origin) and another for multiclass beat labeling. The usual preprocessing stages were scrutinized with the DBA to evaluate their impact on the quality of the complete ML system, such as signal detrending and filtering, beat balancing, or inter-beat distance. With the support of the DBA, our methodology was able to detect significant differences in terms of some of the options in the algorithm design. For instance, balancing the number of beats in each class for training significantly improved the classification accuracy of the minority classes at 3.22% for the multiclass dataset but not for the biclass dataset. Also, accuracy improved significantly by about 6% for the biclass regrouping without data normalization, whereas overall accuracy improved significantly by about 7% for the multiclass regrouping with data normalization. In addition, the analysis of the statistical dispersion of confusion matrices showed that this database should be considered with caution when training ML-based family classifiers. We can conclude that the proposed DBA can provide us with statistically principled criteria for designing ML-based classifiers and reducing their bias in strongly unbalanced beat family datasets.

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Section: Introductionmentioning

confidence: 99%

Differential Beat Accuracy for ECG Family Classification Using Machine Learning

et al. 2022

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“…Effective management can help to maintain the blood glucose (BG) levels of people with diabetes within a reasonable range and then prevent diabetes complications [2][3][4]. The continuous glucose monitoring (CGM) device is an effective tool to continuously monitor BG levels of users [5,6]. Furthermore, a large amount of CGM data provided by the CGM device can enable data-driven models to predict future BG levels for users, then alert their hypoglycemic and hyperglycemic events, and help to control their BG levels within a reasonable range for decreasing the risk of developing diabetes complications.…”

Section: Introductionmentioning

confidence: 99%

A predictive model incorporating the change detection and Winsorization methods for alerting hypoglycemia and hyperglycemia

Xie

Yang

2021

Med Biol Eng Comput

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This paper focuses on establishing an effective predictive model to quickly and accurately alert hypoglycemia and hyperglycemia for helping control blood glucose levels of people with diabetes. In general, a good predictive model is established on the features of data. Inspired by this, we first analyze the characteristics of continuous glucose monitoring (CGM) data by the equality of variances test and outlier detection, which show time-varying fluctuations and jump points in CGM data. Therefore, we incorporate the change detection method and the Winsorization method into the predictive model based on the autoregressive moving average (ARMA) model and the recursive least squares (RLS) method to fit the above characteristics. To the best of our knowledge, the proposed method is the first attempt to give a solution for matching the time-varying fluctuations and jump points of CGM data simultaneously. A case study using CGM data is given to validate the effectiveness of the proposed method under 30-min-ahead prediction. The results show that the proposed method can improve the true alarm ratio of hypoglycemia and hyperglycemia from 0.7983 to 0.8783, and lengthen the average advance detection time of hypoglycemia and hyperglycemia from 19.77 to 22.64 min.

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