A comparative stationarity analysis of EEG signals

Launonen²,

Årsand³

et al. 2020

J Med Internet Res

Background Type 1 diabetes is a chronic condition of blood glucose metabolic disorder caused by a lack of insulin secretion from pancreas cells. In people with type 1 diabetes, hyperglycemia often occurs upon infection incidences. Despite the fact that patients increasingly gather data about themselves, there are no solid findings that uncover the effect of infection incidences on key parameters of blood glucose dynamics to support the effort toward developing a digital infectious disease detection system. Objective The study aims to retrospectively analyze the effect of infection incidence and pinpoint optimal parameters that can effectively be used as input variables for developing an infection detection algorithm and to provide a general framework regarding how a digital infectious disease detection system can be designed and developed using self-recorded data from people with type 1 diabetes as a secondary source of information. Methods We retrospectively analyzed high precision self-recorded data of 10 patient-years captured within the longitudinal records of three people with type 1 diabetes. Obtaining such a rich and large data set from a large number of participants is extremely expensive and difficult to acquire, if not impossible. The data set incorporates blood glucose, insulin, carbohydrate, and self-reported events of infections. We investigated the temporal evolution and probability distribution of the key blood glucose parameters within a specified timeframe (weekly, daily, and hourly). Results Our analysis demonstrated that upon infection incidence, there is a dramatic shift in the operating point of the individual blood glucose dynamics in all the timeframes (weekly, daily, and hourly), which clearly violates the usual norm of blood glucose dynamics. During regular or normal situations, higher insulin and reduced carbohydrate intake usually results in lower blood glucose levels. However, in all infection cases as opposed to the regular or normal days, blood glucose levels were elevated for a prolonged period despite higher insulin and reduced carbohydrates intake. For instance, compared with the preinfection and postinfection weeks, on average, blood glucose levels were elevated by 6.1% and 16%, insulin (bolus) was increased by 42% and 39.3%, and carbohydrate consumption was reduced by 19% and 28.1%, respectively. Conclusions We presented the effect of infection incidence on key parameters of blood glucose dynamics along with the necessary framework to exploit the information for realizing a digital infectious disease detection system. The results demonstrated that compared with regular or normal days, infection incidence substantially alters the norm of blood glucose dynamics, which are quite significant changes that could possibly be detected through personalized modeling, for example, prediction models and anomaly detection algorithms. Generally, we foresee that these findings can benefit the efforts toward building next generation digital infectious disease detection systems and provoke further thoughts in this challenging field.

Section: Methodsmentioning

confidence: 99%

Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System

Launonen²,

Årsand³

et al. 2020

J Med Internet Res

“…Exemplar data depicting the model’s input features for 2 specific patient years with and without infection are shown in Figures 1 - 4 , and a more detailed description of the input features for 10-patient years with and without infection incidences can be found in Multimedia Appendix 2 [ 12 , 19 ]. The data were resampled and imputed in accordance with the description provided by Woldaregay et al [ 19 ], and the preprocessed data were smoothed using a moving average filter of 2 days’ (48 hours) window size to remove short-term and small-scale features [ 19 , 40 , 41 ]. Feature scaling was carried out using min-max scaling [ 42 ] to normalize the data between 0 and 1, which is important to ensure that larger parameters do not dominate the smaller ones.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism

Launonen²,

Albers³

et al. 2020

J Med Internet Res

Background Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model. Objective This study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio. Methods Three groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required. Results The one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time. Conclusions We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.

“…Exemplar data depicting the model's input features for 2 specific patient years with and without infection are shown in Figures 1-4, and a more detailed description of the input features for 10-patient years with and without infection incidences can be found in Multimedia Appendix 2 [12,19]. The data were resampled and imputed in accordance with the description provided by Woldaregay et al [19], and the preprocessed data were smoothed using a moving average filter of 2 days' (48 hours) window size to remove short-term and small-scale features [19,40,41]. Feature scaling was carried out using min-max scaling [42] to normalize the data between 0 and 1, which is important to ensure that larger parameters do not dominate the smaller ones.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism (Preprint)

Launonen²,

Albers³

et al. 2020

Preprint

BACKGROUND Infections incidence in people with type 1 diabetes often makes self-management problematic, i.e. difficulties in controlling blood glucose (BG) levels. During the course of infections, the body demands more energy in order to supply the active tissues in the immune response. Thus, alteration in carbohydrate metabolism is expected to keep up the body’s demand by enhancing glucose uptake and utilization, increasing glucose production, increasing insulin resistance and others. Consequently, despite consuming regular meals, any ingested carbohydrate might cause significant increase in BG levels and often takes longer time to settle down as compared to the regular/normal day. It is common to observe prolonged hyperglycemia episodes, and frequent insulin injections. Patients have to struggle with enhanced and frequent insulin injections so as to lower the abnormal BG episode. This kind of event (BG anomalies) presents an enormous opportunity for automatically detecting infection incidence using self-recorded data, and thereby detecting infectious disease outbreak if properly detected with a dedicated algorithm. Moreover, it can also enable to provide a personalized decision support and learning platform for individuals, family and caregivers. During the course of infection, information regarding BG evolution, alterations in insulin sensitivity, shift incurred in ratio of insulin to carbohydrate, which is a change in amount of insulin needed for every gram of carbohydrate consumed, could be vital. Despite these potential, there has been very limited study that focused on detecting infection incidences in an individual with type 1 diabetes using a dedicated personalized algorithm. OBJECTIVE The study aims to develop an algorithm, i.e. a personalized health model, which can automatically detect the incidence of infection in people with type 1 diabetes using self-recorded BG levels, diet intake (carbohydrate in grams) and insulin information as indicator variables. The model is expected to detect deviations from the norm due to infections incidences considering elevated BG level (hyperglycemia incidences), coupled with unusual change in insulin to carbohydrate ratio (frequent insulin injections and unusual reduction in carbohydrate intakes). METHODS Method: Semi-supervised models, i.e. one-class classifiers, were trained and tested to detect incidence of infection in people with type 1 diabetes. Three group of one-class classifiers were trained on regular/normal day measurements (target datasets) and tested on dataset containing both the target (regular days) and non-target (infection days); boundary and domain-based, density-based, and reconstruction-based method. The boundary and domain-based method includes one-class support vector machine (v-SVM), minimum spanning tree (MST), support vector data description (SVDD), nearest neighbor (NN), and incremental svm (incSVM). Density-based method includes Parzen, Naïve Parzen, normal Gaussian, mixture of Gaussian (MOG), minimum covariance Gaussian (MCG), k-nearest neighbor (KNN), and local outlier factor (LOF). The reconstruction-based method includes Auto-encoder network, self-organizing map (SOM), K-means, and principal component analysis (PCA). For comparison purposes, two unsupervised models were also tested; local outlier factor (LOF) and connectivity-based outlier factor (COF). The one-class classifiers were evaluated based on twenty times 5-fold stratified cross validation. Area under the ROC curve (AUC), sensitivity, and F1-score were taken into consideration for measuring the models performance. The models were compared on two groups of data; raw data and filtered data (with a moving average filter of 2-days). Generally, the models were compared based on their detection performance, complexity, computational time, and number of samples required. Materials: A high precision self-recorded data of ten patient years collected from 3 real subjects (2 males and 1 females with average age of 34 (13.2) years) with type 1 diabetes were used. The datasets consist of BG measurement and continuous glucose monitor (CGM), injected insulin (basal and bolus), diet (carbohydrate in grams), and self-reported events of acute infection. It is costly and time consuming to collect such a rich and large dataset from a lot of participants, if not impossible. The patients have used different diabetes self-management technologies to gather these datasets including Diabetes Diary, Spike, Dexcom CGM, insulin Pens and pumps. The datasets are consisted of regular/normal years without infection incidences and years with at least one or more acute infection incidences. The regular/normal patient years are used, as baseline data, to compare the effect of all patient controllable parameters and patient uncontrollable parameters during the incidence of infection. The self-reported incidences of acute infections are a case of influenza (flu), and mild and light common cold without fever. All the experiments were conducted using MATLAB® 2018b (Mathworks, Inc, Natwick, MA). RESULTS The analysis of self-recorded data of ten patient years reveals that BG levels and insulin to carbohydrate ratio are highly affected by the incidence of infection as compared to the regular/normal days. Semi-supervised and unsupervised models trained and tested using bivariate input, BG levels and insulin to carbohydrate ratio, achieved an excellent performance in describing the dataset, i.e. detecting the infection days from the regular/normal days. However, the unsupervised methods suffer in performance degradation as compared to the one-class classifier mainly because of the atypical nature of the data, not distributed uniformly, where some regions contain high density and other are sparse. In regard to the one-class classifiers, the boundary and domain-based method produced better description of the data as compared to the density and reconstruction-based methods mainly because of the atypicality of the data. Regarding the computational time, NN, SVDD, and SOM took considerable training time, which typically grows as the samples size increases. As for the models testing time, only LOF and COF took considerable time. CONCLUSIONS We demonstrated the applicability of semi-supervised and unsupervised models for the detection of infection incidences in people with type 1 diabetes. Detecting the incidence of infection in these patient group can provide an opportunity to devise tailored services, i.e. a personalized decision support and a learning platform for the individuals, and simultaneously can be used for detecting potential public health threats, i.e. infectious disease outbreak, on a large scale through a spatio-temporal cluster detection. In general, the proposed approaches achieved excellent performance, and in particular the boundary and domain-based method performed better. In contrast to the particular models, v-SVM, K-NN, and K-means achieved better performance in all the infection cases. Altogether, we foresee that the presented result could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, e.g. various CGM feature and physical activity data, on a large scale basis.