Cinar, Beyza

Chronic diseases, especially diabetes, are burdens for the patient since lifelong management is required, and comorbidities can occur as a consequence of insufficient prevention. Hypoglycemia, a medical condition encountered by diabetic individuals, can result in severe symptoms if untreated, necessitating prompt preventive actions. Continuous health monitoring based on data collected with wearables can enable the early prediction of extreme blood glucose states. However, integrating and using data acquired from various sensors is challenging, especially when it comes to maintaining the quality and quantity of data due to inherent noise and missing values. To this end, the review discusses dataset constraints and highlights the temporal behaviour of prominent features in predicting hypoglycemia. It outlines a framework of preprocessing techniques that could be adopted to mitigate dataset limitations. A thorough analysis of the imputation procedures employed in the reviewed studies is conducted. In addition, machine learning imputation techniques applied in other healthcare domains are studied to investigate if they could be adopted to close the longer gaps of missing values in the datasets involved in the prediction of hypoglycemia. Based on a comprehensive evaluation of imputation techniques, a paradigm, Impute-Paradigm, is proposed and validated through a case study, enabling imputation tailored to variable duration time gaps. After analysing the reviewed studies, we recommend studying the rate of change of individual features and conclude that different time gaps of separate features should be treated with multiple imputation techniques.

Type 1 diabetes (T1D) is an autoimmune disorder that leads to the destruction of insulin-producing cells, resulting in insulin deficiency, as to why the affected individuals depend on external insulin injections. However, insulin can decrease blood glucose levels and can cause hypoglycemia. Hypoglycemia is a severe event of low blood glucose levels (≤70 mg/dL) with dangerous side effects of dizziness, coma, or death. Data analysis can significantly enhance diabetes care by identifying personal patterns and trends leading to adverse events. Especially, machine learning (ML) models can predict glucose levels and provide early alarms. However, diabetes and hypoglycemia research is limited by the unavailability of large datasets. Thus, this work systematically integrates 15 datasets to provide a large database of 2510 subjects with glucose measurements recorded every 5 minutes. In total, 149 million measurements are included, of which 4% represent values in the hypoglycemic range. Moreover, two sub-databases are extracted. Sub-database I includes demographics, and sub-database II includes heart rate data. The integrated dataset provides an equal distribution of sex and different age levels. As a further contribution, data quality is assessed, revealing that data imbalance and missing values present a significant challenge. Moreover, a correlation study on glucose levels and heart rate data is conducted, showing a relation between 15 and 55 minutes before hypoglycemia.

DiaData integrates 13 different datasets and presents a large continuous glucose monitoring (CGM) dataset comprising data from individuals with Type 1 Diabetes (T1D) across various age groups. The Maindatabase contains CGM measurements of all 1720 subjects. From this, two subsets are extracted: Subdatabase I includes CGM data and demographics of age and sex for 1306 subjects, while Subdatabase II includes CGM and heart rate data for a subset of 51 subjects.

Human-Computer Interaction (HCI) research increasingly focuses on developing systems that can recognize and respond to human stress, a key factor in preventing the negative health effects of prolonged stress exposure. Currently, progress in the domain of automated stress recognition based on multi-modal data shows clear potential but is especially hindered by the lack of available datasets and standardized protocols for data collection. Our research aims to contribute towards filling this gap by employing a framework for conducting experiments and data collection in the affective computing domain, supporting improved reuse and reproducibility of results. Specifically in our analysis, we apply a multi-modal approach integrating physiological signals to conduct and evaluate automated stress recognition. By employing standard classifiers, our study achieved notable results: in a ternary classification setting (distinguishing baseline, physical, and overall stress), we attained an accuracy of 79%, while a binary classification (baseline vs. stress) reached up to 89% accuracy. These findings not only replicate existing research in the stress detection domain but clearly show the advantage of using multi-modal data and also establish a benchmark for future analysis studies.

Type 1 diabetes (T1D) management can be significantly enhanced through the use of predictive machine learning (ML) algorithms, which can mitigate the risk of adverse events like hypoglycemia. Hypoglycemia, characterized by blood glucose levels below 70 mg/dL, is a life-threatening condition typically caused by excessive insulin administration, missed meals, or physical activity. Its asymptomatic nature impedes timely intervention, making ML models crucial for early detection. This study integrates short- (up to 2h) and long-term (up to 24h) prediction horizons (PHs) within a single classification model to enhance decision support. The predicted times are 5-15 min, 15-30 min, 30 min-1h, 1-2h, 2-4h, 4-8h, 8-12h, and 12-24h before hypoglycemia. In addition, a simplified model classifying up to 4h before hypoglycemia is compared. We trained ResNet and LSTM models on glucose levels, insulin doses, and acceleration data. The results demonstrate the superiority of the LSTM models when classifying nine classes. In particular, subject-specific models yielded better performance but achieved high recall only for classes 0, 1, and 2 with 98%, 72%, and 50%, respectively. A population-based six-class model improved the results with at least 60% of events detected. In contrast, longer PHs remain challenging with the current approach and may be considered with different models.

Hypoglycemia is a serious condition associated with increased mortality in patients with type 1 diabetes, which is an incurable autoimmune disease. Hypoglycemia is defined by blood glucose levels below 70 mg/dL. The causes can include excessive insulin injections, skipping meals, or increased physical activity. It can occur suddenly and may be asymptomatic, impeding timely preventive measures. Thus, innovative technologies, such as machine learning, can help to predict the state before it occurs. Prediction models are mainly classified as short- and long-term prediction horizons (PHs) of up to 2 hours and up to 24 hours, respectively. Most research conducted in the field of diabetes forecasts blood glucose values. Still, the obtained accuracy may not be su!cient to prevent hypoglycemia due to the possible time lag of CGM devices. Moreover, most studies focus on one PH only. This thesis included short- and long-term PHs in the same classification model to consider multiple use cases and to enable better decision support. The predicted times are 5-15 min, 15-30 min, 30 min-1 h, 1-2 h, 2-4 h, 4-8 h, 8-12 h, 12-24 h before hypoglycemia. The input features are prior glucose measurements, the administered basal and bolus insulin dosages, and acceleration data. First, a correlation analysis between the input features and the classes is conducted. Thereafter, RNN and CNN are explored to classify the onset of hypoglycemia based on the proposed nine classes. Furthermore, training with six classes classifying up to 4 hours before the onset is compared. Finally, subject-specific models are tested. The population-based correlation analysis reveals a very weak association between basal insulin and glucose, and between basal insulin and acceleration data. An individual correlation analysis showed stronger relationships, but the scores varied significantly among the subjects. For the classification model with nine classes, the best results are obtained with a LSTM model. Subject-specific models improve the performance. However, only classes 0-2 could be well classified with recalls of 98%, 72%, and 50%, respectively. A population-based model with only six classes obtains better results with recalls of 99%, 73%, and 56% for classes 0, 1, and 2, respectively. In conclusion, the proposed system that includes short- and long-term PHs is not feasible with the data or models used. Whereas, a model classifying multiple short-term horizons up to 4 hours before hypoglycemia produces promising results with improved precision, and F1-measure and indicates that at least 60% of events can be predicted which is increased to approximately 70% in subject 563.

Die automatische Erkennung von Emotionen mit Hilfe biologischer Signale ist ein sehr vielversprechendes Forschungsgebiet in den Gesundheitswissenschaften. Vor allem die fortschreitende Entwicklung von Wearables und Cloud-Computing ermöglicht eine kontinuierliche Erfassung der Daten und die Erkennung der Emotionen, welche helfen frühzeitige Diagnosen von psychologischen Erkrankungen wie Depression festzustellen. Ebenfalls können Therapiemethoden entsprechend dem psychologischen Wohlbefinden individuell angepasst/gestaltet werden. Gängige Sensordaten hierbei sind der Blutvolumenpuls, die Herzrate, die Herzraten-Varibilität, die Hautleitfähigkeit und die Hauttemperatur. Nach einer Filterung und (statistischen) Merkmalsextraktionen der Signale werden öfteres maschinelle Lernverfahren zur Klassifizierung benutzt (Support Vector Machines, K-Nearest-Neighbor, Random-Forest...). Neuerdings gibt es auch Forschungen für Deep-Learning Methoden wie Convolutional-Neural-Networks. Für die Emotionsklassifizierung gibt es zwei konkrete Emotionenmodelle, das diskrete, in welchem vorbestimmte Emotionen analysiert und das dimensionale, in welchem Emotionen als Kombination (Vektoren) aus mehreren Komponenten (Dimensionen) dargestellt werden. Hierbei ist das zwei dimensionale Model am gängigsten, in welchem eine Achse die Intensität und die andere die Polung der Emotion darstellt. In dieser Arbeit wurde mittels bereits durchgeführter Studien, welche das zwei dimensionale Model benutzt haben, unter Betrachtung der verfügbaren Sensoren analysiert, ob Wearables eine gute Basis für die Ermittlung von Emotionen darbieten. Die Analyse zeigt, dass Wearables vielversprechend sind und genaue Ergebnisse liefern können, jedoch müssen Daten sehr gut für die Klassifizierungsmethode vorbereitet werden. Zudem ist eine große Datenmenge und homogen verteilte Gruppe an Probanden notwendig. Es wird festgestellt, dass die Genauigkeit stark von den Probanden abhängt und Emotionen sehr subjektiv bewertet werden. Des weiteren scheint das vorgestellte zwei-dimensional Model nicht ausreichend zu sein und es wird eine Erweiterung vorgeschlagen, um bessere Grenzen zwischen ähnlichen Emotionen zu ziehen. Letztlich kann durch den Vergleich verschiedener Arbeiten angenommen werden, dass es nicht das genau Richtige oder die Beste Klassifizierungsmethode/Algorithmus gibt und für jede Datenmenge die beste Methode “erkundet” werden sollte.

Blood analysis is a major procedure to diagnose and monitor diseases, but conventional methods lead to delayed interventions. This work aims to analyze current advances in non-invasive blood analysis. The identified methods include optical sensors, image processing and biosensors. Image processing can be used for self-screening but is not reliable for clinical monitoring. Optical and biochemical sensors are promising and can be extended to more components of interest. More research in this area is expected in the future combining different components.

Patients with type 1 diabetes (T1D) have a higher risk of experiencing hypoglycemia, which is a severe condition of decreased blood glucose levels under 70 mg/dL and can result in coma, or death in the worst case. Prediction algorithms could improve diabetes care by enabling preventive actions, but research is limited by available multivariate datasets. Thus, this work investigates the feasibility of transfer learning between two different datasets of people with T1D and type 2 pre-diabetes using a 1 Dimensional Convolutional Neural Network (1DCNN) model. Moreover, different thresholds for defining hypoglycemia are compared for the pre-diabetes group. The results show that transfer learning could be feasible if the model is trained on T1D with a threshold of 70 mg/dL, while a threshold of 80–85 mg/dL achieved the best training performance for the pre-diabetes data.