Data mart based research in heart surgery

Arnrich B. Data mart based research in heart surgery. Bielefeld (Germany): Bielefeld University; 2006.The proposed data mart based information system has proven to be useful and effective in the particular application domain of clinical research in heart surgery. In contrast to common data warehouse systems who are focused primarily on administrative, managerial, and executive decision making, the primary objective of the designed and implemented data mart was to provide an ongoing, consolidated and stable research basis. Beside detail-oriented patient data also aggregated data are incorporated in order to fulfill multiple purposes. Due to the chosen concept, this technique integrates the current and historical data from all relevant data sources without imposing any considerable operational or liability contract risk for the existing hospital information systems (HIS). By this means the possible resistance of involved persons in charge can be minimized and the project specific goals effectively met. The challenges of isolated data sources, securing a high data quality, data with partial redundancy and consistency, valuable legacy data in special file formats, and privacy protection regulations are met with the proposed data mart architecture. The applicability was demonstrated in several fields, including (i) to permit easy comprehensive medical research, (ii) to assess preoperative risks of adverse surgical outcomes, (iii) to get insights into historical performance changes, (iv) to monitor surgical results, (v) to improve risk estimation, and (vi) to generate new knowledge from observational studies. The data mart approach allows to turn redundant data from the electronically available hospital data sources into valuable information. On the one hand, redundancies are used to detect inconsistencies within and across HIS. On the other hand, redundancies are used to derive attributes from several data sources which originally did not contain the desired semantic meaning. Appropriate verification tools help to inspect the extraction and transformation processes in order to ensure a high data quality. Based on the verification data stored during data mart assembly, various aspects on the basis of an individual case, a group, or a specific rule can be inspected. Invalid values or inconsistencies must be corrected in the primary source data bases by the health professionals. Due to all modifications are automatically transferred to the data mart system in a subsequent cycle, a consolidated and stable research data base is achieved throughout the system in a persistent manner. In the past, performing comprehensive observational studies at the Heart Institute Lahr had been extremely time consuming and therefore limited. Several attempts had already been conducted to extract and combine data from the electronically available data sources. Dependent on the desired scientific task, the processes to extract and connect the data were often rebuilt and modified. Consequently the semantics and the definitions of the research data changed from one study to the other. Additionally, it was very difficult to maintain an overview of all data variants and derived research data sets. With the implementation of the presented data mart system the most time and effort consuming process with conducting successful observational studies could be replaced and the research basis remains stable and leads to reliable results

DPD-fVAE: Synthetic Data Generation Using Federated Variational Autoencoders With Differentially-Private Decoder

Federated learning (FL) is getting increased attention for processing sensitive, distributed datasets common to domains such as healthcare. Instead of directly training classification models on these datasets, recent works have considered training data generators capable of synthesising a new dataset which is not protected by any privacy restrictions. Thus, the synthetic data can be made available to anyone, which enables further evaluation of machine learning architectures and research questions off-site. As an additional layer of privacy-preservation, differential privacy can be introduced into the training process. We propose DPD-fVAE, a federated Variational Autoencoder with Differentially-Private Decoder, to synthesise a new, labelled dataset for subsequent machine learning tasks. By synchronising only the decoder component with FL, we can reduce the privacy cost per epoch and thus enable better data generators. In our evaluation on MNIST, Fashion-MNIST and CelebA, we show the benefits of DPD-fVAE and report competitive performance to related work in terms of Fr\'echet Inception Distance and accuracy of classifiers trained on the synthesised dataset

Analyzing Population-Level Trials as N-of-1 Trials: an Application to Gait

Studying individual causal effects of health interventions is of interest whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. In this study, we propose to re-analyze existing population-level studies as N-of-1 trials as an alternative, and we use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. We first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian linear mixed models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses showed nuanced differences between participants. Baseline values of the gait parameters varied largely among all participants, and the changes induced by fatigue and cognitive task performance were also highly heterogeneous, with some individuals showing effects in opposite direction. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.Comment: 25 pages, 11 figures, including supplementary material

Monitoring of mental workload levels during an everyday life office-work scenario

Personal and ubiquitous healthcare applications offer new opportunities to prevent long-term health damage due to increased mental workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a quantification of mental load, different load levels that occur during a workday have to be discriminated. In this work, we present how mental workload levels in everyday life scenarios can be discriminated with data from a mobile ECG logger by incorporating individual calibration measures. We present an experiment design to induce three different levels of mental workload in calibration sessions and to monitor mental workload levels in everyday life scenarios of seven healthy male subjects. Besides the recording of ECG data, we collect subjective ratings of the perceived workload with the NASA Task Load Index (TLX), whereas objective measures are assessed by collecting salivary cortisol. According to the subjective ratings, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation can be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous system activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous system activity or predominance, show an increase in their values with increased workload. We employ multiple regression analysis to model the relationship between relevant HRV features and the subjective ratings of NASA-TLX in order to predict the mental workload levels during office-work. The resulting predictions were correct for six out of the seven subjects. In addition, we compare the performance of three classification methods to identify the mental workload level during office-work. The best results were obtained with linear discriminant analysis (LDA) that yielded a correct classification for six out of the seven subjects. The k-nearest neighbor algorithm (k-NN) and the support vector machine (SVM) resulted in a correct classification of the mental workload level during office-work for five out of the seven subject

A Real-time Human Pose Estimation Approach for Optimal Sensor Placement in Sensor-based Human Activity Recognition

Sensor-based Human Activity Recognition facilitates unobtrusive monitoring of human movements. However, determining the most effective sensor placement for optimal classification performance remains challenging. This paper introduces a novel methodology to resolve this issue, using real-time 2D pose estimations derived from video recordings of target activities. The derived skeleton data provides a unique strategy for identifying the optimal sensor location. We validate our approach through a feasibility study, applying inertial sensors to monitor 13 different activities across ten subjects. Our findings indicate that the vision-based method for sensor placement offers comparable results to the conventional deep learning approach, demonstrating its efficacy. This research significantly advances the field of Human Activity Recognition by providing a lightweight, on-device solution for determining the optimal sensor placement, thereby enhancing data anonymization and supporting a multimodal classification approach

Towards long term monitoring of electrodermal activity in daily life

Manic depression, also known as bipolar disorder, is a common and severe form of mental disorder. The European research project MONARCA aims at developing and validating mobile technologies for multi-parametric, long term monitoring of physiological and behavioral information relevant to bipolar disorder. One aspect of MONARCA is to investigate the long term monitoring of Electrodermal activity (EDA) to support the diagnosis and treatment of bipolar disorder patients. EDA is known as an indicator of the emotional state and the stress level of a person. To realize a long-term monitoring of the EDA, the integration of the sensor system in the shoe or sock is a promising approach. This paper presents a first step towards such a sensor system. In a feasibility study including 8 subjects, we investigate the correlation between EDA measurements at the fingers, which is the most established sensing site, with measurements of the EDA at the feet. The results indicate that 88% of the evoked skin conductance responses (SCRs) occur at both sensing sites. When using an action movie as psychophysiologically activating stimulus, we have found weaker reactivity in the foot than in the hand EDA. The results also suggest that the influence of moderate physical activity on EDA measurements is low and has a similar effect for both recording sites. This suggests that the foot recording location is suitable for recordings in daily life even in the presence of moderate movemen

Voice analysis for neurological disorder recognition – a systematic review and perspective on emerging trends

Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance

Unobtrusive physiological monitoring in an airplane seat

Air travel has become the preferred mode of long-distance transportation for most of the world's travelers. People of every age group and health status are traveling by airplane and thus the airplane has become part of our environment, in which people with health-related limitations need assistive support. Since the main interaction point between a passenger and the airplane is the seat, this work presents a smart airplane seat for measuring health-related signals of a passenger. We describe the design, implementation and testing of a multimodal sensor system integrated into the seat. The presented system is able to measure physiological signals, such as electrocardiogram, electrodermal activity, skin temperature, and respiration. We show how the design of the smart seat system is influenced by the trade-off between comfort and signal quality, i.e. incorporating unobtrusive sensors and dealing with erroneous signals. Artifact detection through sensor fusion is presented and the working principle is shown with a feasibility study, in which normal passenger activities were performed. Based on the presented method, we are able to identify signal regions in which the accuracies for detecting the heart- and respiration-rate are 88 and 82%, respectively, compared to 40 and 76% without any artifact remova