Aleš Smrdel

Nutrition is an important factor in the recovery of critically ill patients and should be monitored to ensure optimal intake. Different types of applications can be used to assist in monitoring. The aim of this study was to evaluate the usefulness and implications on working practice of different types of applications for monitoring the nutrient intake of patients in hospitals. For this purpose, a desktop and a web application for nutrient intake monitoring were developed and deployed in a hospital at different points in time. The desktop application has been in use for a longer period, while the web application is still in the testing phase. A comparative observational study was conducted, in which ICU staff monitored nutrient intakeusing traditional non-assisted methods, the desktop application, and the web application. Task completion times, user satisfaction, and perceived effects on patient outcomes were recorded. Time-on-task data were collected by direct observation, while user feedback was obtained through structured surveys assessingacceptability, usability, and workflow integration. There were 8 respondents for the desktop and three for the web application. Both applications were well received by staff. The most important measurable result was a 25% reduction in task completion time with the desktop application compared to non-assisted monitoring, and an additional 6.6% reduction when using the web application compared to the desktopversion. Users reported no perceived impact on treatment outcomes. Both applications simplified nutrient intake management and proved useful in the ICU context, with the primary benefit being time efficiency rather than changes in patient recovery indicators.

Parkinson’s Disease (PD) is one of the most common diseases affecting the human brain, thus approaches are needed to help diagnose it. Since the changes caused by PD are visible in electroencephalograms (EEG), analysis of EEG represents one such approach. In this study, we used 25 EEG recordings of PD patients and 25 of healthy controls, subjected to auditory tasks, available in the Parkinson’s Oddball database. The mean age of the PD patients was 69.7 years (std. 8.7) and 69.3 years (std. 9.6) of the control subjects. We employed the Independent Component Analysis (ICA) method to characterize the PD and control EEG recordings, to represent the changes in habituation as a response to different auditory events via the ICA components in the form of topological distributions, and to classify the EEG recordings of the two groups. Characterization of the frontal and central electrodes of the topological distribution showed high separation power to differentiate EEG recordings of the PD patients and healthy subjects. The average classification results using 5-fold cross-validation over 50 trials and the first four features ranked according to the variance of the ICA components, while the features were logarithm of the variance of the ICA components, yielded the following performances: classification accuracy of 88.56%, sensitivity of 89.36%, and specificity of 87.76%. The use of the ICA method appears to be a promising approach for characterizing and classifying auditory EEG recordings.

One of the most common diseases that affects human brain is Parkinson’s disease. Detection of Parkinson’s disease (PD) poses a serious challenge. Robust methods for feature extraction allowing separation between the electroencephalograms (EEG) of healthy subjects and PD patients are required. We used the EEG records of healthy subjects and PD patients which were subject to auditory tasks. We used the common spatial patterns (CSP) and Laplacian mask as methods to allow robust selection and extraction of features. We used the derived CSP whitening matrix to determine those channels that are the most promising in the terms of differentiating between EEGs of healthy controls and of PD patients. Using the selection of features calculated using the CSP we managed to obtain the classification accuracy of 85% when classifying EEG records belonging to groups of controls or PD patients. Using the features calculated using the Laplacian operator we obtained the classification accuracy of 90%. Diagnosing the PD in early stages using EEG is possible. The CSP proved to be a promising technique to detect informative channels and to separate between the groups. Use of the combination of features calculated using the Laplacian offers good separability between the two groups.

Elevated transient ischemic ST segment episodes in the ambulatory electrocardiographic (AECG) records appear generally in patients with transmural ischemia (e. g. Prinzmetal's angina) while depressed ischemic episodes appear in patients with subendocardial ischemia (e. g. unstable or stable angina). Huge amount of AECG data necessitates automatic methods for analysis. We present an algorithm which determines type of transient ischemic episodes in the leads of records (elevations/depressions) and classifies AECG records according to type of ischemic heart disease (Prinzmetal's angina; coronary artery diseases excluding patients with Prinzmetal's angina; other heart diseases). The algorithm was developed using 24-hour AECG records of the Long Term ST Database (LTST DB). The algorithm robustly generates ST segment level function in each AECG lead of the records, and tracks time varying non-ischemic ST segment changes such as slow drifts and axis shifts to construct the ST segment reference function. The ST segment reference function is then subtracted from the ST segment level function to obtain the ST segment deviation function. Using the third statistical moment of the histogram of the ST segment deviation function, the algorithm determines deflections of leads according to type of ischemic episodes present (elevations, depressions), and then classifies records according to type of ischemic heart disease. Using 74 records of the LTST DB (containing elevated or depressed ischemic episodes, mixed ischemic episodes, or no episodes), the algorithm correctly determined deflections of the majority of the leads of the records and correctly classified majority of the records with Prinzmetal's angina into the Prinzmetal's angina category (7 out of 8); majority of the records with other coronary artery diseases into the coronary artery diseases excluding patients with Prinzmetal's angina category (47 out of 55); and correctly classified one out of 11 records with other heart diseases into the other heart diseases category. The developed algorithm is suitable for processing long AECG data, efficient, and correctly classified the majority of records of the LTST DB according to type of transient ischemic heart disease.