A Study on Clinical Variables Contributing to Differentiation of Delirium and Non-Delirium Patients in the ICU / 정신신체의학

OBJECTIVES@#It is not clear which clinical variables are most closely associated with delirium in the Intensive Care Unit (ICU). By comparing clinical data of ICU delirium and non-delirium patients, we sought to identify variables that most effectively differentiate delirium from non-delirium.@*METHODS@#Medical records of 6,386 ICU patients were reviewed. Random Subset Feature Selection and Principal Component Analysis were utilized to select a set of clinical variables with the highest discriminatory capacity. Statistical analyses were employed to determine the separation capacity of two models-one using just the selected few clinical variables and the other using all clinical variables associated with delirium.@*RESULTS@#There was a significant difference between delirium and non-delirium individuals across 32 clinical variables. Richmond Agitation Sedation Scale (RASS), urinary catheterization, vascular catheterization, Hamilton Anxiety Rating Scale (HAM-A), Blood urea nitrogen, and Acute Physiology and Chronic Health Examination II most effectively differentiated delirium from non-delirium. Multivariable logistic regression analysis showed that, with the exception of vascular catheterization, these clinical variables were independent risk factors associated with delirium. Separation capacity of the logistic regression model using just 6 clinical variables was measured with Receiver Operating Characteristic curve, with Area Under the Curve (AUC) of 0.818. Same analyses were performed using all 32 clinical variables;the AUC was 0.881, denoting a very high separation capacity.@*CONCLUSIONS@#The six aforementioned variables most effectively separate delirium from non-delirium. This highlights the importance of close monitoring of patients who received invasive medical procedures and were rated with very low RASS and HAM-A scores.

Optimized automatic sleep stage classification using the normalized mutual information feature selection (NMIFS) method.

Sleep is a very important physiological phenomenon for recovery of physical and mental fatigue. Recently, there has been a lot of interest in the quality of sleep and the research is actively under way. In particular, it is important to have a repetitive and regular sleep cycle for good sleep. However, it takes a lot of time to determine sleep stages using physiological signals by experts. In this study, we constructed an optimized classifier based on normalized mutual information feature selection (NMIFS) and kernel based extreme learning machine (K-ELM), and total 4 sleep stages (Awake, weak sleep (stage1+stage2), deep sleep(stage3+stage4) and rapid eye movement (REM)) were automatically classified. As a results, the average of the accuracy obtained by proposed method (NMIFS+K-ELM) is 2~3% higher than that of simple method (K-ELM).

Changes in heart rate variability of patients with delirium in intensive care unit.

Delirium is an important syndrome in intensive care unit (ICU) patients, however, its characteristics are still unclear. Many evidences showed that this syndrome can be related to the autonomic instability. In this study, we aimed to investigate the possible alterations of autonomic nervous system (ANS) in delirium patients in ICU. Electrocardiography (ECG) of every ICU patient was measured during routine daily ICU care, and the data were gathered to evaluate the heart rate variability (HRV). HRV of total 60 patients were analyzed in time, frequency and non-linear domains. As a result, we found that heart rates of delirium patients were more variable and irregular than non-delirium patients. These findings may facilitate early detection and prevention of delirium in ICU.

Discrimination of multiple stress levels in virtual reality environments using heart rate variability.

People are suffering from various stress during daily living. Stress can cause a variety of symptoms, and in severe cases, it can lead to a dangerous disease. For this reason, it is essential to develop a simple method to evaluate stress level precisely. Popularly, heart rate variability (HRV) is used because it can reflect autonomic nervous system (ANS) activity. On the other hand, virtual reality (VR), which can provide environments similar to reality, is widely used in laboratory-based experiments. In this paper, we analyzed the HRV of healthy people by using the photoplethysmogram (PPG) while providing diverse stress situations. To detect and classify the exact stress levels, extracted HRV features and linear discriminant analysis (LDA) were utilized. As a result, high multi-class classification accuracy was obtained: Baseline (74%), mild stress (81%), and severe stress (82%).

A Preliminary Study on the Circadian Rhythm of Autonomic Nervous System in Patients Admitted to Intensive Care Unit, Using the Heart Rate Variability

OBJECTIVES: normal circadian rhythm of autonomic nervous system function stands for the daily change of sympathetic and parasympathetic modulation, which can be measured by heart rate variability (HRV). Generally, patients in the intensive care unit (ICU) are prone to sleep-wake cycle dysregulation, therefore, it may have an influence on the circadian rhythm of autonomic nervous system. This study was designed to interpret possible dysregulation of autonomic nervous system in ICU patients by using HRV. METHODS: HRV was assessed every 3 hours in 21 ICU patients during a 7-minute period. The statistical differences of HRV features between the morning (AM 6 : 00–PM 12 : 00), and the afternoon (PM 12 : 00–PM 18 : 00) periods were evaluated in time domain and frequency domain. RESULTS: Patients showed significantly increased normalized power of low frequencey (nLF), absolute power of low frequencey (LF)/absolute power of high frequencey (HF) in the afternoon period as compared to the morning period. However, normalized power of high frequency (nHF) was significantly decreased in the afternoon period. There was no statistically significant difference between the morning period and the afternoon period in the time domain analysis. CONCLUSIONS: The increased sympathetic tone in the afternoon period supports possible dysregulation in the circadian rhythm of autonomic nervous system in ICU patients. Future studies can help to interpret the association between autonomic dysregulation and negative outcomes of ICU patients.

Non-contact robust heart rate estimation using HSV color model and matrix-based IIR filter in the face video imaging.

The heart rate (HR) is one of the important indicators for observing the patient condition. Therefore, many estimation techniques for acquiring heart rate have been developed. The photoplethysmography (PPG) and electrocardiography (ECG) are the common measurement techniques for estimating the heart rate. However, they should contact on the human skin in order to estimate the accurate heart rate. In this paper, we propose a non-contact robust heart rate measurement method using a webcam device. In addition, we evaluated the performance of the proposed algorithm in comparison with other algorithms.