Analysis of Patient Alarms in Adult Intensive Care Units

...Our study aims were pretty straight-forward. We concentrated quite a bit on arrhythmia alarms, which is a little different than the parameter alarms we\u27ve been talking about so far today. We decided we were going to assess the alarm prevalence of patient\u27s physiological monitor alarms. We\u27ll identify the alarm burden, analyze a select high priority number of arrhythmia alarms and determine patient characteristics that may be associated with the frequent alarms

Evaluation of patient electrocardiogram datasets using signal quality indexing

Electrocardiogram (ECG) is widely used in the hospital emergency rooms for detecting vital signs, such as heart rate variability and respiratory rate. However, the quality of the ECGs is inconsistent. ECG signals lose information because of noise resulting from motion artifacts. To obtain an accurate information from ECG, signal quality indexing (SQI) is used where acceptable thresholds are set in order to select or eliminate the signals for the subsequent information extraction process. A good evaluation of SQI depends on the R-peak detection quality. Nevertheless, most R-peak detectors in the literature are prone to noise. This paper assessed and compared five peak detectors from different resources. The two best peak detectors were further tested using MIT-BIH arrhythmia database and then used for SQI evaluation. These peak detectors robustly detected the R-peak for signals that include noise. Finally, the overall SQI of three patient datasets, namely, Fantasia, CapnoBase, and MIMIC-II, was conducted by providing the interquartile range (IQR) and median SQI of the signals as the outputs. The evaluation results revealed that the R-peak detectors developed by Clifford and Behar showed accuracies of 98% and 97%, respectively. By introducing SQI and choosing only high-quality ECG signals, more accurate vital sign information will be achieved

Development of Respiratory Rate Estimation Technique Using Electrocardiogram and Photoplethysmogram for Continuous Health Monitoring

Abnormal vital signs often predict a serious condition of acutely ill hospital patients in 24 hours. The notable fluctuations of respiratory rate (RR) are highly predictive of deteriorations among the vital signs measured. Traditional methods of detecting RR are performed by directly measuring the air flow in or out of the lungs or indirectly measuring the changes of the chest volume. These methods require the use of cumbersome devices, which may interfere with natural breathing, are uncomfortable, have frequently moving artifacts, and are extremely expensive. This study aims to estimate the RR from electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which consist of passive and non-invasive acquisition modules. Algorithms have been validated by using PhysioNet’s Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II)’s patient datasets. RR estimation provides the value of mean absolute error (MAE) for ECG as 1.25 bpm (MIMIC-II) and 1.05 bpm for the acquired data. MAE for PPG is 1.15 bpm (MIMIC-II) and 0.90 bpm for the acquired data. By using 1-minute windows, this method reveals that the filtering method efficiently extracted respiratory information from the ECG and PPG signals. Smaller MAE for PPG signals results from fewer artifacts due to easy sensor attachment for the PPG because PPG recording requires only one-finger pulse oximeter sensor placement. However, ECG recording requires at least three electrode placements at three positions on the subject’s body surface for a single lead (lead II), thereby increasing the artifacts. A reliable technique has been proposed for RR estimation

Signal quality assessment of a novel ecg electrode for motion artifact reduction

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring

A study on the effects of window size on electrocardiogram signal quality classification

The sliding window-based method is one of the most used method for automatic Electrocardiogram (ECG) signal quality classification. Based on this method, ECG signals are generally divided into small segments depending on a window size and these segments are then used in another classification process, e.g., feature extraction. The segmentation step is necessary and important for signal classification and signal segments with different window sizes can directly affect the performance of classification. However, in signal quality classification, the window size is often randomly selected and further analysis on the most appropriate window sizes is thus required. In this paper, an extensive investigation of the effects of window size on signal quality classification is presented.A set of statistical-amplitude-based features widely used in the literature was extracted based on 10 different window sizes, ranging from 1 to 10 seconds.To construct signal quality classification models, four well-known machine learning techniques, i.e., Decision Tree, Multilayer Perceptron, k-Nearest Neighbor, and Naïve Bayes, were employed.The performance of the quality classification models was validated on an ECG dataset collected using wireless sensors from 20 volunteers while performing routine activities, e.g.,sitting, walking, and jogging.The evaluation results obtained from four machine-learning classifiers demonstrated that the performance of signal quality classification using window sizes of 5 and 7 seconds were good compared with other sizes