Physiological measurement
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Physiological measurement · Aug 2016
Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for the reduction of false alarms in ICUs.
False intensive care unit (ICU) alarms induce stress in both patients and clinical staff and decrease the quality of care, thus significantly increasing both the hospital recovery time and rehospitalization rates. In the PhysioNet/CinC Challenge 2015 for reducing false arrhythmia alarms in ICU bedside monitor data, this paper validates the application of a real-time arrhythmia detection library (ADLib, Schiller AG) for the robust detection of five types of life-threatening arrhythmia alarms. The strength of the application is to give immediate feedback on the arrhythmia event within a scan interval of 3 s-7.5 s, and to increase the noise immunity of electrocardiogram (ECG) arrhythmia analysis by fusing its decision with supplementary ECG quality interpretation and real-time pulse wave monitoring (quality and hemodynamics) using arterial blood pressure or photoplethysmographic signals. ⋯ The performance (true positive rate, true negative rate) is reported in the blinded challenge test set for different arrhythmias: asystole (83%, 96%), extreme bradycardia (100%, 90%), extreme tachycardia (98%, 80%), ventricular tachycardia (84%, 82%), and ventricular fibrillation (78%, 84%). Another part of this study considers the validation of ADLib with four reference ECG databases (AHA, EDB, SVDB, MIT-BIH) according to the international recommendations for performance reports in ECG monitors (ANSI/AAMI EC57). The sensitivity (Se) and positive predictivity (+P) are: QRS detector QRS (Se, +P) > 99.7%, ventricular ectopic beat (VEB) classifier VEB (Se, +P) = 95%, and ventricular fibrillation detector VFIB (P + = 94.8%) > VFIB (Se = 86.4%), adjusted to the clinical setting requirements, giving preference to low false positive alarms.
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Physiological measurement · Aug 2016
Suppression of false arrhythmia alarms in the ICU: a machine learning approach.
This paper presents a novel approach for false alarm suppression using machine learning tools. It proposes a multi-modal detection algorithm to find the true beats using the information from all the available waveforms. This method uses a variety of beat detection algorithms, some of which are developed by the authors. ⋯ For the ventricular tachycardia and ventricular fibrillation alarms, separate classification models are trained to distinguish between the normal and abnormal beats. This information, along with alarm-specific criteria, is used to decide if the alarm is false. The results indicate that the presented method was effective in suppressing false alarms when it was tested on a hidden validation dataset.
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Physiological measurement · Aug 2016
Reduction of false arrhythmia alarms using signal selection and machine learning.
In this paper, we propose an algorithm that classifies whether a generated cardiac arrhythmia alarm is true or false. The large number of false alarms in intensive care is a severe issue. The noise peaks caused by alarms can be high and in a noisy environment nurses can experience stress and fatigue. ⋯ The algorithm was trained and evaluated with the PhysioNet/Computing in Cardiology Challenge 2015 data set. In the test set the overall true positive rates were 93 and 95% and true negative rates 80 and 83%, respectively for events with no information and events with information after the alarm. The overall challenge scores were 77.39 and 81.58.
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Physiological measurement · Jun 2016
Regional lung function determined by electrical impedance tomography during bronchodilator reversibility testing in patients with asthma.
The measurement of rapid regional lung volume changes by electrical impedance tomography (EIT) could determine regional lung function in patients with obstructive lung diseases during pulmonary function testing (PFT). EIT examinations carried out before and after bronchodilator reversibility testing could detect the presence of spatial and temporal ventilation heterogeneities and analyse their changes in response to inhaled bronchodilator on the regional level. We examined seven patients suffering from chronic asthma (49 ± 19 years, mean age ± SD) using EIT at a scan rate of 33 images s(-1) during tidal breathing and PFT with forced full expiration. ⋯ Spatial and temporal ventilation distribution improved in the patients with asthma after the bronchodilator administration as evidenced mainly by the histograms of pixel FEV1/FVC values and pixel expiration times. The examination of regional lung function using EIT enables the assessment of spatial and temporal heterogeneity of ventilation distribution during bronchodilator reversibility testing. EIT may become a new tool in PFT, allowing the estimation of the natural disease progression and therapy effects on the regional and not only global level.
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Physiological measurement · Apr 2016
Apnea-hypopnea index estimation using quantitative analysis of sleep macrostructure.
Obstructive sleep apnea, characterized by recurrent cessation or substantial reduction in breathing during sleep, is a prevalent and serious medical condition. Although a significant relationship between obstructive sleep apnea and sleep macrostructure has been revealed in several studies, useful applications of this relationship have been limited. The aim of this study was to suggest a novel approach using quantitative analysis of sleep macrostructure to estimate the apnea-hypopnea index, which is commonly used to assess obstructive sleep apnea. ⋯ Between the apnea-hypopnea index estimates and the reference values reported from polysomnography, a root mean square error of 7.30 events h(-1) was obtained in the validation set. At an apnea-hypopnea index cut-off of ⩾30 events h(-1), the obstructive sleep apnea diagnostic performance was provided with a sensitivity of 90.0%, a specificity of 93.5%, and an accuracy of 92.4% by our method. The developed apnea-hypopnea index estimation model has the potential to be utilized in circumstances in which it is not possible to acquire or analyze respiration signal but it is possible to obtain information on sleep macrostructure.