IEEE transactions on bio-medical engineering
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IEEE Trans Biomed Eng · Jan 2012
Weighted conditional random fields for supervised interpatient heartbeat classification.
This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.
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IEEE Trans Biomed Eng · Jan 2012
An algorithm used for ventricular fibrillation detection without interrupting chest compression.
Ventricular fibrillation (VF) is the primary arrhythmic event in the majority of patients suffering from sudden cardiac arrest. Attention has been focused on this particular rhythm since it is recognized that prompt therapy, especially electrical defibrillation, may lead to a successful outcome. However, current versions of automated external defibrillators (AEDs) mandate repetitive interruptions of chest compression for rhythm analyses since artifacts produced by chest compression during cardiopulmonary resuscitation (CPR) preclude reliable electrocardiographic (ECG) rhythm analysis. ⋯ A total of 232 patients and 31,092 episodes of either VF or SR were accessed, in which 8195 episodes were corrupted by artifacts produced by chest compressions. We also compared the performance of this method with three other established algorithms, including VF filter, spectrum analysis, and complexity measurement. Even though there was a modest decrease in specificity and accuracy when chest compression artifact was present, the performance of this method was still superior to other reported methods for VF detection during uninterrupted CPR.
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IEEE Trans Biomed Eng · Nov 2011
Analysis of FMRI data using an integrated principal component analysis and supervised affinity propagation clustering approach.
Clustering analysis is a promising data-driven method for analyzing functional magnetic resonance imaging (fMRI) time series data. The huge computational load, however, creates practical difficulties for this technique. We present a novel approach, integrating principal component analysis (PCA) and supervised affinity propagation clustering (SAPC). ⋯ Four simulation studies and tests with three in vivo fMRI datasets containing data from both block-design and event-related experiments revealed that functional brain activation was effectively detected and different response patterns were distinguished using our integrated method. In addition, the improved SAPC method was superior to the k -centers clustering and hierarchical clustering methods in both block-design and event-related fMRI data, as measured by the average squared error. These results suggest that our proposed novel integrated approach will be useful for detecting brain functional activation in both block-design and event-related experimental fMRI data.
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IEEE Trans Biomed Eng · Nov 2011
Design and control of a demand flow system assuring spontaneous breathing of a patient connected to an HFO ventilator.
Lung protective ventilation is intended to minimize the risk of ventilator induced lung injury and currently aimed at preservation of spontaneous breathing during mechanical ventilation. High-frequency oscillatory ventilation (HFOV) is a lung protective ventilation strategy. Commonly used high-frequency oscillatory (HFO) ventilators, SensorMedics 3100, were not designed to tolerate spontaneous breathing. ⋯ In a bench test the performance of the DFS was evaluated using a simulator ASL 5000. With the gas inflow controlled, MAP was returned to its preset value within 115 ms after the beginning of inspiration. The DFS might help to spread the use of HFOV in clinical practice.
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IEEE Trans Biomed Eng · Sep 2011
Identification of adequate neurally adjusted ventilatory assist (NAVA) during systematic increases in the NAVA level.
Neurally adjusted ventilatory assist (NAVA) delivers airway pressure (P(aw)) in proportion to the electrical activity of the diaphragm (EAdi) using an adjustable proportionality constant (NAVA level, cm·H(2)O/μV). During systematic increases in the NAVA level, feedback-controlled down-regulation of the EAdi results in a characteristic two-phased response in P(aw) and tidal volume (Vt). The transition from the 1st to the 2nd response phase allows identification of adequate unloading of the respiratory muscles with NAVA (NAVA(AL)). ⋯ Median NAVA(AL) visually estimated by 18 independent physicians was 2.7 (range 0.4 to 5.8) cm·H(2)O/μV and identified by our model was 2.6 (range 0.6 to 5.0) cm·H(2)O/μV. NAVA(AL) identified by our model was below the range of visually estimated NAVA(AL) in two instances and was above in one instance. We conclude that our model identifies NAVA(AL) in most instances with acceptable accuracy for application in clinical routine and research.