IEEE journal of biomedical and health informatics
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IEEE J Biomed Health Inform · Mar 2017
Automatic Detection and Parameterization of Manual Bag-Mask Ventilation on Newborns.
Birth asphyxia is a condition where a fetus suffers from lack of oxygen during birth. Intervention by manual ventilation should start within one minute after birth. Bag-mask resuscitators are commonly used in situations where ventilation is provided by a single health care worker. Due to a high complexity of interactions between physiological conditions of the newborns and the clinical treatment, the recommendations for bag-mask ventilation of infants remains controversial. The purpose of this paper is to illustrate the processing and parameterization of ventilation signals recorded from the Laerdal newborn resuscitation monitor into meaningful data. ⋯ Information about ventilation events and ventilation parameters could potentially be useful during a resuscitation situation by giving immediate feedback to the health care provider.
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IEEE J Biomed Health Inform · Jan 2017
An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification.
The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer vision offer the opportunity for image-based diagnosis, teaching, and biomedical research. However, the content and semantics of an image can vary depending on its modality and as such the identification of image modality is an important preliminary step. The key challenge for automatically classifying the modality of a medical image is due to the visual characteristics of different modalities: some are visually distinct while others may have only subtle differences. ⋯ These features are used to train numerous multiclass classifiers whose posterior probabilities are fused to predict the modalities of unseen images. Our experiments on the ImageCLEF 2016 medical image public dataset (30 modalities; 6776 training images, and 4166 test images) show that our ensemble of fine-tuned CNNs achieves a higher accuracy than established CNNs. Our ensemble also achieves a higher accuracy than methods in the literature evaluated on the same benchmark dataset and is only overtaken by those methods that source additional training data.
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IEEE J Biomed Health Inform · Sep 2016
Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.
Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). ⋯ These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.
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IEEE J Biomed Health Inform · Jul 2016
Mahalanobis Taguchi System to Identify Pre-indicators of Delirium in the ICU.
This paper was designed to determine if the Mahalanobis-Taguchi System (MTS) applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The methodology defined the evidence-based bundle as a multidimensional system that conformed to a parameter diagram. The Mahalanobis distance (MD) was calculated for the retrospective healthy observations and the retrospective unhealthy observations. ⋯ The specificity of the detection system was 0.93 with a 95% confidence interval between 0.90 and 0.95. The MTS applied to the delirium-evidence-based bundle could detect medical patterns in retrospective datasets. The implication of this paper to a biomedical research is an automated decision support tool for the delirium-evidence-based bundle providing an early detection capability needed today.
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IEEE J Biomed Health Inform · Mar 2016
Improvement in Neural Respiratory Drive Estimation From Diaphragm Electromyographic Signals Using Fixed Sample Entropy.
Diaphragm electromyography is a valuable technique for the recording of electrical activity of the diaphragm. The analysis of diaphragm electromyographic (EMGdi) signal amplitude is an alternative approach for the quantification of the neural respiratory drive (NRD). The EMGdi signal is, however, corrupted by electrocardiographic (ECG) activity, and this presence of cardiac activity can make the EMGdi interpretation more difficult. ⋯ The mean and standard deviation of the Pearson's correlation values between inspiratory mouth pressure (an indirect measure of the respiratory muscle activity) and fSampEn, ARV, and RMS parameters, estimated in the recorded EMGdi signal at tidal volume (without inspiratory load), were 0.38±0.12, 0.27±0.11 , and 0.11±0.13, respectively. Whereas at 33 cmH2O (maximum inspiratory load) were 0.83±0.02, 0.76±0.07, and 0.61±0.19 , respectively. Our findings suggest that the proposed method may improve the evaluation of NRD.