Computational and mathematical methods in medicine
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Comput Math Methods Med · Jan 2014
Gastroscopic image graph: application to noninvasive multitarget tracking under gastroscopy.
Gastroscopic examination is one of the most common methods for gastric disease diagnosis. In this paper, a multitarget tracking approach is proposed to assist endoscopists in identifying lesions under gastroscopy. This approach analyzes numerous preobserved gastroscopic images and constructs a gastroscopic image graph. ⋯ The clinical experimental results demonstrated that the accuracy at angularis, antral, and stomach body was 6.3 ± 2.4 mm, 7.6 ± 3.1 mm, and 7.9 ± 1.6 mm, respectively. The mean accuracy was 7.31 mm, average targeting time was 56 ms, and the P value was 0.032, which makes it an attractive candidate for clinical practice. Furthermore, this approach provides a significant reference for endoscopic target tracking of other soft tissue organs.
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Comput Math Methods Med · Jan 2014
Methodological framework for estimating the correlation dimension in HRV signals.
This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. ⋯ The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension. D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.
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The present review describes and validates a new ratio "S" created for matching predictability and balance between TP and TN. Validity of S was studied in a three-step process as follows: (i) S was applied to the data of a past study predicting cardiac output response to fluid bolus from response to passive leg raise (PLR); (ii) S was comparatively analyzed with traditional ratios by modeling different 2 ∗ 2 contingency tables in 1000 hypothetical patients; (iii) precision of S was compared with other ratios by computing random fluctuations in the same patients. In comparison to other ratios, S performs better in predicting the cardiac response to fluid bolus and supports more directly the clinical conclusions. ⋯ When the proportion of true responses is high, S is the unique ratio that identifies the categorization that balances the proportion of TP and TN. The precision of S is close to that of CC. In conclusion, S should be considered for creating categories from quantitative variables; especially when matching predictability with balance between TP and TN is a concern.
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Comput Math Methods Med · Jan 2014
A patient-specific airway branching model for mechanically ventilated patients.
Respiratory mechanics models have the potential to guide mechanical ventilation. Airway branching models (ABMs) were developed from classical fluid mechanics models but do not provide accurate models of in vivo behaviour. Hence, the ABM was improved to include patient-specific parameters and better model observed behaviour (ABMps). ⋯ The ABMps model allows the estimation of airway pressure drop at each bronchial generation with patient-specific physiological measurements and can be generated from data measured at the bedside. The distribution of patient-specific α values indicates that the overall ABM can be readily improved to better match observed data and capture patient condition.
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Comput Math Methods Med · Jan 2014
A wavelet transform based method to determine depth of anesthesia to prevent awareness during general anesthesia.
Awareness during general anesthesia for its serious psychological effects on patients and some juristically problems for anesthetists has been an important challenge during past decades. Monitoring depth of anesthesia is a fundamental solution to this problem. The induction of anesthesia alters frequency and mean of amplitudes of the electroencephalogram (EEG), and its phase couplings. ⋯ Moreover, when BIS values increase, the entropy value of modulated signal also increases and vice versa. In addition, measuring phase coupling between delta and alpha subbands of EEG signals through continuous CWT analysis reveals the depth of anesthesia level. As a result, awareness during anesthesia can be prevented.