Sample Entropy Computation on Signals with Missing Values.

Sample entropy embeds time series into m-dimensional spaces and estimates entropy based on the distances between points in these spaces. However, when samples can be considered as missing or invalid, defining distance in the embedding space becomes problematic. Preprocessing techniques, such as deletion or interpolation, can be employed as a solution, producing time series without missing or invalid values.

Carpal Tunnel Syndrome Automated Diagnosis: A Motor vs. Sensory Nerve Conduction-Based Approach.

The objective of this study was to evaluate the effectiveness of machine learning classification techniques applied to nerve conduction studies (NCS) of motor and sensory signals for the automatic diagnosis of carpal tunnel syndrome (CTS). Two methodologies were tested. In the first methodology, motor signals recorded from the patients' median nerve were transformed into time-frequency spectrograms using the short-time Fourier transform (STFT).

Automatic Electrodiagnosis of Carpal Tunnel Syndrome Using Machine Learning.

Recent literature has revealed a long discussion about the importance and necessity of nerve conduction studies in carpal tunnel syndrome management. The purpose of this study was to investigate the possibility of automatic detection, based on electrodiagnostic features, for the median nerve mononeuropathy and decision making about carpal tunnel syndrome. The study included 38 volunteers, examined prospectively.

A Two-Steps-Ahead Estimator for Bubble Entropy.

: Bubble entropy (bEn) is an entropy metric with a limited dependence on parameters. bEn does not directly quantify the conditional entropy of the series, but it assesses the change in entropy of the ordering of portions of its samples of length , when adding an extra element. The analytical formulation of bEn for autoregressive (AR) processes shows that, for this class of processes, the relation between the first autocorrelation coefficient and bEn changes for odd and even values of .

Estimation of HRV Based on Low Frequency Data Transmission.

Smart devices, including the popular smart watches, often collect information on the heart beat rhythm and transmit it to a central server for storage or further processing. A factor introducing important limitations in the amount of data collected, transmitted and finally processed is the life of the mobile device or smart watch battery. Some devices choose to transmit the mean heart rate over relatively long periods of time, to save power.

Mathematical Models and Computer Simulation of Chemical Carcinogenesis Process and its Inhibition by Anticarcinogenic Substances.

Cancer research has yielded tremendous gains over the last two decades with remarkable results addressing this worldwide major public health problem. Continuous technological developments and persistent research has led to significant progress in targeted therapies. This paper focuses on the study of mathematical models that describe in the most optimal way the development of malignant tumours induced in experimental animals of a particular species following chemical carcinogenesis with a complete carcinogen factor known as 3,4-benzopyrene.

Low Computational Cost for Sample Entropy.

Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regularity/complexity of a time series. On the other hand, it is a computationally expensive method which may require a large amount of time when used in long series or with a large number of signals. The computationally intensive part is the similarity check between points in dimensional space.

Bubble Entropy: An Entropy Almost Free of Parameters.

: A critical point in any definition of entropy is the selection of the parameters employed to obtain an estimate in practice. We propose a new definition of entropy aiming to reduce the significance of this selection. We call the new definition .

Deceleration Capacity of Heart Rate Predicts Arrhythmic and Total Mortality in Heart Failure Patients.

Background: Deceleration capacity (DC) of heart rate proved an independent mortality predictor in postmyocardial infarction patients. The original method (DCorig) may produce negative values (9% in our analyzed sample). We aimed to improve the method and to investigate if DC also predicts the arrhythmic mortality.

Alignment of R-R interval signals using the circadian heart rate rhythm.

R-R interval signals that come from different subjects are regularly aligned and averaged according to the horological starting time of the recordings. We argue that the horological time is a faulty alignment criterion and provide evidence in the form of a new alignment method. Our main motivation is that the human heart rate (HR) rhythm follows a circadian cycle, whose pattern can vary among different classes of people.

Is low-molecular-weight heparin safe for venous thromboembolism prophylaxis in patients with traumatic brain injury? A Western Trauma Association multicenter study.

Background: Venous thromboembolism (VTE) is a significant risk in trauma patients. Although low-molecular weight heparin (LMWH) is effective in VTE prophylaxis, its use for patients with traumatic intracranial hemorrhage remains controversial. The purpose of this study was to evaluate the safety of LMWH for VTE prophylaxis in blunt intracranial injury.

The use of emergency department thoracotomy for traumatic cardiopulmonary arrest.

Despite the establishment of evidence-based guidelines for the resuscitation of critically injured patients who have sustained cardiopulmonary arrest, rapid decisions regarding patient salvageability in these situations remain difficult even for experienced physicians. Regardless, survival is limited after traumatic cardiopulmonary arrest. One applicable, well-described resuscitative technique is the emergency department thoracotomy-a procedure that, when applied correctly, is effective in saving small but significant numbers of critically injured patients.

Automated diagnosis of diseases based on classification: dynamic determination of the number of trees in random forests algorithm.

The accurate diagnosis of diseases with high prevalence rate, such as Alzheimer, Parkinson, diabetes, breast cancer, and heart diseases, is one of the most important biomedical problems whose administration is imperative. In this paper, we present a new method for the automated diagnosis of diseases based on the improvement of random forests classification algorithm. More specifically, the dynamic determination of the optimum number of base classifiers composing the random forests is addressed.

A six stage approach for the diagnosis of the Alzheimer's disease based on fMRI data.

The aim of this work is to present an automated method that assists in the diagnosis of Alzheimer's disease and also supports the monitoring of the progression of the disease. The method is based on features extracted from the data acquired during an fMRI experiment. It consists of six stages: (a) preprocessing of fMRI data, (b) modeling of fMRI voxel time series using a Generalized Linear Model, (c) feature extraction from the fMRI data, (d) feature selection, (e) classification using classical and improved variations of the Random Forests algorithm and Support Vector Machines, and (f) conversion of the trees, of the Random Forest, to rules which have physical meaning.

Heartbeat time series classification with support vector machines.

In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation.

Fast computation of approximate entropy.

The approximate entropy (ApEn) is a measure of systems complexity. The implementation of the method is computationally expensive and requires execution time analogous to the square of the size of the input signal. We propose here a fast algorithm which speeds up the computation of approximate entropy by detecting early some vectors that are not similar and by excluding them from the similarity test.

Robustness of support vector machine-based classification of heart rate signals.

In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth.

Assessment of the classification capability of prediction and approximation methods for HRV analysis.

The goal of this paper is to examine the classification capabilities of various prediction and approximation methods and suggest which are most likely to be suitable for the clinical setting. Various prediction and approximation methods are applied in order to detect and extract those which provide the better differentiation between control and patient data, as well as members of different age groups. The prediction methods are local linear prediction, local exponential prediction, the delay times method, autoregressive prediction and neural networks.

Open aneurysm repair in elderly patients not candidates for endovascular repair (EVAR): Comparison with patients undergoing EVAR or preferential open repair.

The authors reviewed a 2-year experience with abdominal aortic aneurysm (AAA) repair to determine if patients who were excluded from endovascular aneurysm repair (EVAR) because of anatomic criteria (Group III) represented a higher risk for subsequent open aneurysm repair than either patients undergoing EVAR (Group II) or those patients who preferentially underwent open repair (Group I). Between January 2001 and December 2003, 107 patients underwent AAA repair. Open repair was recommended in patients <70 years of age and without significant comorbidities (Group I).