Complex Biomedical Signals Research Articles

Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals.

Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series.

ЕЛЕМЕНТИ ІНТЕРФЕЙСУ НАУКОВО-ДОСЛІДНОГО WEB-РЕСУРСУ PHYSIONET ТА ІМПОРТ ДАНИХ У СИСТЕМУ КОМП'ЮТЕРНОЇ МАТЕМАТИКИ MAPLE 17

Since 1999, PhysioNet (http://physionet.org/) has offered free access via the web to large collections of recorded physiologic signals and medical databases as well as associated open-source software. The intention of this scientific resource is to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. PhysioBank archives include today the records obtained from healthy individuals and from patients with different diagnoses obtained under various conditions. It includes sudden cardiac death, congestive heart failure, neurological disorders, epilepsy and many others. Software packages PhysioToolkit is valuable for physiological signal processing and analysis, for creation of new databases, the interactive display and characterization of signals, the simulation of physiological and other signals. Nonetheless, a researcher should have skills to work with the operating system Unix and be knowledgeable in special commands to successful use software PhysioToolkit. Therefore, it makes sense to convert the necessary signals to a user-friendly computer algebra system. This paper describes interface elements of scientific web-resource PhysioNet, the simple methods of converting from binary medical data files to the text format and import of received digital signals into computer mathematics system Maple 17.

Use of K-means clustering in migraine detection by using EEG records under flash stimulation

In this study, a new migraine analysis method was proposed by using EEG (electroencephalography) signals under flash stimulation in time domain. EEG signal is one of the most complex biomedical signals due to its complex nature. Therefore, these types of signals are commonly pre-processed before the analysis procedure. Since, pre-processing techniques affect the analysis results positively or negatively, the achievements of these pre-processing techniques are very important. Flash-stimulated and non-stimulated EEG signals obtained from healthy subjects and migraine patients. First, a digital band pass FIR (Finite Impulse Response) filter is used to select beta band of the T5-T3 channel EEG signals. Then a time domain based pre-processing technique, histogram, was employed to obtain the features. Histogram differences in the case of flash stimulation (or non-stimulation) calculated and used as features for the healthy subjects and migraine patients. These features are applied to a k-means clustering algorithm to see clustering results of the proposed technique. Silhoutte clustering results show that, a good clustering performance is evaluated as 86.6% correct clustering rate (CCR) with the used techniques in migraine analysis. Results provide a good insight in this area for future studies. Key words: Migraine, EEG, K-means cluster, histogram.

PhysioNet: an NIH research resource for complex signals

The Research Resource for Complex Physiologic Signals, supported by the National Institutes of Health (NIH), is intended to promote and facilitate investigations in the study of cardiovascular and other complex biomedical signals. The resource website ( www.physionet.org ) has 3 interdependent components: 1) PhysioBank is an archive of well-characterized digital recordings of physiologic signals and related data, including databases of electrocardiogram and heart rate time series from patients with heart failure, coronary disease, sleep apnea syndromes, and cardiac arrhythmias; 2) PhysioToolkit is a library of open-source software for physiologic signal processing and analysis; and 3) PhysioNet, for which the resource is named, is an on-line forum for dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. PhysioNet, in cooperation with the annual Computers in Cardiology conference, hosts a series of challenges inviting participants to tackle clinically interesting problems that are either unsolved or not well solved. PhysioNet invites contributions of databases and software from the biomedical community.

PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.

The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of Health, is intended to stimulate current research and new investigations in the study of cardiovascular and other complex biomedical signals. The resource has 3 interdependent components. PhysioBank is a large and growing archive of well-characterized digital recordings of physiological signals and related data for use by the biomedical research community. It currently includes databases of multiparameter cardiopulmonary, neural, and other biomedical signals from healthy subjects and from patients with a variety of conditions with major public health implications, including life-threatening arrhythmias, congestive heart failure, sleep apnea, neurological disorders, and aging. PhysioToolkit is a library of open-source software for physiological signal processing and analysis, the detection of physiologically significant events using both classic techniques and novel methods based on statistical physics and nonlinear dynamics, the interactive display and characterization of signals, the creation of new databases, the simulation of physiological and other signals, the quantitative evaluation and comparison of analysis methods, and the analysis of nonstationary processes. PhysioNet is an on-line forum for the dissemination and exchange of recorded biomedical signals and open-source software for analyzing them. It provides facilities for the cooperative analysis of data and the evaluation of proposed new algorithms. In addition to providing free electronic access to PhysioBank data and PhysioToolkit software via the World Wide Web (http://www.physionet. org), PhysioNet offers services and training via on-line tutorials to assist users with varying levels of expertise.