A Wavelet based Method for QRS Complex Detection

ECG signal plays an important role in the diagnosis and analysis of heart diseases and allows the assessment of cardiac muscle functionality. The main and most obvious part of electrocardiography tracing is its QRS complex which corresponds to the ventricular depolarization. The morphology of QRS complex and its repetition are important issues in the analysis of heart diseases so its detection is important for such analysis. In this paper an algorithm based on the multiplication of wavelet coefficients is presented to find out the R peak in ECG for QRS complex detection. The proposed method is based on the band-limited properties of QRS waveform. The ability of proposed method has been evaluated through the comparison with traditional Pan-Tompkins algorithm by standard datasets. The results show that the proposed method besides having lower complexity is comparable with Pan-Tompkins method.

Electromagnetic radiation therapy for Parkinson’s disease tremor reduction- systematic reviews and Bayesian meta-analyses for comparing the effectiveness of electric, magnetic and light stimulation methods

Abstract Purpose Tremor is one of the key characteristics of Parkinson’s disease (PD), leading to physical disabilities and often showing limited responses to pharmacological treatments. To suppress tremors in PD patients, several types of non-invasive and non-pharmacological methods have been proposed so far. In the current systematic review, three electromagnetic-based radiation strategies including electrical stimulation, magnetic stimulation, and light stimulation methods were reviewed and compared. Methods Major databases were searched to retrieve eligible studies. For the meta-analysis, a random-effect Bayesian framework was used. Also, heterogeneity between studies was assessed using I2 statistic, prediction interval, and tau2. Publication bias was assessed using funnel plot, and the effectiveness of methods for reducing tremor was compared using network Bayesian meta-analysis. Results and conclusion Thirty-one studies were found for qualitative analysis, and 16 studies were found for quantitative synthesis. Based on the suppression ratio, methods can be ordered as electrical stimulation, light therapy, and magnetic stimulation. Furthermore, the results showed that electrical and magnetic stimulation were more effective for tremor suppression at early stages of PD, while light therapy was found to be more effective during the later stages of PD

Discrimination of ADHD Subtypes Using Decision Tree on Behavioral, Neuropsychological, and Neural Markers

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD) is a well-known neurodevelopmental disorder. Diagnosis and treatment of ADHD can often lead to a developmental trajectory toward positive results. The present study aimed at implementing the decision tree method to recognize children with and without ADHD, as well as ADHD subtypes.  Methods: In the present study, the subjects included 61 children with ADHD (subdivided into ADHD-I (n=25), ADHD-H (n=14), and ADHD-C (n=22) groups) and 43 typically developing controls matched by IQ and age. The Child Behavior Checklist (CBCL), Integrated Visual And Auditory (IVA) test, and quantitative EEG during eyes-closed resting-state were utilized to evaluate the level of behavioral, neuropsychology, and electrophysiology markers using a decision tree algorithm, respectively. Results: Based on the results, excellent classification accuracy (100%) was obtained to discriminate children with ADHD from the control group. Also, the ADHD subtypes, including combined, inattention, and hyperactive/impulsive subtypes were recognized from others with an accuracy of 80.41%, 84.17%, and 71.46%, respectively.  Conclusion: Our results showed that children with ADHD can be recognized from the healthy controls based on the neuropsychological data (sensory-motor parameters of IVA). Also, subtypes of ADHD can be distinguished from each other using behavioral, neuropsychiatric and electrophysiological parameters. The findings suggested that the decision tree method may present an efficient and accurate diagnostic tool for the clinicians

Parkinson’s disease tremor prediction using EEG data analysis-A preliminary and feasibility study

Abstract Purpose Tremor is one of the hallmarks of Parkinson’s disease (PD) that does not respond effectively to conventional medications. In this regard, as a complementary solution, methods such as deep brain stimulation have been proposed. To apply the intervention with minimal side effects, it is necessary to predict tremor initiation. The purpose of the current study was to propose a novel methodology for predicting resting tremors using analysis of EEG time-series. Methods A modified algorithm for tremor onset detection from accelerometer data was proposed. Furthermore, a machine learning methodology for predicting PD hand tremors from EEG time-series was proposed. The most discriminative features extracted from EEG data based on statistical analyses and post-hoc tests were used to train the classifier for distinguishing pre-tremor conditions. Results Statistical analyses with post-hoc tests showed that features such as form factor and statistical features were the most discriminative features. Furthermore, limited numbers of EEG channels (F3, F7, P4, CP2, FC6, and C4) and EEG bands (Delta and Gamma) were sufficient for an accurate tremor prediction based on EEG data. Based on the selected feature set, a KNN classifier obtained the best pre-tremor prediction performance with an accuracy of 73.67%. Conclusion This feasibility study was the first attempt to show the predicting ability of EEG time-series for PD hand tremor prediction. Considering the limitations of this study, future research with longer data, and different brain dynamics are needed for clinical applications

Interaction of low frequency external electric fields and pancreatic β-cell: a mathematical modeling approach to identify the influence of excitation parameters

<p><b>Purpose:</b> Although the effect of electromagnetic fields on biological systems has attracted attraction in recent years, there has not been any conclusive result concerning the effects of interaction and the underlying mechanisms involved. Besides the complexity of biological systems, the parameters of the applied electromagnetic field have not been estimated in most of the experiments.</p> <p><b>Materials and Methods:</b> In this study, we have used computational approach in order to find the excitation parameters of an external electric field which produces sensible effects in the function of insulin secretory machinery, whose failure triggers the diabetes disease. A mathematical model of the human β-cell has been used and the effects of external electric fields with different amplitudes, frequencies and wave shapes have been studied.</p> <p><b>Results:</b> The results from our simulations show that the external electric field can influence the membrane electrical activity and perhaps the insulin secretion when its amplitude exceeds a threshold value. Furthermore, our simulations reveal that different waveforms have distinct effects on the β-cell membrane electrical activity and the characteristic features of the excitation like frequency would change the interaction mechanism.</p> <p><b>Conclusion:</b> The results could help the researchers to investigate the possible role of the environmental electromagnetic fields on the promotion of diabetes disease.</p

MAPPING LOCAL PATTERNS OF CHILDHOOD OVERWEIGHT AND WASTING IN LOW- AND MIDDLE-INCOME COUNTRIES BETWEEN 2000 AND 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic