kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances

Effects of steroids and angiotensin converting enzyme inhibition on circumferential strain in boys with Duchenne muscular dystrophy: a cross-sectional and longitudinal study utilizing cardiovascular magnetic resonance

<p>Abstract</p> <p>Background</p> <p>Steroid use has prolonged ambulation in Duchenne muscular dystrophy (DMD) and combined with advances in respiratory care overall management has improved such that cardiac manifestations have become the major cause of death. Unfortunately, there is no consensus for DMD-associated cardiac disease management. Our purpose was to assess effects of steroid use alone or in combination with angiotensin converting enzyme inhibitors (ACEI) or angiotension receptor blocker (ARB) on cardiovascular magnetic resonance (CMR) derived circumferential strain (ε_cc).</p> <p>Methods</p> <p>We used CMR to assess effects of corticosteroids alone (Group A) or in combination with ACEI or ARB (Group B) on heart rate (HR), left ventricular ejection fraction (LVEF), mass (LVM), end diastolic volume (LVEDV) and circumferential strain (ε_cc) in a cohort of 171 DMD patients >5 years of age. Treatment decisions were made independently by physicians at both our institution and referral centers and not based on CMR results.</p> <p>Results</p> <p>Patients in Group A (114 studies) were younger than those in Group B (92 studies)(10 ± 2.4 vs. 12.4 ± 3.2 years, p < 0.0001), but HR, LVEF, LVEDV and LVM were not different. Although ε_ccmagnitude was lower in Group B than Group A (-13.8 ± 1.9 vs. -12.8 ± 2.0, p = 0.0004), age correction using covariance analysis eliminated this effect. In a subset of patients who underwent serial CMR exams with an inter-study time of ~15 months, ε_ccworsened regardless of treatment group.</p> <p>Conclusions</p> <p>These results support the need for prospective clinical trials to identify more effective treatment regimens for DMD associated cardiac disease.</p

The Electroencephalogram as a Biomarker Based on Signal Processing Using Nonlinear Techniques to Detect Dementia

Dementia being a syndrome caused by a brain disease of a chronic or progressive nature, in which the irreversible loss of intellectual abilities, learning, expressions arises; including memory, thinking, orientation, understanding and adequate communication, of organizing daily life and of leading a family, work and autonomous social life; leads to a state of total dependence; therefore, its early detection and classification is of vital importance in order to serve as clinical support for physicians in the personalization of treatment programs. The use of the electroencephalogram as a tool for obtaining information on the detection of changes in brain activities. This article reviews the types of cognitive spectrum dementia, biomarkers for the detection of dementia, analysis of mental states based on electromagnetic oscillations, signal processing given by the electroencephalogram, review of processing techniques, results obtained where it is proposed the mathematical model about neural networks, discussion and finally the conclusions

Analysis of Epileptic Activity Based on Brain Mapping of EEG Adaptive Time-Frequency Decomposition

The applications of Empirical Mode Decomposition (EMD) in Biomedical Signal analysis have increased and is common now to find publications that use EMD to identify behaviors in the brain or heart. EMD has shown excellent results in the identification of behaviours from the use of electroencephalogram (EEG) signals. In addition, some advances in the computer area have made it possible to improve their performance. In this paper, we presented a method that, using an entropy analysis, can automatically choose the relevant Intrinsic Mode Functions (IMFs) from EEG signals. The idea is to choose the minimum number of IMFs to reconstruct the brain activity. The EEG signals were processed by EMD and the IMFs were ordered according to the entropy cost function. The IMFs with more relevant information are selected for the brain mapping. To validate the results, a relative error measure was used

Aortic Function: From the Research Laboratory to the Clinic

For many years, much of the pioneering research on aortic function was carried out by a small group of investigators frequently working away from the clinical environment in the research laboratory. The evaluation of aortic function using aortic pulse wave velocity, aortic distensibility, or other practical indices had yet to reach clinical threshold. It was necessary for the clinicians to take over and to apply these indices to the clinic. In this Odyssey, the work by the basic scientist was important to define the fundamental mechanisms of aortic function; however, it was the vision of the clinical investigator who recognized the importance of aortic function and introduced it into clinical practice. In the near future, the clinical investigator will introduce aortic function in daily clinical practice as the measurement of left ventricular function is used today. A close collaboration between the clinical and the basic investigator will be necessary in order to define the molecular mechanisms related to aortic wall synthesis and degradation of collagen and elastin. Application of these findings by the clinical investigator may help to delay or prevent aortic dysfunction related to aging or other conditions and diseases. Copyright (c) 2012 S. Karger AG, Base

Toward a Semi-Self-Paced EEG Brain Computer Interface: Decoding Initiation State from Non-Initiation State in Dedicated Time Slots

Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are “synchronous” systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in “asynchronous” BCIs subjects pace the interaction and the system must determine when the subject’s control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject’s intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs