A method for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats is presented. The method allocates manually detected heartbeats to one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard, i.e., normal beat, ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), fusion of a normal and a VEB, or unknown beat type. Data was obtained from the 44 nonpacemaker recordings of the MIT-BIH arrhythmia database. The data was split into two datasets with each dataset containing approximately 50 000 beats from 22 recordings. The first dataset was used to select a classifier configuration from candidate configurations. Twelve configurations processing feature sets derived from two ECG leads were compared. Feature sets were based on ECG morphology, heartbeat intervals, and RR-intervals. All configurations adopted a statistical classifier model utilizing supervised learning. The second dataset was used to provide an independent performance assessment of the selected configuration. This assessment resulted in a sensitivity of 75.9%, a positive predictivity of 38.5%, and a false positive rate of 4.7% for the SVEB class. For the VEB class, the sensitivity was 77.7%, the positive predictivity was 81.9%, and the false positive rate was 1.2%. These results are an improvement on previously reported results for automated heartbeat classification systems.

Electroencephalogram (EEG) data are typically contaminated with artifacts (e.g., by eye movements). The effect of artifacts can be attenuated by deleting data with amplitudes over a certain value, for example. Independent component analysis (ICA) separates EEG data into neural activity and artifact; once identified, artifactual components can be deleted from the data. Often, artifact rejection algorithms require supervision (e.g., training using canonical artifacts). Many artifact rejection methods are time consuming when applied to high-density EEG data. We describe FASTER (Fully Automated Statistical Thresholding for EEG artifact Rejection). Parameters were estimated for various aspects of data (e.g., channel variance) in both the EEG time series and in the independent components of the EEG: outliers were detected and removed. FASTER was tested on both simulated EEG (n=47) and real EEG (n=47) data on 128-, 64-, and 32-scalp electrode arrays. FASTER was compared to supervised artifact detection by experts and to a variant of the Statistical Control for Dense Arrays of Sensors (SCADS) method. FASTER had >90% sensitivity and specificity for detection of contaminated channels, eye movement and EMG artifacts, linear trends and white noise. FASTER generally had >60% sensitivity and specificity for detection of contaminated epochs, vs. 0.15% for SCADS. FASTER also aggregates the ERP across subject datasets, and detects outlier datasets. The variance in the ERP baseline, a measure of noise, was significantly lower for FASTER than either the supervised or SCADS methods. ERP amplitude did not differ significantly between FASTER and the supervised approach.

Human electrophysiological (EEG) studies have demonstrated the involvement of alpha band (8- to 14-Hz) oscillations in the anticipatory biasing of attention. In the context of visual spatial attention within bilateral stimulus arrays, alpha has exhibited greater amplitude over parietooccipital cortex contralateral to the hemifield required to be ignored, relative to that measured when the same hemifield is to be attended. Whether this differential effect arises solely from alpha desynchronization (decreases) over the “attending” hemisphere, from synchronization (increases) over the “ignoring” hemisphere, or both, has not been fully resolved. This is because of the confounding effect of externally evoked desynchronization that occurs involuntarily in response to visual cues. Here, bilateral flickering stimuli were presented simultaneously and continuously over entire trial blocks, such that externally evoked alpha desynchronization is equated in precue baseline and postcue intervals. Equivalent random letter sequences were superimposed on the left and right flicker stimuli. Subjects were required to count the presentations of the target letter “X” at the cued hemifield over an 8-s period and ignore the sequence in the opposite hemifield. The data showed significant increases in alpha power over the ignoring hemisphere relative to the precue baseline, observable for both cue directions. A strong attentional bias necessitated by the subjective difficulty in gating the distracting letter sequence is reflected in a large effect size of 2.1 (η² = 0.82), measured from the attention × hemisphere interaction. This strongly suggests that alpha synchronization reflects an active attentional suppression mechanism, rather than a passive one reflecting “idling” circuits.

An adaptive system for the automatic processing of the electrocardiogram (ECG) for the classification of heartbeats into one of the five beat classes recommended by ANSI/AAMI EC57:1998 standard is presented. The heartbeat classification system processes an incoming recording with a global-classifier to produce the first set of beat annotations. An expert then validates and if necessary corrects a fraction of the beats of the recording. The system then adapts by first training a local-classifier using the newly annotated beats and combines this with the global-classifier to produce an adapted classification system. The adapted system is then used to update beat annotations. The results of this study show that the performance of a patient adaptable classifier increases with the amount of training of the system on the local record. Crucially, the performance of the system can be significantly boosted with a small amount of adaptation even when all beats used for adaptation are from a single class. This study illustrates the ability to provide highly beneficial automatic arrhythmia monitoring and is an improvement on previously reported results for automated heartbeat classification systems

Major challenges must be tackled for brain-computer interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.

There is an interplay, between emotions and learning, but this interaction is far more complex than previous theories have articulated. The article proffers a novel model by which to: 1). regard the interplay of emotions upon learning for, 2). the larger practical aim of crafting computer-based models that will recognize a learner's affective state and respond appropriately to it, so that learning will proceed at an optimal pace.