Intelligent Reading Recognition Method of Pointer Meter Based on Deep Learning in Real Environment

The accurate reading of pointer meter is a crucial task in complex environments such as substations, military and aerospace. The current recognition algorithm is mainly used to identify the same type and non-tilt meter, which has limited application in real environment. This paper proposes a novel end-to-end intelligent reading method of pointer meter based on deep learning, which locates the meter and extracts the pointer simultaneously without any prior information. Especially, the pointer is directly and precisely extracted using the designed semi-pointer detection method without any handcrafted features designed in advance, which avoids the accumulated error caused by preprocessing. Based on the extracted panel object, including semi-pointer, panel center and scale characters, the indicated value of the pointer is obtained by a local angle method, which can achieve better performance than the traditional angle method by referring to the neighboring scale lines of the pointer. Experimental results demonstrate that the method is faster and more effective than some common methods. It is worth noting that this study has the advantage of being able to recognize pointer meters in complex environments such as tilt, rotation, blur and illumination. It is acceptable for the actual application requirements in real environment with a recognition accuracy of 99.20% and the average reference error of 0.34%.

Do Life Course Physical Activity Profiles Reduce the Effects of Childhood Exposures on Cognition?

Although physical activity throughout life is one of the most reliable predictors of healthy aging, can less consistent or favorable trajectories also improve cognition trajectories among older adults? Drawing from accumulation theories, we use longitudinal data from the Health and Retirement Study and Life History Mail Survey (N=9,309) to examine the early antecedents of cognitive decline and the extent to which different life course physical activity profiles can slow such a decline. Results from latent class analysis reveal seven distinct profiles: consistently low, consistently high, consistently average (reference), improvers, decliners, midlife motivators, and previously athletic “couch potatoes.” Growth curve modeling analyses show that membership in the consistently high class and midlife motivators were associated with better cognition initially and over time, with no difference between the two classes. Additionally, though poor health and learning problems in childhood were associated with worse initial cognition, physical activity does not mediate the relationship.

Altered electroencephalography resting state network coherence in remitted MDD

Individuals with remitted depression are at greater risk for subsequent depression and therefore may provide a unique opportunity to understand the neurophysiological correlates underlying the risk of depression. Research has identified abnormal resting-state electroencephalography (EEG) power metrics and functional connectivity patterns associated with major depression, however little is known about these neural signatures in individuals with remitted depression. We investigate the spectral dynamics of 64-channel EEG surface power and source-estimated network connectivity during resting states in 37 individuals with depression, 56 with remitted depression, and 49 healthy adults that did not differ on age, education, and cognitive ability across theta, alpha, and beta frequencies. Average reference spectral EEG surface power analyses identified greater left and midfrontal theta in remitted depression compared to healthy adults. Using Network Based Statistics, we also demonstrate within and between network alterations in LORETA transformed EEG source-space coherence across the default mode, fronto-parietal, and salience networks where individuals with remitted depression exhibited enhanced coherence compared to those with depression, and healthy adults. This work builds upon our currently limited understanding of resting EEG connectivity in depression, and helps bridge the gap between aberrant EEG power and brain network connectivity dynamics in this disorder. Further, our unique examination of remitted depression relative to both healthy and depressed adults may be key to identifying brain-based biomarkers for those at high risk for future, or subsequent depression.

The Listening Zone of Human Electrocorticographic Field Potential Recordings

Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans due to their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed the correlations between signals as a function of distance (expressed as full width at half maximum; FWHM) between 8,752 recording sites in 71 patients implanted with either subdural electrodes (SDE), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDE and sEEG electrodes, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high gamma (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different, however the FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEG electrodes showed much lower power for frequencies 17 to 200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEG electrodes, as compared with a white matter reference or a common average reference. These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.

Investigating Data Cleaning Methods to Improve Performance of Brain–Computer Interfaces Based on Stereo-Electroencephalography

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.

Clinical evaluation of a wearable sensor for mobile monitoring of respiratory rate on hospital wards

A wireless and wearable system was recently developed for mobile monitoring of respiratory rate (RR). The present study was designed to compare RR mobile measurements with reference capnographic measurements on a medical-surgical ward. The wearable sensor measures impedance variations of the chest from two thoracic and one abdominal electrode. Simultaneous measurements of RR from the wearable sensor and from the capnographic sensor (1 measure/minute) were compared in 36 ward patients. Patients were monitored for a period of 182 ± 56 min (range 68–331). Artifact-free RR measurements were available 81% of the monitoring time for capnography and 92% for the wearable monitoring system (p < 0.001). A total of 4836 pairs of simultaneous measurements were available for analysis. The average reference RR was 19 ± 5 breaths/min (range 6–36). The average difference between the wearable and capnography RR measurements was − 0.6 ± 2.5 breaths/min. Error grid analysis showed that the proportions of RR measurements done with the wearable system were 89.7% in zone A (no risk), 9.6% in zone B (low risk) and < 1% in zones C, D and E (moderate, significant and dangerous risk). The wearable method detected RR values > 20 (tachypnea) with a sensitivity of 81% and a specificity of 93%. In ward patients, the wearable sensor enabled accurate and precise measurements of RR within a relatively broad range (6–36 b/min) and the detection of tachypnea with high sensitivity and specificity.

Enhancing Performance of SSVEP-Based Visual Acuity via Spatial Filtering

The purpose of this study was to enhance the performance of steady-state visual evoked potential (SSVEP)-based visual acuity assessment with spatial filtering methods. Using the vertical sinusoidal gratings at six spatial frequency steps as the visual stimuli for 11 subjects, SSVEPs were recorded from six occipital electrodes (O1, Oz, O2, PO3, POz, and PO4). Ten commonly used training-free spatial filtering methods, i.e., native combination (single-electrode), bipolar combination, Laplacian combination, average combination, common average reference (CAR), minimum energy combination (MEC), maximum contrast combination (MCC), canonical correlation analysis (CCA), multivariate synchronization index (MSI), and partial least squares (PLS), were compared for multielectrode signals combination in SSVEP visual acuity assessment by statistical analyses, e.g., Bland–Altman analysis and repeated-measures ANOVA. The SSVEP signal characteristics corresponding to each spatial filtering method were compared, determining the chosen spatial filtering methods of CCA and MSI with a higher performance than the native combination for further signal processing. After the visual acuity threshold estimation criterion, the agreement between the subjective Freiburg Visual Acuity and Contrast Test (FrACT) and SSVEP visual acuity for the native combination (0.253 logMAR), CCA (0.202 logMAR), and MSI (0.208 logMAR) was all good, and the difference between FrACT and SSVEP visual acuity was also all acceptable for the native combination (−0.095 logMAR), CCA (0.039 logMAR), and MSI (−0.080 logMAR), where CCA-based SSVEP visual acuity had the best performance and the native combination had the worst. The study proved that the performance of SSVEP-based visual acuity can be enhanced by spatial filtering methods of CCA and MSI and also recommended CCA as the spatial filtering method for multielectrode signals combination in SSVEP visual acuity assessment.

Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

Parkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Knn) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Knn. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.

Dietary Intake Patterns, Nutrient Adequacy and Associated Factors in a Multi-Ethnic Caribbean Population

Objectives To evaluate the distribution and correlates of foods consumed among persons 18–65 years. Methods Participants completed a questionnaire consisting of dietary, demographic and lifestyle items. Anthropometry was self reported with 15% of participants having weight and heights measured using recommended procedures. Dietary intakes were analyzed for nutrient composition using the NutriGenie 7.0 software. Foods were categorized by the level of processing as unprocessed/minimally processed or processed/ultra-processed. Inadequate intakes were categorized as energy-adjusted nutrient intakes &lt; estimated average reference intake (EAR) or average intakes (AI) according to the Institute of Medicine 2006 recommendations. Dietary patterns were determined by principal component analysis (PCA). The study was approved by The University of the West Indies Ethics Committee. Participation was voluntary follow oral and written consent Results Altogether, 11783 persons (females = 6743; males 5040) participated in the study. Approximately 72.5% of participants reported habitual plausible energy intakes (i.e., a Goldberg ratio of 1.35–2.40). Mean calorie intakes were higher in males than females (2771 ± 674 vs. 2270 ± 599 kcals; P &lt; 0.001). Persons of South Asian- and Mixed-descents were more likely that those of African-descent to report plausible intakes of calories. PCA reveal three predominate dietary intake patterns designated ‘Typical’, ‘Fruit and Vegetables’, and ‘Prudent’ that explain 44% of the variance in nutrient adequate diets. Process/ultra-process foods accounted for 83% of calories consumed and 60–80% of micronutrient intakes with the exception of potassium, vitamin C, folate and fibre. Overall nutrient inadequacies were noted for potassium, magnesium, vitamins D, E, &lt; K and fibre; and vitamin B12 and iron among females. Conclusions Among participants process/ultra-processed foods were the main sources of nutrients. Furthermore participants may be at risk for inadequate intakes of key nutrients. Our food policy needs to create an environment that fosters availability and consumption of nutrient- rather that energy-dense foods. Funding Sources Self funded.