There exists a growing need for a cost-effective, reliable, and portable pulsation simulator that can generate a wide variety of pulses depending on age and cardiovascular disease. For constructing compact pulsation simulator, this study proposes to use a pneumatic actuator based on cam-follower mechanism controlled by a DC motor. The simulator is intended to generate pulse waveforms for a range of pulse pressures and heart beats that are realistic to human blood pulsations. This study first performed in vivo testing of a healthy young man to collect his pulse waveforms using a robotic tonometry system (RTS). Based on the collected data a representative human radial pulse waveform is obtained by conducting a mathematical analysis. This standard pulse waveform is then used to design the cam profile. Upon fabrication of the cam, the pulsatile simulator, consisting of the pulse pressure generating component, pressure and heart rate adjusting units, and the real-time pulse display, is constructed. Using the RTS, a series of testing was performed on the prototype to collect its pulse waveforms by varying the pressure levels and heart rates. Followed by the testing, the pulse waveforms generated by the prototype are compared with the representative, in vivo, pulse waveform. The radial Augmentation Index analysis results show that the percent error between the simulator data and human pulse profiles is sufficiently small, indicating that the first two peak pressures agree well. Moreover, the phase analysis results show that the phase delay errors between the pulse waveforms of the prototype and the representative waveform are adequately small, confirming that the prototype simulator is capable of simulating realistic human pulse waveforms. This study demonstrated that a very accurate radial pressure waveform can be reproduced using the cam-based simulator. It can be concluded that the same testing and design methods can be used to generate pulse waveforms for other age groups or any target pulse waveforms. Such a simulator can make a contribution to the research efforts, such as development of wearable pressure sensors, standardization of pulse diagnosis in oriental medicine, and training medical professionals for pulse diagnosis techniques.

Alzheimer’s disease (AD) is the leading cause of dementia, and mild cognitive impairment (MCI) is considered the transitional state to AD dementia (ADD) and other types of dementia, whose symptoms are accompanied by altered eye movement. In this work, we reviewed the existing literature and conducted a meta-analysis to extract relevant eye movement parameters that are significantly altered owing to ADD and MCI. We conducted a systematic review of 35 eligible original publications in saccade paradigms and a meta-analysis of 27 articles with specified task conditions, which used mainly gap and overlap conditions in both prosaccade and antisaccade paradigms. The meta-analysis revealed that prosaccade and antisaccade latencies and frequency of antisaccade errors showed significant alterations for both MCI and ADD. First, both prosaccade and antisaccade paradigms differentiated patients with ADD and MCI from controls, however, antisaccade paradigms was more effective than prosaccade paradigms in distinguishing patients from controls. Second, during prosaccade in the gap and overlap conditions, patients with ADD had significantly longer latencies than patients with MCI, and the trend was similar during antisaccade in the gap condition as patients with ADD had significantly more errors than patients with MCI. The anti-effect magnitude was similar between controls and patients, and the magnitude of the latency of the gap effect varied among healthy controls and MCI and ADD subjects, but the effect size of the latency remained large in both patients. These findings suggest that, using gap effect, anti-effect, and specific choices of saccade paradigms and conditions, distinctions could be made between MCI and ADD patients as well as between patients and controls.

We investigated whether cognitive decline could be explained by resting-state electroencephalography (EEG) biomarkers measured in prefrontal regions that reflect the slowing of intrinsic EEG oscillations. In an aged population dwelling in a rural community (total = 496, males = 165, females = 331), we estimated the global cognitive decline using the Mini-Mental State Examination (MMSE) and measured resting-state EEG parameters at the prefrontal regions of Fp1 and Fp2 in an eyes-closed state. Using a tertile split method, the subjects were classified as T3 (MMSE 28–30, N = 162), T2 (MMSE 25–27, N = 179), or T1 (MMSE ≤ 24, N = 155). The EEG slowing biomarkers of the median frequency, peak frequency and alpha-to-theta ratio decreased as the MMSE scores decreased from T2 to T1 for both sexes (−5.19 ≤ t-value ≤ −3.41 for males and −7.24 ≤ t-value ≤ −4.43 for females) after adjusting for age and education level. Using a double cross-validation procedure, we developed a prediction model for the MMSE scores using the EEG slowing biomarkers and demographic covariates of sex, age and education level. The maximum intraclass correlation coefficient between the MMSE scores and model-predicted values was 0.757 with RMSE = 2.685. The resting-state EEG biomarkers showed significant changes in people with early cognitive decline and correlated well with the MMSE scores. Resting-state EEG slowing measured in the prefrontal regions may be useful for the screening and follow-up of global cognitive decline in elderly individuals.

We investigated segmental phase angles (PAs) in the four limbs using a multi-frequency bioimpedance analysis (MF-BIA) technique for noninvasively diagnosing diabetes mellitus. We conducted a meal tolerance test (MTT) for 45 diabetic and 45 control subjects stratified by age, sex and body mass index (BMI). HbA1c and the waist-to-hip-circumference ratio (WHR) were measured before meal intake, and we measured the glucose levels and MF-BIA PAs 5 times for 2 hours after meal intake. We employed a t-test to examine the statistical significance and the area under the curve (AUC) of the receiver operating characteristics (ROC) to test the classification accuracy using segmental PAs at 5, 50, and 250 kHz. Segmental PAs were independent of the HbA1c or glucose levels, or their changes caused by the MTT. However, the segmental PAs were good indicators for noninvasively screening diabetes In particular, leg PAs in females and arm PAs in males showed best classification accuracy (AUC = 0.827 for males, AUC = 0.845 for females). Lastly, we introduced the PA at maximum reactance (PAmax), which is independent of measurement frequencies and can be obtained from any MF-BIA device using a Cole-Cole model, thus showing potential as a useful biomarker for diabetes.

Objective: To examine whether prefrontal electroencephalography (EEG) can be used for screening dementia.Methods: We estimated the global cognitive decline using the results of Mini-Mental Status Examination (MMSE), measurements of brain activity from resting-state EEG, responses elicited by auditory stimulation [sensory event-related potential (ERP)], and selective attention tasks (selective-attention ERP) from 122 elderly participants (dementia, 35; control, 87). We investigated that the association between MMSE and each EEG/ERP variable by using Pearson’s correlation coefficient and performing univariate linear regression analysis. Kernel density estimation was used to examine the distribution of each EEG/ERP variable in the dementia and non-dementia groups. Both Univariate and multiple logistic regression analyses with the estimated odds ratios were conducted to assess the associations between the EEG/ERP variables and dementia prevalence. To develop the predictive models, five-fold cross-validation was applied to multiple classification algorithms.Results: Most prefrontal EEG/ERP variables, previously known to be associated with cognitive decline, show correlations with the MMSE score (strongest correlation has |r| = 0.68). Although variables such as the frontal asymmetry of the resting-state EEG are not well correlated with the MMSE score, they indicate risk factors for dementia. The selective-attention ERP and resting-state EEG variables outperform the MMSE scores in dementia prediction (areas under the receiver operating characteristic curve of 0.891, 0.824, and 0.803, respectively). In addition, combining EEG/ERP variables and MMSE scores improves the model predictive performance, whereas adding demographic risk factors do not improve the prediction accuracy.Conclusion: Prefrontal EEG markers outperform MMSE scores in predicting dementia, and additional prediction accuracy is expected when combining them with MMSE scores.Significance: Prefrontal EEG is effective for screening dementia when used independently or in combination with MMSE.

Background: Early identification of patients at risk of dementia, alongside timely medical intervention, can prevent disease progression. Despite their potential clinical utility, the application of diagnostic tools, such as neuropsychological assessments and neuroimaging biomarkers, is hindered by their high cost and time-consuming administration, rendering them impractical for widespread implementation in the general population. We aimed to develop non-invasive and cost-effective classification models for predicting mild cognitive impairment (MCI) using eye movement (EM) data.Methods: We collected eye-tracking (ET) data from 594 subjects, 428 cognitively normal controls, and 166 patients with MCI while they performed prosaccade/antisaccade and go/no-go tasks. Logistic regression (LR) was used to calculate the EM metrics’ odds ratios (ORs). We then used machine learning models to construct classification models using EM metrics, demographic characteristics, and brief cognitive screening test scores. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUROC).Results: LR models revealed that several EM metrics are significantly associated with increased odds of MCI, with odds ratios ranging from 1.213 to 1.621. The AUROC scores for models utilizing demographic information and either EM metrics or MMSE were 0.752 and 0.767, respectively. Combining all features, including demographic, MMSE, and EM, notably resulted in the best-performing model, which achieved an AUROC of 0.840.Conclusion: Changes in EM metrics linked with MCI are associated with attentional and executive function deficits. EM metrics combined with demographics and cognitive test scores enhance MCI prediction, making it a non-invasive, cost-effective method to identify early stages of cognitive decline.