Ground Force Precision Calibration Method for Customized Piezoresistance Sensing Flexible Force Measurement Mat.

A force plate is mainly used in biomechanics; it aims to measure the ground reaction force in a person's walking or standing position. In this study, a large-area force mat of the piezoresistance sensing type was developed, and a deep-learning-based weight measurement calibration method was applied to solve the problem in which measurements are not normalized because of physical limitations in hardware and signal processing. The test set was composed of the values measured at each point by weight and the value of the center of the pressure variable, and the measured value was predicted using a deep neural network (DNN) regression model. The calibration verification results show that the average weight errors range from a minimum of 0.06% to a maximum of 3.334%. This is simpler than the previous method, which directly measures the ratio of the resistance value to the measured weight of each sensor and derives an equation.

Saccadic Eye Movement in Mild Cognitive Impairment and Alzheimer's Disease: A Systematic Review and Meta-Analysis.

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.

Age-Related Changes in the Characteristics of the Elderly Females Using the Signal Features of an Earlobe Photoplethysmogram.

Non-invasive measurement of physiological parameters and indicators, specifically among the elderly, is of utmost importance for personal health monitoring. In this study, we focused on photoplethysmography (PPG), and developed a regression model that calculates variables from the second (SDPPG) and third (TDPPG) derivatives of the PPG pulse that can observe the inflection point of the pulse wave measured by a wearable PPG device. The PPG pulse at the earlobe was measured for 3 min in 84 elderly Korean women (age: 71.19 ± 6.97 years old). Based on the PPG-based cardiovascular function, we derived additional variables from TDPPG, in addition to the aging variable to predict the age. The Aging Index (AI) from SDPPG and Sum of TDPPG variables were calculated in the second and third differential forms of PPG. The variables that significantly correlated with age were c/a, Tac, AI of SDPPG, sum of TDPPG, and correlation coefficient 'r' of the model. In multiple linear regression analysis, the r value of the model was 0.308, and that using deep learning on the model was 0.839. Moreover, the possibility of improving the accuracy of the model using supervised deep learning techniques, rather than the addition of datasets, was confirmed.

Principal Characteristics of Affected and Unaffected Side Trunk Movement and Gait Event Parameters during Hemiplegic Stroke Gait with IMU Sensor.

This study describe the characteristics of hemiplegic stroke gait with principal component analysis (PCA) of trunk movement (TM) and gait event (GE) parameters by the inertial measurement unit (IMU) sensors: (1) Background: This process can determine dominant variables through multivariate examination to identify the affected, unaffected, and healthy lower-limb sides; (2) Methods: The study monitored forty patients with stroke and twenty-eight healthy individuals comprising the control group for comparison. The IMU sensors were attached to each subject while performing a 6 m walking test. Sixteen variables extracted from the measured data were divided into 7 GE and 9 TM variables explaining pelvis tilt, oblique, and rotation. (3) Results: The tilt range variables of the trunk movement on the affected and unaffected sides were lower than those of the healthy side; this showed between-group differences in various GE variables. For the healthy and affected sides, 80% of variances were explained with 2 or 3 PCs involving only a few dominant variables; and (4) Conclusions: The difference between each side leg should be considered during the development of a diagnosis method. This research can be utilized to develop functional assessment tools for personalized treatment and to design appropriate training protocols.

A compact pulsatile simulator based on cam-follower mechanism for generating radial pulse waveforms.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Hemodynamic changes caused by acupuncture in healthy volunteers: a prospective, single-arm exploratory clinical study.

BACKGROUND: Radial pressure pulse wave (RPPW) examination has been a key diagnostic component of traditional Chinese medicine. The objective of this study was to investigate the changes in RPPW along with various hemodynamic variables after acupuncture stimulation and to examine the validity of pulse diagnosis as a modern diagnostic tool. METHODS: We conducted acupuncture stimulation at both ST36 acupuncture points in 25 healthy volunteers. We simultaneously assessed the RPPW by pulse tonometry; heart rate variability (HRV) by electrocardiogram; photoplethysmogram (PPG) signals, respiration rate, peripheral blood flow velocity and arterial depth by ultrasonography; and cardiac output by impedance cardiography, before, during and after a session of acupuncture stimulation. RESULTS: We observed consistent patterns of increased spectral energy at low frequency (<10 Hz) and pulse power using RPPW examination and in the amplitude and systolic area of the PPG signal during the entire acupuncture session. The low- and high-frequency domains of HRV increased and decreased, respectively, during the acupuncture session. The peripheral blood velocity rose shortly after needle insertion, reached a maximum in the middle of the session and decreased afterwards. The augmentation index (AIX) and pulse transit time (PTT) obtained from RPPW did not change significantly. CONCLUSION: Acupuncture stimulation at ST36 in healthy subjects increased the peripheral pulse amplitudes (pressure pulse wave (PPW) and PPG), blood flow velocity (ultrasonography) and sympathetic nerve activity (HRV). The lack of changes in the AIX and PTT suggests that the increased pulse amplitudes and blood flow velocity may result from increased cardiac output. TRIAL REGISTRATION: Clinical Research Information Service ( KCT0001663 ).

Association of hypertension with physical factors of wrist pulse waves using a computational approach: a pilot study.

BACKGROUND: The objectives of this pilot study were to examine the association between hypertension and physical factors of wrist pulse waves to avoid subjective diagnoses in Traditional Chinese Medicine (TCM) and Traditional Korean Medicine (TKM). An additional objective was to assess the predictive power of individual and combined physical factors in order to identify the degree of agreement between diagnosis accuracies using physical factors and using a sphygmomanometer in the prediction of hypertension. METHODS: In total, 393 women aged 46 to 73 years participated in this study. Logistic regression (LR) and a naïve Bayes algorithm (NB) were used to assess statistically significant differences and the predictive power of hypertension, and a wrapper-based machine learning method was used to evaluate the predictive power of combinations of physical factors. RESULTS: In both wrists, L-PPI and R-PPI (maximum pulse amplitudes in the left Gwan and right Gwan) were the factors most strongly associated with hypertension after adjusting for age and body mass index (p = <0.001, odds ratio (OR) = 2.006 on the left and p = <0.001, OR = 2.504 on the right), and the best predictors (NB-AUC = 0.692, LR-AUC = 0.7 on the left and NB-AUC = 0.759, LR-AUC = 0.763 on the right). Analyses of both individual and combined physical factors revealed that the predictive power of the physical factors in the right wrist was higher than for the left wrist. The predictive powers of the combined physical factors were higher than those of the best single predictors in both the left and right wrists. CONCLUSION: We suggested new physical factors related to the sum of the area on the particular region of pulse waves in both wrists. L-PPI and R-PPI among all variables used in this study were good indicators of hypertension. Our findings support the quantification and objectification of pulse patterns and disease in TCM and TKM for complementary and alternative medicine.

Constitution-specific features of perspiration and skin visco-elasticity in SCM.

BACKGROUND: Human skin properties have been used as an important diagnostic component in traditional medicine as they change with health conditions. Sasang constitutional medicine (SCM) puts emphasis on the recognition of the constitution-specific skin features prior to the diagnostic decision of health. In this work, in search of skin-characteristics effectively reflecting SCM features, we compared several skin properties such as perspiration, visco-elasticity, elasticity, and elasticity hysteresis, in several candidate body parts. METHODS: We conducted a clinical study in which a total of 111 healthy females aged 50 - 70 years participated with their Sasang constitution (SC) types determined objectively by the Sasang constitutional analytic tool. Perspiration on the skin surface was estimated by using a capacitance sensor to measure the amount of moisture on the palm, forehead, and philtrum before and after a heating stimulus. We acquired the visco-elasticity, elasticity, and elasticity hysteresis at the forearm by Dermalab's elasticity sensing device. An analysis of covariance (ANCOVA) was conducted to evaluate the effect of SC on the nine skin features acquired. RESULTS: The visco-elasticity of the forearm of the Soeum-in (SE) group was significantly lower than that of the Taeeum-in (TE) group (F = 68.867, p < 0.001), whereas the elasticity hysteresis of the SE group was higher than that of the TE group (F = 10.364, p < 0.01). The TE group had more perspiration on the forehead than the SE group (F = 9.050, p < 0.01). The SE group had a large perspiration difference between the philtrum and the forehead compared with the TE group (F = 7.892, p < 0.01). CONCLUSIONS: We found four significant skin features that reflect the inherent constitutional attributes of the TE and SE groups in accordance with SCM literature; the visco-elasticity, elasticity hysteresis, perspiration on the forehead and philtrum. Our findings are based on a novel interpretation of the SCM literature and will contribute to developing the constitutional health status evaluation system in SCM.

Comparative efficacy of seven nonpharmacological interventions on global cognition in older adults with and without mild cognitive impairment: a network meta-analysis of randomized controlled trials.

To maintain current cognitive function and access greater cognitive reserves, nonpharmacological interventions may be a viable alternative for older adults with or without cognitive impairment. This study aimed to compare different nonpharmacological interventions for enhancing global cognition, including mind-body exercise, physical exercise, non-invasive brain stimulation, cognitive training intervention (CTI), acutherapy (ACU), meditation, and music therapy, by applying a network meta-analysis (NMA). Sixty-one randomized controlled trials evaluating the efficacy of interventions on global cognition in older adults with or without mild cognitive decline were selected. An NMA was conducted to compare the efficacy of different nonpharmacological interventions. The NMA revealed that mind-body exercise (standardized mean difference, 1.384; 95% confidence interval, 0.777-1.992); ACU (1.283; 0.478-2.088); meditation (0.910; 0.097-1.724); non-invasive brain stimulation (1.242; 0.254-2.230); CTI (1.269; 0.736-1.802); and physical exercise (0.977; 0.212-1.742), showed positive effects compared to passive controls. There were no significant differences between the efficacies of other interventions. Nonpharmacological interventions may potentially enhance and maintain global cognition through various pathways, such as memorizing movements and enhancing brain plasticity by reducing stress in the older adult population. Additional studies are needed to clarify the impact of other variables, including intervention methods or psychological variables.

Association of menopausal status with body composition and anthropometric indices in Korean women.

BACKGROUND: Menopause induces various health problems and is associated with obesity, but the association between menopausal status and obesity is unclear due to several confounding factors, such as aging and reduced physical activity. The objective of this study was to examine the association of menopausal status with anthropometric indices and body composition indices in South Korean women. METHODS: In this cross-sectional study, a total of 734 subjects (297 postmenopausal women, 437 premenopausal women) from five university hospitals in South Korea were included. A binary logistic regression analysis was performed to examine the association of menopause with anthropometric indices and body composition indices. RESULTS: Height, body mass index, waist-to-height ratio, waist-to-hip ratio, and neck, armpit, chest, rib, waist, iliac, and hip circumferences were associated with menopausal status in the crude analysis, but these associations disappeared in the adjusted models. Among the body composition indices, menopausal status was strongly associated with total body water, skeletal muscle mass, body fat mass, and body fat percentage in the crude analysis. However, the associations with body fat mass and body fat percentage disappeared in the adjusted models. Only the associations with total body water and skeletal muscle mass remained in the adjusted models. CONCLUSION: Most of the anthropometric indices and body composition indices were not associated with menopausal status, but total body water and skeletal muscle mass were significantly lower in postmenopausal women than in premenopausal women.

Prefrontal intra-individual ERP variability and its asymmetry: exploring its biomarker potential in mild cognitive impairment.

BACKGROUND: The worldwide trend of demographic aging highlights the progress made in healthcare, albeit with health challenges like Alzheimer's Disease (AD), prevalent in individuals aged 65 and above. Its early detection at the mild cognitive impairment (MCI) stage is crucial. Event-related potentials (ERPs) obtained by averaging EEG segments responded to repeated events are vital for cognitive impairment research. Consequently, examining intra-trial ERP variability is vital for comprehending fluctuations within psychophysiological processes of interest. This study aimed to investigate cognitive deficiencies and instability in MCI using ERP variability and its asymmetry from a prefrontal two-channel EEG device. METHODS: In this study, ERP variability for both target and non-target responses was examined using the response variance curve (RVC) in a sample comprising 481 participants with MCI and 1,043 age-matched healthy individuals. The participants engaged in auditory selective attention tasks. Cognitive decline was assessed using the Seoul Neuropsychological Screening Battery (SNSB) and the Mini-Mental State Examination (MMSE). The research employed various statistical methods, including independent t-tests, and univariate and multiple logistic regression analyses. These analyses were conducted to investigate group differences and explore the relationships between neuropsychological test results, ERP variability and its asymmetry measures, and the prevalence of MCI. RESULTS: Our results showed that patients with MCI exhibited unstable cognitive processing, characterized by increased ERP variability compared to cognitively normal (CN) adults. Multiple logistic regression analyses confirmed the association between ERP variability in the target and non-target responses with MCI prevalence, independent of demographic and neuropsychological factors. DISCUSSION: The unstable cognitive processing in the MCI group compared to the CN individuals implies abnormal neurological changes and reduced and (or) unstable attentional maintenance during cognitive processing. Consequently, utilizing ERP variability measures from a portable EEG device could serve as a valuable addition to the conventional ERP measures of latency and amplitude. This approach holds significant promise for identifying mild cognitive deficits and neural alterations in individuals with MCI.

A screening method for mild cognitive impairment in elderly individuals combining bioimpedance and MMSE.

We investigated a screening method for mild cognitive impairment (MCI) that combined bioimpedance features and the Korean Mini-Mental State Examination (K-MMSE) score. Data were collected from 539 subjects aged 60 years or older at the Gwangju Alzheimer's & Related Dementias (GARD) Cohort Research Center, A total of 470 participants were used for the analysis, including 318 normal controls and 152 MCI participants. We measured bioimpedance, K-MMSE, and the Seoul Neuropsychological Screening Battery (SNSB-II). We developed a multiple linear regression model to predict MCI by combining bioimpedance variables and K-MMSE total score and compared the model's accuracy with SNSB-II domain scores by the area under the receiver operating characteristic curve (AUROC). We additionally compared the model performance with several machine learning models such as extreme gradient boosting, random forest, support vector machine, and elastic net. To test the model performances, the dataset was divided into a training set (70%) and a test set (30%). The AUROC values of SNSB-II scores were 0.803 in both sexes, 0.840 for males, and 0.770 for females. In the combined model, the AUROC values were 0.790 (0.773) for males (and females), which were significantly higher than those from the model including MMSE scores alone (0.723 for males and 0.622 for females) or bioimpedance variables alone (0.640 for males and 0.615 for females). Furthermore, the accuracies of the combined model were comparable to those of machine learning models. The bioimpedance-MMSE combined model effectively distinguished the MCI participants and suggests a technique for rapid and improved screening of the elderly population at risk of cognitive impairment.

Development of a radial pulse tonometric (RPT) sensor with a temperature compensation mechanism.

Several RPT sensors have been developed to acquire objective and quantitative pulse waves. These sensors offer improved performance with respect to pressure calibration, size and sensor deployment, but not temperature. Since most pressure sensors are sensitive to temperature, various temperature compensation techniques have been developed, but these techniques are largely inapplicable to RPT sensors due to the size restrictions of the sensor, and incompatibility between the compensation techniques and the RPT sensor. Consequently, in this paper a new RPT sensor comprising six piezoresistive pressure sensors and one thermistor has been developed through finite element analysis and then a suitable temperature compensation technique has been proposed. This technique compensates for temperature variations by using the thermistor and simple compensation equations. As verification of the proposed compensation technique, pulse waves of all types were successfully compensated for temperature changes.

Eye movement changes as an indicator of mild cognitive impairment.

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.

Reduced body cell mass and functions in lower extremities are associated with mild cognitive impairment and Alzheimer's dementia.

This study examined the alterations of segmental body composition in individuals with Alzheimer's pathology (AD), including mild cognitive impairment (MCI) and dementia. A multifrequency bioimpedance analysis (BIA) was used to provide segmental water and impedance variables from 365 cognitively normal (CN), 123 MCI due to AD, and 30 AD dementia participants. We compared the BIA variables between the three groups, examined their correlations with neuropsychological screening test scores, and illustrate their 95% confidence RXc graphs. AD dementia participants were older, more depressive, and had worse cognitive abilities than MCI due to AD and CN participants. Although the BIA variables showed weak partial correlations with the cognitive test scores, we found patterns of an increasing water content in lean mass, increasing extra to intracellular water ratio, and decreasing reactance and phase angle in the lower extremities with effect sizes ranging from 0.26 to 0.51 in the groups of MCI and dementia due to AD compared with CN individuals. The RXc graphs upheld the findings with a significant displacement downward and toward the right, dominantly in the lower extremities. Individuals with AD pathology exhibit a reduced body cell mass or cell strength, an abnormal cellular water distribution, and an overhydration status in lean mass, especially in the lower extremities.

Corrigendum: Prefrontal event-related potential markers in association with mild cognitive impairment.

[This corrects the article DOI: 10.3389/fnagi.2023.1273008.].

Saccadic eye movement variables as biomarkers for cognitive decline in elderly individuals.

Alzheimer's disease (AD) is the leading cause of Dementia, and mild cognitive impairment (MCI) is often considered a precursor to the development of AD dementia and other types of Dementia. Biomarkers such as amyloid beta are specific and sensitive in identifying AD and can identify individuals who have biological evidence of the disease but have no symptoms, but clinicians and researchers may not easily use them on a large scale. Ocular biomarkers, such as those obtained through eye tracking (ET) technology, have the potential as a diagnostic tool due to their accuracy, affordability, and ease of use. In this study, we show that eye movement (EM) metrics from an interleaved Pro/Anti-saccade (PS/AS) ET task can differentiate between cognitively normal (CN) and MCI subjects and that the presence of Aß brain deposits, a biomarker of AD, significantly affects performance on these tasks. Individuals with Aß deposits (Aß+) performed worse than those without (Aß-). Our findings suggest that eye-tracking measurements may be a valuable tool for detecting amyloid brain pathology and monitoring changes in cognitive function in CN and MCI individuals over time.Clinical Relevance- The PS/AS paradigm, which measures saccadic eye movements, can accurately detect subtle cognitive impairments and changes in the brain associated with Alzheimer's disease in CN and MCI individuals. This makes it a valuable tool for identifying individuals at risk for cognitive decline and tracking changes in cognitive function over time.

Prefrontal event-related potential markers in association with mild cognitive impairment.

Background: Alzheimer's disease (AD) is among the leading contributors of dementia globally with approximately 60-70% of its cases. Current research is focused on the mild cognitive impairment (MCI), which is associated with cognitive decline but does not disrupt routine activities. Event-related potential (ERP) research is essential in screening patients with MCI. Low-density channel electroencephalography (EEG) is frequently used due to its convenience, portability, and affordability, making it suitable for resource-constrained environments. Despite extensive research on neural biomarkers for cognitive impairment, there is a considerable gap in understanding the effects on early stages of cognitive processes, particularly when combining physiological and cognitive markers using portable devices. The present study aimed to examine cognitive shortfalls and behavioral changes in patients with MCI using prefrontal selective attention ERP recorded from a prefrontal two-channel EEG device. Methods: We assessed cognitive decline using the Mini-Mental State Examination (MMSE) and the Seoul Neuropsychological Screening Battery (SNSB). We administered auditory selective attention tasks to 598 elderly participants, including those with MCI (160) and cognitively normal (CN) individuals (407). We conducted statistical analyses such as independent t-tests, Pearson's correlations, and univariate and multiple logistic regression analyses to assess group differences and associations between neuropsychological tests, ERP measures, behavioral measures, and MCI prevalence. Results: Our findings revealed that patients with MCI demonstrated slower information-processing abilities, and exhibited poorer task execution, characterized by reduced accuracy, increased errors, and higher variability in response time, compared to CN adults. Multiple logistic regression analyses confirmed the association between some ERP and behavioral measures with MCI prevalence, independent of demographic and neuropsychological factors. A relationship was observed between neuropsychological scores, ERP, and behavioral measures. Discussion: The slower information processing abilities, and poor task execution in the MCI group compared to the CN individuals suggests flawed neurological changes and reduced attentional maintenance during cognitive processing, respectively. Hence, the utilization of portable EEG devices to capture prefrontal selective attention ERPs, in combination with behavioral assessments, holds promise for the identification of mild cognitive deficits and neural alterations in individuals with MCI. This approach could potentially augment the traditional neuropsychological tests during clinical screening for MCI.

Prefrontal EEG slowing, synchronization, and ERP peak latency in association with predementia stages of Alzheimer's disease.

Background: Early screening of elderly individuals who are at risk of dementia allows timely medical interventions to prevent disease progression. The portable and low-cost electroencephalography (EEG) technique has the potential to serve it. Objective: We examined prefrontal EEG and event-related potential (ERP) variables in association with the predementia stages of Alzheimer's disease (AD). Methods: One hundred elderly individuals were recruited from the GARD cohort. The participants were classified into four groups according to their amyloid beta deposition (A+ or A-) and neurodegeneration status (N+ or N-): cognitively normal (CN; A-N-, n = 27), asymptomatic AD (aAD; A + N-, n = 15), mild cognitive impairment (MCI) with AD pathology (pAD; A+N+, n = 16), and MCI with non-AD pathology (MCI(-); A-N+, n = 42). Prefrontal resting-state eyes-closed EEG measurements were recorded for five minutes and auditory ERP measurements were recorded for 8 min. Three variables of median frequency (MDF), spectrum triangular index (STI), and positive-peak latency (PPL) were employed to reflect EEG slowing, temporal synchrony, and ERP latency, respectively. Results: Decreasing prefrontal MDF and increasing PPL were observed in the MCI with AD pathology. Interestingly, after controlling for age, sex, and education, we found a significant negative association between MDF and the aAD and pAD stages with an odds ratio (OR) of 0.58. Similarly, PPL exhibited a significant positive association with these AD stages with an OR of 2.36. Additionally, compared with the MCI(-) group, significant negative associations were demonstrated by the aAD group with STI and those in the pAD group with MDF with ORs of 0.30 and 0.42, respectively. Conclusion: Slow intrinsic EEG oscillation is associated with MCI due to AD, and a delayed ERP peak latency is likely associated with general cognitive impairment. MCI individuals without AD pathology exhibited better cortical temporal synchronization and faster EEG oscillations than those with aAD or pAD. Significance: The EEG/ERP variables obtained from prefrontal EEG techniques are associated with early cognitive impairment due to AD and non-AD pathology. This result suggests that prefrontal EEG/ERP metrics may serve as useful indicators to screen elderly individuals' early stages on the AD continuum as well as overall cognitive impairment.

Novel diagnostic model for the deficient and excess pulse qualities.

The deficient and excess pulse qualities (DEPs) are the two representatives of the deficiency and excess syndromes, respectively. Despite its importance in the objectification of pulse diagnosis, a reliable classification model for the DEPs has not been reported to date. In this work, we propose a classification method for the DEPs based on a clinical study. First, through factor analysis and Fisher's discriminant analysis, we show that all the pulse amplitudes obtained at various applied pressures at Chon, Gwan, and Cheok contribute on equal orders of magnitude in the determination of the DEPs. Then, we discuss that the pulse pressure or the average pulse amplitude is appropriate for describing the collective behaviors of the pulse amplitudes and a simple and reliable classification can be constructed from either quantity. Finally, we propose an enhanced classification model that combines the two complementary variables sequentially.