The prevalence of neurodegenerative diseases (NDD) has grown rapidly in recent years and NDD screening receives much attention. NDD could cause gait abnormalities so that to screen NDD using gait signal is feasible. The research aim of this study is to develop an NDD classification algorithm via gait force (GF) using multiscale sample entropy (MSE) and machine learning models. The Physionet NDD gait database is utilized to validate the proposed algorithm. In the preprocessing stage of the proposed algorithm, new signals were generated by taking one and two times of differential on GF and are divided into various time windows (10/20/30/60-sec). In feature extraction, the GF signal is used to calculate statistical and MSE values. Owing to the imbalanced nature of the Physionet NDD gait database, the synthetic minority oversampling technique (SMOTE) was used to rebalance data of each class. Support vector machine (SVM) and k-nearest neighbors (KNN) were used as the classifiers. The best classification accuracies for the healthy controls (HC) vs. Parkinson’s disease (PD), HC vs. Huntington’s disease (HD), HC vs. amyotrophic lateral sclerosis (ALS), PD vs. HD, PD vs. ALS, HD vs. ALS, HC vs. PD vs. HD vs. ALS, were 99.90%, 99.80%, 100%, 99.75%, 99.90%, 99.55%, and 99.68% under 10-sec time window with KNN. This study successfully developed an NDD gait classification based on MSE and machine learning classifiers.

Background: A higher risk of developing dementia is observed in patients with atrial fibrillation (AF). Results are inconsistent regarding the risk of dementia when patients with AF use different anticoagulants. We aimed to investigate the risk of dementia in patients with AF receiving non‐vitamin K antagonist oral anticoagulants (NOACs) compared with those receiving warfarin. Methods and Results: We conducted a nationwide population‐based cohort study of incident cases using the Taiwan National Health Insurance Research Database. We initially enlisted all incident cases of AF and then selected those treated with either NOACs or warfarin for at least 90 days between 2012 and 2016. First‐ever diagnosis of dementia was the primary outcome. We performed propensity score matching to minimize the difference between each cohort. We used the Fine and Gray competing risk regression model to calculate the hazard ratio (HR) for dementia. We recruited 12 068 patients with AF (6034 patients in each cohort). The mean follow‐up time was 3.27 and 3.08 years in the groups using NOACs and warfarin, respectively. Compared with the HR for the group using warfarin, the HR for dementia was 0.82 (95% CI, 0.73–0.92; P =0.0004) in the group using NOACs. Subgroup analysis demonstrated that users of NOAC aged 65 to 74 years, with a high risk of stroke or bleeding were associated with a lower risk of dementia than users of warfarin with similar characteristics. Conclusions: Patients with AF using NOACs were associated with a lower risk of dementia than those using warfarin. Further randomized clinical trials are greatly needed to prove these findings.

Using 1000 successive points of a pulse wave velocity (PWV) series, we previously distinguished healthy from diabetic subjects with multi-scale entropy (MSE) using a scale factor of 10. One major limitation is the long time for data acquisition (i.e., 20 min). This study aimed at validating the sensitivity of a novel method, short time MSE (sMSE) that utilized a substantially smaller sample size (i.e., 600 consecutive points), in differentiating the complexity of PWV signals both in simulation and in human subjects that were divided into four groups: healthy young (Group 1; n = 24) and middle-aged (Group 2; n = 30) subjects without known cardiovascular disease and middle-aged individuals with well-controlled (Group 3; n = 18) and poorly-controlled (Group 4; n = 22) diabetes mellitus type 2. The results demonstrated that although conventional MSE could differentiate the subjects using 1000 consecutive PWV series points, sensitivity was lost using only 600 points. Simulation study revealed consistent results. By contrast, the novel sMSE method produced significant differences in entropy in both simulation and testing subjects. In conclusion, this study demonstrated that using a novel sMSE approach for PWV analysis, the time for data acquisition can be substantially reduced to that required for 600 cardiac cycles (~10 min) with remarkable preservation of sensitivity in differentiating among healthy, aged, and diabetic populations.

Physiological signals often show complex fluctuation (CF) under the dual influence of temporal and spatial scales, and CF can be used to assess the health of physiologic systems in the human body. This study applied multiscale cross-approximate entropy (MC-ApEn) to quantify the complex fluctuation between R-R intervals series and photoplethysmography amplitude series. All subjects were then divided into the following two groups: healthy upper middle-aged subjects (Group 1, age range: 41–80 years,n=27) and upper middle-aged subjects with type 2 diabetes (Group 2, age range: 41–80 years,n=24). There are significant differences of heart rate variability, LHR, between Groups 1 and 2 (1.94±1.21versus1.32±1.00,P=0.031). Results demonstrated differences in sum of large scale MC-ApEn (MC-ApEnLS) (5.32±0.50versus4.74±0.78,P=0.003). This parameter has a good agreement with pulse-pulse interval and pulse amplitude ratio (PAR), a simplified assessment for baroreflex activity. In conclusion, this study employed the MC-ApEn method, integrating multiple temporal and spatial scales, to quantify the complex interaction between the two physical signals. The MC-ApEnLSparameter could accurately reflect disease process in diabetics and might be another way for assessing the autonomic nerve function.

Complex fluctuations within physiological signals can be used to evaluate the health of the human body. This study recruited four groups of subjects: young healthy subjects (Group 1,n=32), healthy upper middle-aged subjects (Group 2,n=36), subjects with well-controlled type 2 diabetes (Group 3,n=31), and subjects with poorly controlled type 2 diabetes (Group 4,n=24). Data acquisition for each participant lasted 30 minutes. We obtained data related to consecutive time series with R-R interval (RRI) and pulse transit time (PTT). Using multiscale cross-approximate entropy (MCE), we quantified the complexity between the two series and thereby differentiated the influence of age and diabetes on the complexity of physiological signals. This study used MCE in the quantification of complexity between RRI and PTT time series. We observed changes in the influences of age and disease on the coupling effects between the heart and blood vessels in the cardiovascular system, which reduced the complexity between RRI and PTT series.