BackgroundThis paper suggests a method to assess the extent to which ultra-short Heart Rate Variability (HRV) features (less than 5 min) can be considered as valid surrogates of short HRV features (nominally 5 min). Short term HRV analysis has been widely investigated for mental stress assessment, whereas the validity of ultra-short HRV features remains unclear. Therefore, this study proposes a method to explore the extent to which HRV excerpts can be shortened without losing their ability to automatically detect mental stress.MethodsECGs were acquired from 42 healthy subjects during a university examination and resting condition. 23 features were extracted from HRV excerpts of different lengths (i.e., 30 s, 1 min, 2 min, 3 min, and 5 min). Significant differences between rest and stress phases were investigated using non-parametric statistical tests at different time-scales. Features extracted from each ultra-short length were compared with the standard short HRV features, assumed as the benchmark, via Spearman’s rank correlation analysis and Bland-Altman plots during rest and stress phases. Using data-driven machine learning approaches, a model aiming to detect mental stress was trained, validated and tested using short HRV features, and assessed on the ultra-short HRV features.ResultsSix out of 23 ultra-short HRV features (MeanNN, StdNN, MeanHR, StdHR, HF, and SD2) displayed consistency across all of the excerpt lengths (i.e., from 5 to 1 min) and 3 out of those 6 ultra-short HRV features (MeanNN, StdHR, and HF) achieved good performance (accuracy above 88%) when employed in a well-dimensioned automatic classifier.ConclusionThis study concluded that 6 ultra-short HRV features are valid surrogates of short HRV features for mental stress investigation.Electronic supplementary materialThe online version of this article (10.1186/s12911-019-0742-y) contains supplementary material, which is available to authorized users.

Wearable inertial sensors have been widely investigated for fall risk assessment and prediction in older adults. However, heterogeneity in published studies in terms of sensor location, task assessed and features extracted is high, making challenging evidence-based design of new studies and/or real-life applications. We conducted a systematic review and meta-analysis to appraise the best available evidence in the field. Namely, we applied established statistical methods for the analysis of categorical data to identify optimal combinations of sensor locations, tasks, and feature categories. We also conducted a meta-analysis on sensor-based features to identify a set of significant features and their pivot values. The results demonstrated that with a walking test, the most effective feature to assess the risk of falling was the velocity with the sensor placed on the shins. Conversely, during quite standing, linear acceleration measured at the lower back was the most effective combination of feature-placement. Similarly, during the sit-to-stand and/or the stand-to-sit tests, linear acceleration measured at the lower back seems to be the most effective feature-placement combination. The meta-analysis demonstrated that four features resulted significantly higher in fallers: the root-mean-square acceleration in the mediolateral direction during quiet standing with eyes closed [Mean Difference (MD): 0.01 g; 95% Confidence Interval (CI95%): 0.006 to 0.014]; the number of steps (MD: 1.638 steps; CI95%: 0.384 to 2.892) and total time (MD: 2.274 seconds; CI95%: 0.531 to 4.017) to complete the timed up and go test; and the step time (MD: 0.053; CI95%: 0.012 to 0.095; p = 0.01) during walking.

BackgroundApproximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m, r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups.MethodsA public dataset of COP time-series was used. ApEn and SampEn were calculated for m = {2, 3, 4, 5}, r = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n = 85), and older adults (age ≥ 60) with (n = 18) and without (n = 56) falls in the last year. The effects of changing parameters m, r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m, r and N; between-subject factor: group). Specific combinations of m, r and N producing significant differences between groups were identified using the Tukey’s honest significant difference procedure.ResultsA significant three-way interaction between m, r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (N = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r, highlighting the importance of an adequate selection of input parameters.ConclusionsFuture studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.Electronic supplementary materialThe online version of this article (10.1186/s12984-018-0465-9) contains supplementary material, which is available to authorized users.

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Diabetes Mellitus is a severe, chronic disease that occurs when blood glucose levels rise above certain limits. Over the last years, machine and deep learning techniques have been used to predict diabetes and its complications. However, researchers and developers still face two main challenges when building type 2 diabetes predictive models. First, there is considerable heterogeneity in previous studies regarding techniques used, making it challenging to identify the optimal one. Second, there is a lack of transparency about the features used in the models, which reduces their interpretability. This systematic review aimed at providing answers to the above challenges. The review followed the PRISMA methodology primarily, enriched with the one proposed by Keele and Durham Universities. Ninety studies were included, and the type of model, complementary techniques, dataset, and performance parameters reported were extracted. Eighteen different types of models were compared, with tree-based algorithms showing top performances. Deep Neural Networks proved suboptimal, despite their ability to deal with big and dirty data. Balancing data and feature selection techniques proved helpful to increase the model’s efficiency. Models trained on tidy datasets achieved almost perfect models.

Acute sleep deprivation is known to affect human balance and posture control. However, the effects of variations in sleep quality and pattern over consecutive days have received less attention. This study investigated the associations between day-to-day variations in sleep quality and standing balance in healthy subjects. Twenty volunteers (12 females and 8 males; age: 28.8 ± 5.7 years, body mass index: 23.4 ± 3.4 kg/m2, resting heart rate: 63.1 ± 8.7 bpm) with no history of sleep disorders or balance impairments participated in the study. Sleep and balance were assessed over two consecutive days. Sleep quality variations were assessed using sleep diary, actigraphy and heart rate variability (HRV) measures. Sleep was monitored at home, using an unobtrusive wearable device. Balance was assessed in a gait lab using foot centre of pressure (COP) displacement during quiet standing. Subjects with a day-to-day deterioration in sleep quantity and quality (i.e., decreased duration and increased fragmentation, increased nocturnal activity and decreased HRV) exhibited significant changes in balance (i.e., larger COP area, amplitude and standard deviation). Conversely, subjects with no significant alterations in sleep quantity and quality showed no significant changes in COP displacements. These results confirmed our hypothesis that changes in sleep quality and pattern over consecutive days may affect balance.

Abstract-Mental stress is one of the first causes of cognitive dysfunctions, cardiovascular disorders and depression. In addition, it reduces performances, on the work place and in daily life. The diffusion of wearable sensors (embedded in smart-watches, phones, etc.) has opened up the potential to assess mental stress detection through ultra-short term Heart Rate Variability (HRV) analysis (i.e., less than 5 min). Although informative analyses of features coming from short HRV (i.e., 5 min) have already been performed, the reliability of ultra-short HRV remains unclear. This study aims to tackle this gap by departing from a systematic review of the existing literature and investigating, in healthy subjects, the associations between acute mental stress and short/ultra-short term HRV features in time, frequency, and non-linear domains. Building on these findings, three experiments were carried out to empirically assess the usefulness of HRV for mental stress detection using ultra-short term analysis and wearable devices. Experiment 1 detected mental stress in a real life situation by exploring to which extent HRV excerpts can be shortened without losing their ability to detect mental stress. This allowed us to advance a method to explore to what extent ultra-short HRV features can be considered as good surrogates of 5 min HRV features. Experiment 2 and 3 sought to develop automatic classifiers to detect mental stress through 2 min HRV excerpts, by using a Stroop Color Word Test (CWT) and a highly paced video game, which are two common laboratory-based stressors.Results from experiment 1 demonstrated that 7 ultrashort HRV features can be considered as good surrogates of short HRV features in detecting mental stress in real life. By leveraging these 7 features, experiment 2 and 3 offered an automatic classifier detecting mental stress with ultrashort features (2min), achieving sensitivity, specificity and accuracy rate above 60%.

Sexually transmitted diseases are a serious threat to the public health. Indeed, when an individual seeks medical treatment for a sexually transmitted disease, health authorities frequently attempt to identify, procure, and treat that individual's sexual contact(s). We conducted a comparative analysis of three alternative approaches to tracing the sexual partners of individuals diagnosed as having a sexually transmitted disease. The first approach involved counseling individuals (n = 27) infected with either gonorrhea or nongonococcal urethritis and exhorting them to procure their sexual partners for treatment. In addition to counseling, the second and third approaches involved distributing "occasion cards" for patients to use when informing sexual contacts of the need for treatment. Moreover, in the second approach, the counselor (a nurse or physician) informed infected patients (n = 19) that they and their partners could waive the $3 clinic fee contingent upon the partners seeking treatment within 1 week. In the third approach, the counselor asked infected persons (n = 19) to accept a follow-up telephone contact if their sexual partners failed to seek treatment within 1 week. The third approach was most effective. Ninety percent of the partners identified through this approach sought treatment, versus only about 60% of the partners in the other two conditions. The third approach was also the least expensive, costing about $2.95 to procure each partner for treatment.