A marked increase in the population aged 60 years and over is evident; the proportion of the older adult population will rise 18.6% in 2025. On the other hand, obesity, metabolic syndrome (MS), diabetes and insulin resistance (or low insulin sensitivity-IS) are diseases related to lifestyle, they have become a social and public health problem. IS is the ability of cells to react due to insulin´s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. Studies show that IS decreases with age, though no one knows exactly if it is directly due to aging or changes in muscle mass. IS can be determined using direct or indirect methods. This paper aims to propose an insulin sensitivity method design from anthropometries and lipid measures. The methodology consist in a simple correspondence analysis for determine the variables, and a parametrical optimization using Avignon method as optimal function. The database used is composed by 120 Ecuadorian older adults with and without MS. The results show that the proposed optimized method got a better correlation with Avignon compared to non-optimized method. The proposed method could discriminate between subjects with and without IR and with and without MS. This is an important contribution since other methods like HOMA-IR, which is the most used in clinical practice, cannot find these differences, this means that HOMA-IR is not sensitive for IS estimation in elderly people. Future works will focus in the determination of cutoffs for insulin resistance diagnosis in the proposed method.
This paper reports a comparison between three fetal ECG (fECG) detectors developed during the CinC 2013 challenge for fECG detection. Algorithm A1 is based on Independent Component Analysis, A2 is based on fECG detection of RS Slope and A3 is based on Expectation-Weighted Estimation of Fiducial Points. The proposed methodology was validated using the annotated database available for the challenge. Each detector was characterized in terms of its performance by using measures of sensitivity, (Se), positive predictive value (P+) and delay time (t<sub>d</sub>). Additionally, the database was contaminated with white noise for two SNR conditions. Decision fusion was tested considering the most common types of combination of detectors. Results show that the decision fusion of A1 and A2 improves fQRS detection, maintaining high Se and P+ even under low SNR conditions without a significant t<sub>d </sub>increase.
Insulin sensitivity (IS) is the ability of cells to react due to insulin´s presence; when this ability is diminished, low insulin sensitivity or insulin resistance (IR) is considered. IR had been related to other metabolic disorders as metabolic syndrome (MS), obesity, dyslipidemia and diabetes. IS can be determined using direct or indirect methods. The indirect methods are less accurate and invasive than direct and they use glucose and insulin values from oral glucose tolerance test (OGTT). The accuracy is established by comparison using spearman rank correlation coefficient between direct and indirect method. This paper aims to propose a lipid-anthropometric index which offers acceptable correlation to insulin sensitivity index for different populations (DB1=MS subjects, DB2=sedentary without MS subjects and DB3=marathoners subjects) without to use OGTT glucose and insulin values. The proposed method is parametrically optimized through a random cross-validation, using the spearman rank correlation as comparator with CAUMO method. CAUMO is an indirect method designed from a simplification of the minimal model intravenous glucose tolerance test direct method (MINMOD-IGTT) and with acceptable correlation (0.89). The results show that the proposed optimized method got a better correlation with CAUMO in all populations compared to non-optimized. On the other hand, it was observed that the optimized method has better correlation with CAUMO in DB2 and DB3 groups than HOMA-IR method, which is the most widely used for diagnosing insulin resistance. The optimized propose method could detect incipient insulin resistance, when classify as insulin resistant subjects that present impaired postprandial insulin and glucose values.
This paper reports an automatic method for characterizing the quality of the RR-time series in the stress test database known as DICARDIA. The proposed methodology is simple and consists in subdividing the RR time series in a set of windows for estimating the quantity of artifacts based on a threshold value that depends on the standard deviation of RR-time series for each recorded lead. In a first stage, a manual annotation was performed considering four quality classes for the RR-time series (Reference lead, Good Lead, Low Quality Lead and Useless Lead). Automatic annotation was then performed varying the number of windows and threshold value for the standard deviation of the RR-time series. The metric used for evaluating the quality of the annotation was the Matching Ratio. The best results were obtained using a higher number of windows and considering only three classes (Good Lead, Low Quality Lead and Useless). The proposed methodology allows the utilization of the online available DICARDIA Stress Test database for different types of research.
Physiological signals are commonly the result of complex interactions between systems and organs, these interactions lead to signals that exhibit a non-stationary behaviour. For cardiac signals, non-stationary heart rate variability (HRV) may produce misinterpretations. A previous work proposed to divide a non-stationary signal into stationary segments by looking for changes in the signal’s properties related to changes in the mean of the signal. In this paper, we extract stationary segments from non-stationary synthetic and cardiac signals. For synthetic signals with different signal-to-noise ratio levels, we detect the beginning and end of the stationary segments and the result is compared to the known values of the occurrence of these events. For cardiac signals, RR interval (cardiac cycle length) time series, obtained from electrocardiographic records during stress tests for two populations (diabetic patients with cardiovascular autonomic neuropathy and control subjects), were divided into stationary segments. Results on synthetic signals reveal that the non-stationary sequence is divided into more stationary segments than needed. Additionally, due to HRV reduction and exercise intolerance reported on diabetic cardiovascular autonomic neuropathy patients, non-stationary RR interval sequences from these subjects can be divided into longer stationary segments compared to the control group.