Detection of subjects with ischemic heart disease by using machine learning technique based on heart rate total variability parameters

OBJECTIVE: Ischemic heart disease (IHD), in its chronic stable form, is a subtle pathology due to its silent behavior before developing in unstable angina, myocardial infarction or sudden cardiac death. The clinical assessment is based on typical symptoms and finally confirmed, invasively, by coronary angiography. Recently, heart rate variability (HRV) analysis as well as some machine learning algorithms like Artificial Neural Networks (ANNs) were used to identify cardiovascular arrhythmias and, only in few cases, to classify IHD segments in a limited number of subjects. The goal of this study was the identification of the ANN structure and the HRV parameters producing the best performance to identify IHD patients in a non-invasive way, validating the results on a large sample of subjects. Moreover, we examined the influence of a clinical non-invasive parameter, the left ventricular ejection fraction (LVEF), on the classification performance.APPROACH: To this aim, we extracted several linear and non-linear parameters from 24h RR signal, considering both normal and ectopic beats (Heart Rate Total Variability), of 251 normal and 245 IHD subjects, matched by age and gender. ANNs using several different combinations of these parameters together with age and gender were tested. For each ANN, we varied the number of hidden neurons from 2 to 7 and simulated 100 times changing randomly training and test dataset.MAIN RESULTS: The HRTV parameters showed significant greater variability in IHD than in normal subjects. The ANN applied to meanRR, LF, LF/HF, Beta exponent, SD2 together with age and gender reached a maximum accuracy of 71.8% and, by adding as input LVEF, an accuracy of 79.8%.SIGNIFICANCE: The study provides a deep insight into how a combination of some HRTV parameters and LVEF could be exploited to reliably detect the presence of subjects affected by IHD

Carbohydrate Requirement for Exercise in Type 1 Diabetes: Effects of Insulin Concentration

Physical activity is a keystone of a healthy lifestyle as well as of management of patients with type 1 diabetes. The risk of exercise-induced hypoglycemia, however, is a great challenge for these patients. The glycemic response to exercise depends upon several factors concerning the patient him/herself (eg, therapy, glycemic control, training level) and the characteristics of the exercise performed. Only in-depth knowledge of these factors will allow to develop individualized strategies minimizing the risk of hypoglycemia. The main factors affecting the exercise-induced hypoglycemia in patients with T1D have been analyzed, including the effects of insulin concentration. A model is discussed, which has the potential to become the basis for providing patients with individualized suggestions to keep constant glucose levels on each exercise occasion

Comparison of ECRES Algorithm with Classical Method in Management of Diabetes Type 1 Exercise-Related Imbalances

Nutrition and physical activity are important parts of a healthy lifestyle and management of diabetes. Regular moderate-intensity physical activity in type 1 diabetes patients can enhance insulin sensitivity, reduce the risk of cardiovascular disease and improve psychological well-being. Nevertheless, the risk of exercise-induced hypoglycemia is a great challenge for patients with type 1 diabetes and represents an important barrier to physical activity in these patients. Recently, an algorithm called ECRES has been developed with the aim of estimating, depending on patient\u2019s own therapy and specific physical activity, the glucose supplement required by the patient to maintain safe blood glucose levels. The aim of this study is to compare the ECRES algorithm to classical quantitative approach. Therefore, we measured and compared glycaemia in 23 patients (mean age: 43 \ub1 12 years) during 1-h treadmill walk/run maintaining heart rate at 65% of his/her theoretical maximum value for age. For each subject two separate tests were performed: with carbohydrates supplement estimated by ECRES algorithm and by classical approach, respectively. The average heart rate observed during exercise (average progression speed: 5.8 \ub1 0.8 km/h at 4.2 \ub1 2.3% inclination) was 111.5 \ub1 9.4 bpm. Glycaemia measured by portable glucometer showed no significant differences between tests managed with ECRES algorithm and with classical approach, both before (149 \ub1 47 vs. 128 \ub1 41 mg/dL) and at the end of the performed exercise (134 \ub1 66 vs. 138 \ub1 54 mg/dL). The ECRES algorithm, however, estimated a significantly lower amount of carbohydrate needed for physical activity as compared to that suggested by the classical approach (14.8 \ub1 12.0 g vs. 23.4 \ub1 4.7 g; p < 0.05), while maintaining patients\u2019 blood glucose within optimal clinical limits. The study results confirmed the validity of the estimates made by the ECRES algorithm

A mobile app for the self-management of type 1 diabetes as tool for preventing of exercise-associated glycemic imbalances

mHealth is a growing field of research, concerning the great potentialities of mobile technology as a tool for self-management of chronic conditions. Physical activity greatly influences blood glucose levels, therefore for type 1 diabetes patients is important to adapt their diet and therapy in order to avoid exercise-induced hyperglycemia and hypoglycemia. The later represents one of the major barriers to physical activity and it limits volitional exercise in type 1 diabetes patients. However, there is lack of stand-alone mobile tool that provides the support to the patient in order to perform physical activity and exercise under safe glycaemia levels. Recently, Exercise Carbohydrate Requirement Estimating Software (ECRES) algorithm was proposed to calculate patient-exercise tailored glucose supplement required to maintain safe blood glucose levels during physical activity. The objective of this study was to develop a mobile App which implements an individualized predictive system for blood glucose in type 1 diabetes, depending on exercise strength. Its usability and accuracy were compared to original ECRES estimating software in 15 volunteer subjects. The developed application provides relevant feedback to patients on carbohydrate intake needed to carry out a planned physical activity, in a safe manner. Furthermore, application provides other important features, for self-management of this chronicity, reported in recent literature: entry of blood glucose values, display of diabetes-related data, such as blood glucose readings and their analysis, carbohydrate intake, insulin doses, and easy data export. The application also incorporates food atlas in order to facilitate carbohydrates calculation. The results of the test showed that developed application accurately implements ECRES algorithm and the self-management features. In conclusion, proposed App could be a useful support tool to diabetes type 1 patents. The results should be confirmed in larger clinical study

Impact of Aging on Heart Rate Variability Properties of Healthy Subjects

Heart Rate Variability (HRV) has been studied in a variety of clinical situations in order to quantify the modulations in the heart rate associated to different pathological conditions. Nevertheless, significant changes in spectral and some nonlinear parameters of the HRV were reported also in normal subjects, depending on age and gender. The aim of this work was to quantify the age-related differences in other nonlinear parameters, particularly in the fractal dimension, of the HRV of healthy subjects and to compare the results with the changes showed by spectral measures. The RR time series extracted by the Holter monitoring of 60 healthy subjects, divided into three groups similar for both age and gender, were accurately analyzed. The results only partially revealed age-related changes both in the spectral and fractal HRV measures, underlining the need to carefully examine the RR data selection and the pre-processing phases

A Big-Data-Analytics Framework for Supporting Classification of ADHD and Healthy Children via Principal Component Analysis of EEG Sleep Spindles Power Spectra

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is essentially clinical and research of biomarkers represents a current great challenge. The interest in sleep spindle has been increased after the description of their role in cognitive functions and of their involvement in neurodevelopmental disorders. We aimed to investigate this peculiar aspect of sleep through EEG spectral analysis of three different spindle epochs (ante, spindle, post), in order to provide more and detailed information on sleep brain functioning in ADHD. These features can be analyzed via well-known big data analytics methods. In our case, they were evaluated by using classification methods to support ADHD diagnosis. We combined ADHD\u2019s related PSD features (i.e. theta, beta and sigma bands) with principal component analysis (PCA) for data dimensional reduction, and Linear Supported Vector Machine (Linear-SVM) as classification algorithm. In all bands and epochs, power values in Control group were higher than in ADHD children, although not statistically significant in all cases. Significant differences between ADHD and Control group were not detected for spindle epoch, while for ante and post epochs spectral power differed significantly in theta, beta and sigma bands. Results highlighted the possibility of using our new approach as a possible hallmark for ADHD. Indeed the analysis of PSD parameters combined with PCA and Linear-SVM classification resulted in a highly (94.1%) accurate discrimination between the two groups. The novelty of the approach is PSD analysis of different sleep spindles epochs combined with principal component analysis and Linear Supported Vector Machine classification. This study demonstrated the importance of analyzing sleep microstructures in ADHD. Encouraging results supports the potentiality of using EEG measures with specific methodologies we applied and should be confirmed in a large clinical study

Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis-treated ischemic stroke: a wireless EEG study

Owing to the large inter-subject variability, early post-stroke prognosis is challenging, and objective biomarkers that can provide further prognostic information are still needed. The relation between quantitative EEG parameters in pre-thrombolysis hyper-acute phase and outcomes has still to be investigated. Hence, possible correlations between early EEG biomarkers, measured on bedside wireless EEG, and short-term/long-term functional and morphological outcomes were investigated in thrombolysis-treated strokes. EEG with a wireless device was performed in 20 patients with hyper-acute (< 4.5 h from onset) anterior ischemic stroke before reperfusion treatment. The correlations between outcome parameters (i.e., 7-day/12-month National Institutes of Health Stroke Scale NIHSS, 12-month modified Rankin Scale mRS, final infarct volume) and the pre-treatment EEG parameters were studied. Relative delta power and alpha power, delta/alpha (DAR), and (delta+theta)/(alpha+beta) (DTABR) ratios significantly correlated with NIHSS 7-day (rho = 0.80, - 0.81, 0.76, 0.75, respectively) and NIHSS 12-month (0.73, - 0.78, 0.74, 0.73, respectively), as well as with final infarct volume (0.75, - 0.70, 0.78, 0.62, respectively). A good outcome in terms of mRS 64 2 at 12 months was associated with DAR parameter (p = 0.008). The neurophysiological biomarkers obtained by non-invasive and portable technique as wireless EEG in the early pre-treatment phase may contribute as objective parameters to the short/long-term outcome prediction pivotal to better establish the treatment strategies.Graphical abstract Block diagram of study protocol and main findings. Assessment at admission including wireless EEG acquisition in emergency setting (< 4.5 from stroke onset), extracted EEG features before reperfusion thrombolytic treatment. The main findings in our study sample are summarized in two different exemplificative stroke patients with different pre-thrombolysis alterations of EEG parameters resulting in different final infarct volume extensions and short/long-term clinical outcomes (NIHSS, mRS)

Wake-up stroke: thrombolysis reduces ischemic lesion volume and neurological deficit

Backgrounds: Wake-Up Stroke (WUS) patients are generally excluded from thrombolytic therapy (rTPA) due to the unknown time of stroke onset. This study aimed to investigate the effects of rTPA in WUS patients during every day clinical scenarios, by measuring ischemic lesion volume and functional outcomes compared to non-treated WUS patients. Methods: We retrospectively analyzed clinical and imaging data of 149 (75 rTPA; 74 non-rTPA) patients with acute ischemic WUS. Ischemic volume was calculated on follow-up CT and functional outcomes were the NIHSS and mRS comparing rTPA and non-rTPA WUS. Patients were selected using ASPECTS > 6 on CT and/or ischemic penumbra > 50% of hypoperfused tissue on CTP. Results: A reduced volume was measured on the follow-up CT for rTPA (1 mL, 0-8) compared to the non-rTPA patients (10 mL, 0-40; p = 0.000). NIHSS at 7 days from admission was significantly lower in the rTPA (1, 0-4) compared to non-rTPA group (3, 1-9; p = 0.015), as was the percentage of improvement (\u394NIHSS) (70% vs 50%; p = 0.002). A higher prevalence of mRS 0-2 was observed in the rTPA compared to the non-rTPA (54% vs 39%; p = 0.060). Multivariate analysis showed that NIHSS at baseline and rTPA treatment are significant predictors of good outcome both in terms of NIHSS at 7 days and ischemic lesion volume on follow-up CT (p < 0.05). Conclusions: rTPA in WUS patients selected with CT and/or CTP resulted in reduced ischemic infarct volume on follow-up CT and better functional outcome without increment of intracranial hemorrhages and in-hospital mortality

Has COVID-19 played an unexpected \u201cstroke\u201d on the chain of survival?

Background: The COVID-19 pandemics required several changes in stroke management and it may have influenced some clinical or functional characteristics. We aimed to evaluate the effects of the COVID-19 pandemics on stroke management during the first month of Italy lockdown. In addition, we described the emergency structured pathway adopted by an Italian University Hub Stroke Unit in the cross-border Italy-Slovenia area. Methods: We analyzed admitted patients' clinical features and outcomes between 9th March 2020 and 9th April 2020 (first month of lockdown), and compared them with patients admitted during the same period in 2019. Results: Total admissions experienced a reduction of 45% during the lockdown compared to the same period in 2019 (16 vs 29, respectively), as well as a higher prevalence of severe stroke (NIHSS>10) at admission (n = 8, 50% vs n = 8, 28%). A dramatic prevalence of stroke of unknown symptom onset was observed in 2020 (n = 8, 50% vs n = 3, 10%). During lockdown, worse functional and independence outcomes were found, despite the similar proportion of reperfused patients. Similar 'symptoms alert-to-admission' and 'door-to-treatment' times were observed. During lockdown hospitalization was shorter and fewer patients completed the stroke work-up. Conclusion: In conclusion, the adopted strategies for stroke management during the COVID-19 emergency have suggested being effective, while suffering a reduced and delayed reporting of symptoms. Therefore, we recommend raising awareness among the population against possible stroke symptoms onset. Thus, think F.A.S.T. and do not stay-at-home at all costs

Peripheral nerve adaptations to 10 days of horizontal bed rest in healthy young adult males

Space analogues, such as bed rest, are used to reproduce microgravity-induced morphological and physiological changes and can be used as clinical models of prolonged inactivity. Nevertheless, non-uniform decreases in muscle mass and function have been frequently reported, and peripheral nerve adaptations have been poorly studied, although some of these mechanisms may be explained. Ten young healthy males (18-33 y) underwent 10 days of horizontal bed rest. Peripheral neurophysiological assessments were performed bilaterally for the dominant (DL) and non-dominant upper and lower limbs (N-DL) on the 1st and 10th day of bed rest, including ultrasound of the median, deep peroneal (DPN) and common fibular (CFN) nerves, as well as a complete nerve conduction study (NCS) of the upper and lower limbs. Consistently reduced F-waves, suggesting peripheral nerve dysfunction, of both the peroneal (DL: p= 0.005, N-DL p= 0.013) and tibial nerves (DL: p= 0.037, N-DL p= 0.005) were found bilaterally, while no changes were observed in nerve ultrasound or other parameters of the NCS of both the upper and lower limbs were observed. In these young healthy males, only the F-waves, known to respond to postural changes, were significantly affected by short-term bed rest. These preliminary results suggest that during simulated microgravity, most changes occur at the muscle or central nervous system level. Since the assessment of F-waves is common in clinical neurophysiological examinations, caution should be used when testing individuals after prolonged immobility