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Acute Ischemic Stroke (AIS) is defined as the acute condition of occlusion of a cerebral artery and is often caused by a Hypertensive Condition (HC). Due to its sudden occurrence, AIS is not observable the right moment it occurs, thus information about instantaneous changes in hemodynamics is limited. This study aimed to propose an integrated Lumped Parameter (LP) model of the cardiovascular system to simulate an AIS and describe instantaneous changes in hemodynamics. In the integrated LP model of the cardiovascular system, heart chambers have been modelled with elastance systems with controlled pressure inputs; heart valves have been modelled with static open/closed pressure-controlled valves; eventually, the vasculature has been modelled with resistor-inductor-capacitor (RLC) direct circuits and have been linked to the rest of the system through a series connection. After simulating physiological conditions, HC has been simulated by changing pressure inputs and constant RLC parameters. Then, AIS occurring in arteries of different sizes have been simulated by considering time-dependent RLC parameters due to the elimination from the model of the occluding artery; instantaneous changes in hemodynamics have been evaluated by Systemic Arteriolar Flow (Qa) and Systemic Arteriolar Pressure (Pa) drop with respect to those measured in HC. Occlusion of arteries of different sizes leaded to an average Qa drop of 0.38 ml/s per cardiac cycle (with minimum and maximum values of 0.04 ml/s and 1.93 ml/s) and average Pa drop of 0.39 mmHg, (with minimum and maximum values of 0.04 mmHg and 1.98 mmHg). In conclusion, hemodynamic variations due to AIS are very small with respect to HC. A direct relation between the inverse of the length of the artery in which the occlusion occurs and the hemodynamic variations has been highlighted; this may allow to link the severity of AIS to the length of the interested artery.

The aim of this study was to investigate whether incretins, at physiological levels, affect hepatic and/or extrahepatic insulin clearance. Hepatic and extrahepatic insulin clearance was studied in 31 double incretin receptor knockout (DIRKO) and 45 wild-type (WT) mice, which underwent an Intravenous Glucose Tolerance Test (IVGTT). A novel methodology based on mathematical modeling was designed to provide two sets of values (FEL-P1, CLP-P1, FEL-P2, CLP-P2) accounting for hepatic and extrahepatic clearance in the IVGTT first and second phases, respectively, plus the respective total clearances, CLT-P1 and CLT-P2. A statistically significant difference between DIRKO and WT was found in CLT-P1 (0.61 [0.48–0.82] vs. 0.51 [0.46–0.65] (median [interquartile range]), p = 0.02), which was reflected in the peripheral component, CLP-P1 (0.18 [0.13–0.27] vs. 0.15 [0.11–0.22], p = 0.04), but not in the hepatic component, FEL-P1 (29.7 [26.7–34.9] vs. 28.9 [25.7–32.0], p = 0.18). No difference was detected between DIRKO and WT in CLT-P2 (1.38 [1.13–1.75] vs. 1.69 [1.48–1.87], p = 0.10), neither in CLP-P2 (0.72 [0.64–0.81] vs. 0.79 [0.69–0.87], p = 0.27) nor in FEL-P2 (37.8 [35.1–43.1] vs. 39.8 [35.8–44.2], p = 0.46). In conclusion, our findings suggest that the higher insulin clearance observed in DIRKO compared with WT during the IVGTT first phase may be due to its extrahepatic component.

Electrocardiographic alternans, consisting of P-wave alternans (PWA), QRS-complex alternans (QRSA) and Twave alternans (TWA), is an index of cardiac risk. However, only automated TWA measurement methods have been proposed so far. Here, we presented the enhanced adaptive matched filter (EAMF) method and tested its reliability in both simulated and experimental conditions. Our methodological novelty consists in the introduction of a signal enhancement procedure according to which all sections of the electrocardiogram (ECG) but the wave of interest are set to baseline, and in the extraction of the alternans area (AAr) in addition to the standard alternans amplitude (AAm). Simulated data consisted of 27 simulated ECGs representing all combinations of PWA, QRSA and TWA of low (10 mu V) and high (100 mu V) amplitude. Experimental data consisted of exercise 12-lead ECGs from 266 heart failure patients with an implanted cardioverter defibrillator for primary prevention. EAMF was able to accurately identify and measure all kinds of simulated alternans (absolute maximum error equal to 2%). Moreover, different alternans kinds were simultaneously present in the experimental data and EAMF was able to identify and measure all of them (AAr: 545 mu V x ms, 762 mu V x ms and 1382 mu V x ms; AAm: 5 mu V, 9 mu V and 7 mu V; for PWA, QRSA and TWA, respectively) and to discriminate TWA as the prevalent one (with the highest AAr). EAMF accurately identifies and measures all kinds of electrocardiographic alternans. EAMF may support determination of incremental clinical utility of PWA and QRSA with respect to TWA only.

Introduction Drug-induced block of the hERG potassium channel could predispose to torsade de pointes, depending on occurrence of concomitant blocks of the calcium and/or sodium channels. Since the hERG potassium channel block affects cardiac repolarization, the aim of this study was to propose a new reliable index for non-invasive assessment of drug-induced hERG potassium channel block based on electrocardiographic T-wave features. Methods ERD30% (early repolarization duration) and TS/A (down-going T-wave slope to T-wave amplitude ratio) features were measured in 22 healthy subjects who received, in different days, doses of dofetilide, ranolazine, verapamil and quinidine (all being hERG potassium channel blockers and the latter three being also blockers of calcium and/or sodium channels) while undergoing continuous electrocardiographic acquisition from which ERD30% and TS/A were evaluated in fifteen time points during the 24 h following drug administration ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet). A total of 1320 pairs of ERD30% and TS/A measurements, divided in training (50%) and testing (50%) datasets, were obtained. Drug-induced hERG potassium channel block was modelled by the regression equation BECG(%) = a·ERD30% + b·TS/A+ c·ERD30%·TS/A + d; BECG(%) values were compared to plasma-based measurements, BREF(%). Results Regression coefficients values, obtained on the training dataset, were: a = -561.0 s-1, b = -9.7 s, c = 77.2 and d = 138.9. In the testing dataset, correlation coefficient between BECG(%) and BREF(%) was 0.67 (p Conclusion BECG(%) is a reliable non-invasive index for the assessment of drug-induced hERG potassium channel block, independently from concomitant blocks of other ions.

Glucagon is secreted from the pancreatic alpha cells and plays an important role in the maintenance of glucose homeostasis, by interacting with insulin. The plasma glucose levels determine whether glucagon secretion or insulin secretion is activated or inhibited. Despite its relevance, some aspects of glucagon secretion and kinetics remain unclear. To gain insight into this, we aimed to develop a mathematical model of the glucagon kinetics during an oral glucose tolerance test, which is sufficiently simple to be used in the clinical practice. The proposed model included two first-order differential equations -one describing glucagon and the other describing C-peptide in a compartment remote from plasma - and yielded a parameter of possible clinical relevance (i.e., SGLUCA(t), glucagon-inhibition sensitivity to glucose-induced insulin secretion). Model was validated on mean glucagon data derived from the scientific literature, yielding values for SGLUCA(t) ranging from -15.03 to 2.75 (ng of glucagon·nmol of C-peptide-1). A further validation on a total of 100 virtual subjects provided reliable results (mean residuals between -1.5 and 1.5 ng·L-1) and a negative significant linear correlation (r = -0.74, p < 0.0001, 95% CI: -0.82 – -0.64) between SGLUCA(t) and the ratio between the areas under the curve of suprabasal remote C-peptide and glucagon. Model reliability was also proven by the ability to capture different patterns in glucagon kinetics. In conclusion, the proposed model reliably reproduces glucagon kinetics and is characterized by sufficient simplicity to be possibly used in the clinical practice, for the estimation in the single individual of some glucagon-related parameters.

Myocardial bridge (MB) is a congenital heart condition in which a “bridge” of myocardium is overlying a “tunneled” coronary artery. MB can be associated with a series of critical cardiac events. Aim of this study was to evaluate electrocardiographic alternans (ECGA) on a MB patient, being ECGA a cardiac electrical risk index defined as beat-to-beat alternation of electrocardiographic P-wave, QRS-complex and T-wave morphology at stable heart rate. ECGA analysis was performed in a 1-hour 12-lead electrocardiographic recording of a 54 years-old MB male patient at rest by application of the heart-rate adaptive match filter method. Areas of P-wave, QRS and T-wave alternans (PWAA, QRSAA, TWAA) were measured, evaluating also the prevalent among the three. Results showed the prevalent alternans was T-wave alternans, being TWAA on average equal to 6.3 µV×s (PWAA=4.7 µV×s, QRSAA=4.3 µV×s); TWAA prevalence occurrence rate was 94% (PWAA: 5%, QRSAA:1%). TWAA was also found to be significantly correlated (p=0.72, p

The electrocardiographic QT interval is an index of cardiac risk commonly used in clinics. Accurate QT measure is challenging, especially in noisy conditions, when acquisitions of phonocardiograms (PCGs) may be more reliable than acquisitions of electrocardiograms (ECGs). However, PCG features are less used in clinics. Thus, aim of the study was to propose a model for indirectly measuring the electrocardiographic QT interval from the phonocardiographic heart sounds in healthy subjects. To this aim, simultaneously acquired PCGs and ECGs of 99 healthy subjects were processed to obtain median PCG and ECG beats. Beat length, S1 onset and S2 onset were identified from the median PCG beat, while QT interval (QT) was measured from the median ECG beat. Then, a regression model was formulated by regression analysis to obtain PCG-based QT estimation (QT) and validated by leave-one-out cross-validation. Correlation coefficient (p) and estimation error were also computed. QT and QT did not differ significantly (model formulation: 362ms vs 358ms; model validation:360ms vs 358ms, respectively; P>0.5) and were significantly correlated (model formulation: p=0.7, p −13 ; model validation: p=0.6, P −10 ); median error is 1 ms (

The fetal electrocardiogram (FECG) is the recording of the electrical activity of the fetal heart. Morphologically, FECG shows the standard P-QRS-TU electrocardiographic pattern and a heart rate (HR) of 120–160 bpm. There are two techniques to acquire FECG: the direct one (DI-FECG), with a spiral wire electrode located on the fetal scalp; and the indirect one (IN-FECG), with electrodes located on the mother’s abdomen. Fetal T-wave alternans (TWA) represents a possible cause of unexplained fetal deaths; its investigation, however, is challenging. This chapter discusses about this scientific and clinical problem, and proposes a procedure to identify TWA from both DI-FECG and IN-FECG. The procedure includes the following 3 steps: (1) automatic identification of fetal R peaks, performed through the improved fetal Pan-Tompkins Algorithm; (2) FECG filtering, performed through linear filtering and the segmented-beat modulation method; and (3) automatic TWA identification, performed through the heart-rate adaptive match filter. Application of this procedure to 5 DI-FECG and 20 IN-FECG from 5 fetuses confirmed its goodness for fetal applications and suggested that fetuses show TWA even when healthy. Moreover, TWA detected in DI-FECG was comparable to that in IN-FECG, suggesting that TWA identification was reliable also in IN-FECG, which is much more affected by artifacts and interferences than DI-FECG.

Background In the prehospital triage of patients presenting with symptoms suggestive of acute myocardial ischemia, reliable myocardial ischemia detection in the electrocardiogram (ECG) is pivotal. Due to large interindividual variability and overlap between ischemic and nonischemic ECG‐patterns, incorporation of a previous elective (reference) ECG may improve accuracy. The aim of the current study was to explore the potential value of serial ECG analysis using subtraction electrocardiography. Methods SUBTRACT is a multicenter retrospective observational study, including patients who were prehospitally evaluated for acute myocardial ischemia. For each patient, an elective previously recorded reference ECG was subtracted from the ambulance ECG. Patients were classified as myocardial ischemia cases or controls, based on the in‐hospital diagnosis. The diagnostic performance of subtraction electrocardiography was tested using logistic regression of 28 variables describing the differences between the reference and ambulance ECGs. The Uni‐G ECG Analysis Program was used for state‐of‐the‐art single‐ECG interpretation of the ambulance ECG. Results In 1,229 patients, the mean area‐under‐the‐curve of subtraction electrocardiography was 0.80 (95%CI: 0.77–0.82). The performance of our new method was comparable to single‐ECG analysis using the Uni‐G algorithm: sensitivities were 66% versus 67% (p‐value > .05), respectively; specificities were 80% versus 81% (p‐value > .05), respectively. Conclusions In our initial exploration, the diagnostic performance of subtraction electrocardiography for the detection of acute myocardial ischemia proved equal to that of state‐of‐the‐art automated single‐ECG analysis by the Uni‐G algorithm. Possibly, refinement of both algorithms, or even integration of the two, could surpass current electrocardiographic myocardial ischemia detection.

Minimal model analysis of intravenous glucose tolerance test (IVGTT) data represents the reference method to assess insulin sensitivity (SI) and glucose effectiveness (SG) that quantify the insulin-dependent and insulin-independent processes of glucose disappearance, respectively. However, test duration (3 h) and need for modeling expertise limit the applicability of this method. Aim of this study was providing a simple predictor of SG applicable to short test (1 h), as previously done with SI. Three groups of subjects reflecting different glucose tolerance degrees underwent a 3 h IVGTT: subjects with normal glucose tolerance (NGT, n = 164), with defective glucose regulation (DGR, n = 191), and with type 2 diabetes (T2D, n = 39). Minimal model analysis provided reference SG and its components at zero (GEZI) and basal (BIE) insulin. The simple predictor CSG (calculated SG) was described by the formula CSG = α0 + α1 × KG/Gpeak, being KG the glucose disappearance rate (between 10 and 50 min) and Gpeak the maximum of the glucose curve during the test; α0 and α1 coefficients were provided by linear regression analysis. CSG and SG showed a markedly significant relationship in the whole dataset (r = 0.72, p

Micro as well as clinical atrial fibrillation (AF) is associated with both F-wave occurrence and high heart-rate variability (HRV). Automatic AF identification typically relies on HRV evaluation only. However, high HRV is not AF specific and may not be reliably estimated in very short electrocardiograms (ECG). This study presents a new algorithm for automatic AF identification in very short ECG based on computation of a new spectral F-wave index (SFWI). Data consisted of short (9 heartbeats) 12-lead ECG acquired from 6628 subjects divided in assessment dataset and validation dataset. Each lead was independently analyzed so that 12 values of SFWI, indicating the percentage of spectral power in the 4–10 Hz band, were obtained for each ECG. Additionally, a global SFWI value was computed as the median of SFWI distribution over leads. To identify AF, a threshold on SFWI was firstly assessed on the assessment dataset, and then evaluated on the validation dataset by computation of sensitivity (SE), specificity (SP) and accuracy (AC). Results were compared with those of standard HRV-based approaches. AF identification by SFWI was already good when considering a single lead (SE: 84.6%–88.8%, SP: 84.5%–87.0%, AC: 84.5%–87.3%), improved significantly when combining the 12 leads (SE: 89.0%, SP: 87.0%, AC: 88.7%) and, overall, performed better than standard HRV-based approaches (SE: 82.2%, SP: 83.6%, AC: 83.4%). The presented algorithm is a useful tool to automatically identify AF in very short ECG, and thus has the potentiality to be applied for detection of both micro and clinical AF.

Background Sudden infant death syndrome is more frequent in preterm infants (PTI) than term infants and may be due to cardiac repolarization instability, which may manifest as T-wave alternans (TWA) on the electrocardiogram (ECG). Therefore, the aim of the present work was to analyze TWA in nonpathological PTI and to open an issue on its physiological interpretation. Methods Clinical population consisted of ten nonpathological PTI (gestational age ranging from 29 3 7 to 34 2 7 weeks; birth weight ranging from 0.84 to 2.10 kg) from whom ECG recordings were obtained ("Preterm infant cardio-respiratory signals database" by Physionet). TWA was identified through the heart-rate adapting match filter method and characterized in terms of mean amplitude values (TWAA). TWA correlation with several other clinical and ECG features, among which gestational age-birth weight ratio, RR interval, heart-rate variability, and QT interval, was also performed. Results TWA was variable among infants (TWAA = 26 ± 11 µV). Significant correlations were found between TWAA versus birth weight (ρ = -0.72, p = .02), TWAA versus gestational age-birth weight ratio (ρ = 0.76, p = .02) and TWAA versus heart-rate variability (ρ = -0.71, p = .02). Conclusions Our preliminary retrospective study suggests that nonpathological PTI show TWA of few tens of µV, the interpretation of which is still an open issue but could indicate a condition of cardiac risk possibly related to the low development status of the infant. Further investigations are needed to solve this issue.

Dofetilide is an antiarrhythmic drug that selectively inhibits the rapid component of the delayed rectifier potassium current. The administration of dofetilide may cause ventricular arrhythmias and torsade de pointes. Electrocardiographic (ECG) microvolt T-wave alternans (TWA), an electrophysiologic phenomenon consisting in the beat-to-beat alternation of the T-wave amplitude requiring computerized algorithms to be detected, has also been associated to malignant ventricular arrhythmias. Aim of the present study was to evaluate if dofetilide induces TWA during the 24 hours following administration. The study population consisted of 22 healthy subjects ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet) to whom a 500 μg-dose of dofetilide was administered. For each subject, 10 s ECG were acquired at baseline (0.5 hour before dofetilide administration) and at 15 time points during the 24 hours following the drug administration. ECG were then processed for automatic TWA detection by correlation method. In 21 subjects out of 22, after dofetilide administration, TWA significantly increased to a peak value (median TWA values went from 6 μV at baseline to a max 32 μV; p; 0.05), on average after 5 hours, to then come back to values closer to baseline. Thus, in healthy subjects, dofetilide increases occurrence and levels (6 times baseline value on average) of TWA in the hours following its administration.

Background and aims Insulin clearance is a relevant process in glucose homeostasis. In this observational study, we aimed to assess insulin clearance (ClINS) in women with former gestational diabetes (fGDM) both early after delivery and after a follow-up. Methods and results We analysed 59 fGDM women, and 16 women not developing GDM (CNT). All women underwent an oral glucose tolerance test (OGTT) yearly, and an insulin-modified intravenous glucose tolerance test (IVGTT) at baseline and at follow-up end (until 7 years). Both IVGTT and OGTT ClINS was assessed as insulin secretion to plasma insulin ratio. We also defined IVGTT first (0–10 min) and second phase (10–180 min) ClINS. We found that 14 fGDM women progressed to type 2 diabetes (PROG), whereas 45 women remained diabetes-free (NONPROG). At baseline, IVGTT ClINS showed alterations in PROG, especially in second phase (0.88 ± 0.10 l·min−1 in PROG, 0.60 ± 0.06 in NONPROG, 0.54 ± 0.07 in CNT, p ≤ 0.03). Differences in ClINS were not found from OGTT. Cox regression analysis showed second phase ClINS as significant type 2 diabetes predictor (hazard ratio = 1.90, 95% confidence interval 1.09–3.30, p = 0.02). Conclusion This study showed that insulin clearance derived from an insulin-modified IVGTT is notably altered in women with history of GDM progressing towards type 2 diabetes.

Sex differences may assume a key role in condition of impaired glucose metabolism and progression to type 2 diabetes, affecting insulin-dependent processes. However, the presence of sex differences in non-insulin-dependent processes (i.e. glucose effectiveness) has been scarcely investigated. The aim of this study was to detect the presence of sex differences in glucose effectiveness (SG), as assessed by minimal model analysis, in subjects with different degrees of glucose metabolism impairment. Two groups of subjects ranging from normal (NGR, n = 57, males/females: 31/26) to abnormal glucose regulation (AGR, n = 115, males/females 42/73) underwent a 3-h frequently sampled intravenous glucose tolerance test. Minimal model analysis provided SG and its components at zero (GEZI) and at basal (BIE) insulin. Values for SG were 2.52 ± 0.98 10−2 min−1 and 2.81 ± 1.07 10−2 min−1 for males and females in the NGR group, and 2.08 ± 1.21 10−2 min−1 and 2.09 ± 0.98 10−2 min−1 for males and females in the AGR group. No statistically significant difference was found between males and females in both NGR (p = 0.29) and AGR (p = 0.94) groups. Sex differences were not detected for GEZI, which provided the major contribution to SG, either in NGR or AGR group. In conclusion, glucose effectiveness and its components seem to be not affected by sex differences in all glucose tolerance conditions.

Alternans is an electrophysiological phenomenon consisting in a beat-to-beat variation of the morphology of an electrocardiographic (ECG) waveform. Literature has particularly studied T-wave alternans (TWA) because it has been widely recognized as a noninvasive and clinically useful index to predict occurrence of malignant ventricular arrhythmias and, eventually, sudden cardiac death. Historically, alternans of other segments of ECG, like P wave (PWA), or QRS complex (QRSA) gained less interest than TWA, but this is an incomplete vision of the action potential (AP). AP is influenced by electrical activity of all myocardial cells, so it is reasonable that all ECG waveforms could be affected by alternans phenomenon. ECG alternans (ECGA) can be intended as the prevalent nature of alternans. This study aimed to use the heart-rate adaptive match filter (AMF) method, previously applied for TWA applications, to detect ECGA. AMF effectiveness was tested on simulated alternating ECG (alternans-amplitude range: 10 µV–200 µV), characterized by single- and multiple-wave alternans (always of the same amplitude and morphology). AMF method proved to be specific, being able to recognize ECGA absence, and particularly sensitive to TWA. In general, in case of singular-wave alternans, AMF correctly identified the type of alternans and correctly determined its amplitude (mean error: 0%). When TWA was combined to PWA or QRSA, only TWA was identified with an overestimation of its amplitude (mean error: 23%). In conclusion, overall AMF proved its effectiveness and specificity in revealing and discriminating ECGA.

BACKGROUND: Human ether‐à‐go‐go‐related gene (hERG) potassium‐channel block represents a harmful side effect of drug therapy that may cause torsade de pointes (TdP). Analysis of ventricular repolarization through electrocardiographic T‐wave features represents a noninvasive way to accurately evaluate the TdP risk in drug‐safety studies. This study proposes an artificial neural network (ANN) for noninvasive electrocardiography‐based classification of the hERG potassium‐channel block. METHODS: The data were taken from the “ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects” Physionet database; they consisted of median vector magnitude (VM) beats of 22 healthy subjects receiving a single 500 μg dose of dofetilide. Fourteen VM beats were considered for each subject, relative to time‐points ranging from 0.5 hr before to 14.0 hr after dofetilide administration. For each VM, changes in two indexes accounting for the early and the late phases of repolarization, ΔERD (30%) and ΔT(S) (/A), respectively, were computed as difference between values at each postdose time‐point and the predose time‐point. Thus, the dataset contained 286 ΔERD (30%)‐ΔT(S) (/A) pairs, partitioned into training, validation, and test sets (114, 29, and 143 pairs, respectively) and used as inputs of a two‐layer feedforward ANN with two target classes: high block (HB) and low block (LB). Optimal ANN (OANN) was identified using the training and validation sets and tested on the test set. RESULTS: Test set area under the receiver operating characteristic was 0.91; sensitivity, specificity, accuracy, and precision were 0.93, 0.83, 0.92, and 0.96, respectively. CONCLUSION: OANN represents a reliable tool for noninvasive assessment of the hERG potassium‐channel block.

The most common sleep disorder is sleep apnea, whose manifestations are long breathing pauses. Sleep apnea assessment is usually performed by polysomnography. During this long-term monitoring, patient respiration and other biosignals are recorded by many sensors, causing a high level of discomfort. Thus, methods able to indirectly estimate the biosignal of interest from the others measured should be preferred. Respiration indirectly measured from electrocardiogram (ECG) is called ECG-derived respiratory (EDR) signal. Recently, Segmented Beat Modulation Method (SBMM) was proposed as a good method for EDR signal estimation in normal breathing. Thus, the aim of this study was to assess the quality of EDR signal estimation by SBMM in pathological events of sleep apnea. With this purpose, sixteen long term polysomnographic recordings from MITBIH Polysomnographic Database were considered. After standard preprocessing, respiration and ECG signals were divided in 30s windows and, in order to match to provided annotations, each window was classified into Normal or Apnea. EDR signal was estimated by SBMM procedure from each ECG window. Respiration and EDR signals were then processed by Fourier analysis to extract respiration frequencies. Respiration frequencies computed from respiration and EDR signals were compared in term of error. Results confirmed the good quality of the estimated EDR signal. Respiration frequency extracted from EDR signal in both Normal (16[13;19]cpm) and Apnea windows (18[15;21]cpm) are equal to those extracted from respiration signal (Normal: 16 [13;19]cpm and Apnea: 18 [15;21]cpm), providing null error distributions. In conclusion, SBMM proved to be a promising tool for EDR signal estimation.

BACKGROUND Obesity is known to induce a deterioration of insulin sensitivity (SI ), one of the insulin-dependent components of glucose tolerance. However, few studies investigated whether obesity affects also the insulin-independent component, that is glucose effectiveness (SG ). This cross-sectional study aimed to analyse SG and its components in different body mass index (BMI) categories. MATERIALS AND METHODS Three groups of subjects spanning different BMI (kg m-2 ) categories underwent a 3-h frequently sampled intravenous glucose tolerance test: Lean (LE; 18.5 ≤ BMI

In preterm infants, bradycardias associate to critical health conditions. Standard algorithm for bradycardia identification assumes baseline heart rate (BHR) equal to 150bpm and identifies bradycardias when heart rate (HR) decreases below 100bpm. Since preterm infants show BHR varying from 120bpm to 160bpm, a new adaptive algorithm for real-time bradycardia identification was presented. The adaptive algorithm continuously adjusts BHR by averaging HR over the preceding 10-minute window after eliminating out-of-range HR values, and identifies bradycardias when HR decreases below 67% of BHR. Both standard and adaptive algorithms were evaluated using long-term (20.3–70.3h) electrocardiographic recordings of ten preterm infants ("Preterm Infant Cardio-respiratory Signals" database by Physionet). Bradycardias were characterized in terms of rate (BR, h−1) and depth (BD, bpm). Being also indexes of infants’ health conditions, gestational age at birth (GA, days), birth weight (BW, kg) and HR were used to evaluate performances of the algorithms. Association between BR and BD vs GA, BW and HR was evaluated by computation of the correlation coefficient (ρ). Overall, standard and adaptive algorithms identified 516 and 546 bradycardias, respectively; median BR and BD values were comparable (1.25h−1 and 76bpm vs 1.26h−1 and 70bpm, respectively). However, the adaptive algorithm provided higher BD for HR>150bpm, and vice versa. Significant (p value

Fetal phonocardiogram (FPCG) consists in the recording of fetal heart sounds by means of a sensor placed on the mother’s abdominal surface. Usually, FPCG includes two major sounds for each fetal cardiac cycle: S1, produced by the sudden closure of mitral and tricuspid valves, and S2 produced by the closure of aortic and pulmonary valves. The aim of the present study was to propose AdvFPCG-Delineator for automatic fetal S1 and S2 identification and to demonstrate its reliability in different clinical conditions. The method consists of a wavelet-based filtering procedure followed by the computation of the scalogram, from which S1 and S2 were identified using a threshold-based algorithm. AdvFPCG-Delineator was tested on the “Simulated Fetal PCGs database” (37 FPCG signals) and on the experimental “Shiraz University fetal heart sounds database” (119 FPCG signals), both available at PhysioNet ( https://physionet.org ). Manual S1 and S2 annotations and simultaneously acquired cardiotocographic recordings were used to compute reference fetal heart rate (FHR) for the simulated and experimental databases, respectively. No statistically significant difference was observed between estimated vs reference FHR (140 bpm vs 140 bpm, respectively) for the simulated database, for which AdvFPCG-Delineator was also able to track beat-to-beat variability (correlation over 92%). Additionally, no statistically significant difference was observed between estimated vs reference FHR (141 bpm vs 140 bpm, respectively) for the experimental database, even when stratifying by clinical conditions (maternal age, gestational age, etc.). In conclusion, AdvFPCG-Delineator proved to be a reliable method to automatically identify S1 and S2 from fetal phonocardiograms.

Background. Larger one-time values of spatial QRS-T angle (SA) are associated with risk. However, experience how serial changes in SA $(\Delta SA)$ should be interpreted is lacking. Even within normal limits, any $\Delta SA$ likely signifies electrical remodeling. This study aimed to assess the impact of choosing either $\Delta SA$ or $\vert \Delta SA\vert$ as one of a set of serial ECG difference features that constitute the input for our deep learning serial-ECG classifier (DLSEC). Methods. DLSEC was trained and tested to detect emerging pathology in two serial ECG databases: a heart failure database and an acute ischemia database. Either $\Delta SA$ or $\vert \Delta SA\vert$ were among 13 features of serial-ECG differences. DLSEC was dynamically generated during learning, and testing area under the curve (AUC) of the receiver operating characteristic was computed. Results. The DLSECs performed well in emerging heart failure as well as in acute ischemia: testing AUCs were 72% and 84% for the heart failure database and 77% and 83% for the ischemia database, for $\Delta SA$ or $\vert \Delta SA\vert$ among the features, respectively. Conclusion. $\vert \Delta SA\vert$ among the features was superior to $\Delta SA$ in discriminating cases and controls. Our study supports the concept that any $\Delta SA$ , irrespective of its sign, indicates a worsening clinical condition. Further corroboration requires studies in other clinical situations.

A heart affected by atrial fibrillation (AF) presents atrial cells that depolarize in many sites, generating a chaotic electrical activity. On the electrocardiogram (ECG), this activity reflects in the appearance of fibrillatory (F) waves, consisting of low-amplitude oscillations at 4–10 Hz. Aim of the present study is to propose an automatic AF identification method based on F-wave frequency analysis in 10 s ECGs. To this aim, 10 s ECG from 90 healthy subjects (HSs) and 50 AF patients (AFPs) were considered. ECGs were processed by the segmented beat modulation method to reduce components in the F-wave band. Then, the power spectral density (PSD) was computed and the F-wave frequency ratio (FWFR), defined as the ratio between the spectral area in the F-wave frequency band and the total spectral area, was computed. FWFR ability to discriminate AFPs from HSs was evaluated by analyzing the area under the curve (AUC) of the receiver operating characteristic, and by computation of sensitivity, specificity and accuracy. FWFR values were higher in AFPs than in HSs (p

Epilepsy is a chronic neurological disorder, hallmark of which is unpredictable epileptic seizures (ES). The leading cause of death in people with uncontrolled ES is the sudden unexpected death in epilepsy (SUDEP), which is believed to share genes with sudden cardiac death (SCD). Being T-wave alternans (TWA) an ECG index of SCD, aim of this work was to evaluate TWA occurrence in proximity of ES. Electrocardiograms (ECG) from five partial epileptic patients constituting the “Post-Ictal Heart Rate Oscillations in Partial Epilepsy” Database by Physionet were analysed for automatic TWA identification by the heart-rate adaptive match filter (HRAMF). ES onsets and offsets were annotated. ECG segments starting 10min before ES and ending 10min after ES were extracted and further processed to characterize trends of median heart rate, median TWA (mTWA) and maximum TWA (MTWA) in ES proximity. Levels of mTWA were significantly higher than what previously observed in a female healthy population in all ES (46[25; 59]μV), pre-ES (31 [25;62]μ V) and post-ES (30[26;63]μ V) conditions. Both mTWA and MTWA tended to increase during ES. Thus, in proximity of ES, our epileptic patients are at increased risk of SCD, possibly associated with SUDEP. Other studies defining TWA role as a biomarker for SUDEP are needed.

Fetal T-wave alternans (TWA) is a still littleknown marker for severe fetus-heart instabilities and may be related to some currently unjustified fetal deaths. Automatically detecting TWA on direct fetal electrocardiograms (DFECG) means possibility of providing fetuses the right treatment during delivery. Instead, automatically identifying TWA on indirect fetal electrocardiograms (IFECG) means possibility of providing fetuses the right treatment even during pregnancy, when taking actions for outcome improvement is still possible. Moreover, TWA identification from IFECG is noninvasive, and thus safe for both fetuses and mothers. The aim of this work was testing the heart-rate adaptive match filter (HRAMF) for automatic TWA identification in IFECG and comparing HRAMF performance in IFECG against DFECG. To this aim, simultaneously recorded DFECG and IFECG tracings from 5 healthy fetuses were used ("Abdominal and Direct Fetal Electrocardiogram Database" from Physionet). TWA measurements (frequency, mean amplitude, maximum amplitude, and amplitude standard deviation) in IFECG (1.09±0.04 Hz, 11±5 μV, 21±12 μV and 7±3 μV) were of the same order of magnitude of those in DFECG (1.07±0.02 Hz, 9±2 μV, 30±11 μV and 6±2 μV). Moreover, a direct correlation (ñ) was found between maximum TWA and fetal heart rate (IFECG: ρ=0.999; P=0.022; DEFEG: ρ=0.642; P=0.243). Thus, HRAMF was able to detect TWA from IFECG as well as from DFECG.

Background Defects of cardiac repolarization, noninvasively identifiable by analyzing the electrocardiographic (ECG) ST segment and T wave, are among the major causes of sudden cardiac death. Still, no repolarization-based index has so far shown sufficient sensitivity and specificity to justify preventive treatments. Thus, the aim of this work was to evaluate the predictive power of our recently proposed f99 index for the occurrence of ventricular arrhythmias. Methods Our study populations included 170 patients with implanted cardiac defibrillator (ICD), 44 of which developed ventricular tachycardia and/or fibrillation during the 4-year follow-up (ICD_Cases) and 126 did not (ICD_Controls). The f99 index, defined as the frequency at which the repolarization normalized cumulative energy reaches 99%, was computed in each of the 15 (I to III, aVl, aVr, aVf, V1–V6, X, Y, Z) available ECG leads independently, and then maximized over the 6 precordial leads (f99_MaxV1–V6), 12 standard leads (f99_Max12STD) and three orthogonal leads (f99_MaxXYZ) to avoid dispersion-related issues. Each index predictive power was quantified as the area under the receiving operating characteristic curve (AUC). Results Median f99_MaxV1–V6, f99_Max12STD and f99_MaxXYZ values were significantly higher in the ICD_Cases than in the ICD_Controls (48 Hz vs. 35 Hz, P

Sleep apnea (SA) is linked to cardiovascular complications and to an increased risk of sudden cardiac death. Microvolt T-wave alternans (TWA) is a noninvasive electrocardiographic (ECG) index of cardiovascular risk; its rate of occurrence in SA patients remains unknown. Thus, this study investigated the occurrence of TWA in SA patients during night. To this aim, overnight ECG recordings of 16 SA patients were analyzed for TWA identification by means of our heart rate adaptive match filter. Results indicate that overnight TWA was characterized by a low mean amplitude (mean TWA: 6±3 μV). However, higher-amplitude transient TWA episodes (max TWA: 29±21 μV) occurred overnight, sometimes when patients were awake (max TWA: 33±18 μV; 56% of cases) and sometimes when patients were sleeping (max TWA: 24±23 μV; 44% of cases with 13%, 19%, 6% and 6% during sleep stage 1, 2, 3 and 4, respectively). In only 3 subjects (19%) TWA peaks occurred during an SA episode: during obstructive apnea with arousal in two cases (max TWA of 7 μV and 17 μV, during stages 1 and 2, respectively) and during hypoapnea with arousal in one case (max TWA of 6 μV while awake). Thus, SA patients show significant transient overnight TWA episodes, not necessarily occurring during a SA episode.

Very little is known about the incidence and etiology of fetal T-wave alternans (TWA), an electrophysiologic phenomenon potentially associated to fetal suboptimal outcomes. Thus, availability of automatic methods for quantification of TWA from digital electrocardiograms (ECG) is desirable, since TWA occurrence might indicate the need of taking actions before or during delivery. The heart-rate adaptive match filter (HRAMF) is a well-established method to identify TWA in adult ECG. Aim of the present study was to investigate the possibility of using HRAMF to identify and quantify TWA also in direct fetal ECG (DFECG) recordings. To this aim, HRAMF was applied to 5 min-long DFECG acquired during delivery ("Abdominal and Direct Fetal Electrocardiogram Database" by Physionet) of five healthy fetuses. Significant levels of TWA were measured in all DFECG. Specifically, on average, TWA was quite high in amplitude (9±2 μV) and variable in time, as indicated by values of standard deviation (6±2 μV) and maximum (28±10 μV) of TWA amplitude. Eventually, a positive correlation (ρ=0.68) was observed between maximum TWA and fetal heart rate, even though the limited number of recordings makes this result preliminary. In conclusion, HRAMF proved to be a suitable tool to automatically identify TWA from DFECG.

Phonocardiography (PCG) second heart sound represents aortic-pulmonary valves closure and beginning of isovolumetric relaxation of the ventricles. Electrocardiography (ECG) T wave represents electrical repolarization of the ventricles. Ventricular electrical repolarization is known to drive ventricular mechanical relaxation. Thus, the aim of the present study was to investigate whether, in normal conditions, second heart sound onset (S2on) matches in time T-wave offset (Toff) so that S2on may be used to identify Toff. To this aim, 99 couples of simultaneously recorded short (around 30 s PCG and ECG) signals relative to normal subjects (selected from PhysioNet/CinC Challenge 2016: Training Set A) were analyzed. S2on was identified by application of our newly developed threshold-based algorithm to the median beat of the PCG envelope. Instead, Toff was identified by application of the Laguna and Thakor algorithm to the median ECG beat. Median time-distance (St) between S2on and Toff was 5 ms (P=0.007). Thus, in normal conditions, S2on and Toff differ on average of 5 ms, whose meaning remain to be defined. Still, 5 ms is included in the Toff identification variability (of the order of tens ms) due to different Toff identification methods and electrocardiographic leads. Consequently, in normal condition, S2on may be used to estimate Toff.

Glucose effectiveness (SG) represents the ability of glucose per se, under basal insulin concentrations, to stimulate its own uptake and to suppress its own production. SG and its two components BIE (Basal Insulin Effect) and GEZI (Glucose Effectiveness at Zero Insulin) are known to decline in subjects whose glycemic status worsens, but no study aimed to analyze whether changes may occur even before, when a normal glucose tolerance status is still preserved but insulin resistance has already arisen. To investigate this issue, SG, BIE and GEZI were estimated from the minimal model interpretation of frequently sampled intravenous glucose tolerance (FSIGT) test data in two groups of subjects with normal glucose tolerance (basal glycemia < 5.6 mmol/l): a group of control participants (CNT, n=50) and a group of subjects with pathologies or conditions causing insulin resistance (IR, n=50). No difference in mean values of SG was observed in the IR with respect to the CNT group (2.3 ± 0.9 vs. 2.5 ± 0.9 10−2 min−1; p = 0.17). BIE was found to be the minor component of SG in both CNT and IR group. The GEZI component provided a significantly higher proportional contribution to SG in the IR with respect to CNT (89% vs. 81% of SG, p

The model-based assessment of insulin sensitivity in Zucker rat from Intravenous Glucose Tolerance Test (IVGTT) data is a common procedure. The minimal model methodology provides a very reliable assessment but requires specific competence for running the model. The aim of this study was presenting calculated SI (CSI), as a surrogate index for the simple assessment of insulin sensitivity in the Zucker Rat from IVGTT data. To this aim 25 Zucker Lean Rats (ZLR) and 25 Zucker Fatty Rats (ZFR) were considered. Reference insulin sensitivity (SI) was estimated in each rat through the minimal model methodology. CSI is defined as the ratio between the rate of glucose disappearance (KG) and the mean supra-basal area under the insulin curve during the test (ΔAUCINS), corrected by the proportionality term, α. Regression analysis between SI and KG/ΔAUCINS was performed to identify the α coefficient. Results showed as the computed value of CSI presented a high correlation (r = 0.89, R-square = 0.80 and p < 0.0001, slope ≈1) with SI. Mean value of CSI over the whole population was not significantly different from correspondent SI value (p = 0.17). CSI is able to detect the wellknown reduction of insulin sensitivity in the ZFR group (1.0±0.1 vs. 5.0±0.7 min-1/μU·ml-1, p < 0.001), in accordance with the results provided by SI. In conclusion, the present study proposes CSI, as a suitable empiric index for a simple and reliable assessment of insulin sensitivity in Zucker rat and able to provide the same quantitative information of modelbased SI.

Background: This study aimed to design a simple surrogate marker (i.e., predictor) of the minimal model glucose effectiveness (SG), namely calculated SG (CSG), from a short insulin-modified intravenous glucose tolerance test (IM-IVGTT), and then to ap ply it to study women with previous gestational diabetes mellitus (pGDM). Methods: CSG was designed using the stepwise model selection approach on a population of subjects (n=181) ranging from nor mal tolerance to type 2 diabetes mellitus (T2DM). CSG was then tested on a population of women with pGDM (n=57). Each sub ject underwent a 3-hour IM-IVGTT; women with pGDM were observed early postpartum and after a follow-up period of up to 7 years and classified as progressors (PROG) or non-progressors (NONPROG) to T2DM. The minimal model analysis provided a reference SG. Results: CSG was described as CSG=1.06×10-2+5.71×10-2×KG/Gpeak, KG being the mean slope (absolute value) of loge glucose in 10-25- and 25-50-minute intervals, and Gpeak being the maximum of the glucose curve. Good agreement between CSG and SG in the general population and in the pGDM group, both at baseline and follow-up (even in PROG and NONPROG subgroups), was shown by the Bland-Altman plots (

T-wave alternans (TWA) is a noninvasive index of risk for the occurrence of ventricular arrhythmias. It is known that TWA amplitude (TWAA) increases with heart rate (HR) but how the TWA predictive power varies with HR remains unknown. Thus, the aim of this study was to evaluate the dependency of exercise-induced TWA predictive power for the occurrence of ventricular arrhythmias from HR.TWA was identified using our HR adaptive match filter in exercise ECGs from 248 patients with implanted cardiac defibrillator (ICD), of which 72 developed ventricular tachycardia and/or fibrillation during the 4 year follow-up (ICD_Cases) and 176 did not (ICD_Controls). TWA predictive power was evaluated at HRs from 80 to 120 bpm by computing the area under the receiver operating characteristic curve (AUC) obtained using the maximum TWAA (maxTWAA) and the TWAA ratio (TWAAratio; i.e., the ratio between TWAA at a specific HR and at 80 bpm).TWAA increased with HR. At 80 bpm maxTWAA was lower than at 120 bpm in both ICD_Cases (22 μV vs 41 μV; P < 10(-2) ) and ICD_ Controls (16 μV vs 36 μV; P < 10(-4) ). However, only at 80 bpm ICD_Cases showed significantly higher maxTWAA than ICD_Controls (AUC = 0.6486; P = 0.0080). TWAAratio was higher in ICD_Controls than ICD_Cases for all HR but 120 bpm, and its predictive power was maximum at 115 bpm (AUC = 0.6914; P < 0.05).Exercise-induced TWA predictive power for the occurrence of ventricular arrhythmias, quantified using both maxTWAA and TWAAratio, was higher at low rather than at high HR.

Despite ST elevation having poor sensitivity for acute myocardial infarction (AMI), it remains the main electrocardiographic (ECG) repolarization index for AMI diagnosis. Aim of the present study was to propose a new f99 index, defined as the frequency at which the repolarization normalized cumulative energy reaches 99%, for ECG AMI discrimination from health with good sensitivity and good specificity. Evaluation of such f99 index was performed on 12-standard-lead (I, II, III, aV1, aVr, aVf, V1 to V6) ECG recordings of 47 healthy controls and 108 acute myocardial infarction (AMI) patients. Repolarization dispersion caused f99 distributions to be significantly lead dependent. In most leads (leads I, II, aVl, aVr, V2-V6), f99 median value was lower in the healthy controls (10-17 Hz) than in the AMI patients (12-38 Hz) indicating higher frequency components (i.e. a more fragmented repolarization) in the latter population. AMI patients from healthy controls discrimination by f99, evaluated in terms of sensitivity (Se) and specificity (Sp), was also lead dependent. Single-lead analysis indicated leads I (Se=80%, Sp=77%) and aVl (Se=84%, Sp=74%) as optimal. Instead, lead-system analysis, performed to overcome dispersion issues, provided the best results when averaging over the 6 precordial leads (Se= 81% and Sp=74%). In conclusion, our new f99 index appears as a promising tool for non-invasively and reliably discriminate AMI patients from healthy subjects.

T-wave offset (Toff) identification may be jeopardized by the presence of a significant inter-method (IMV) and inter-lead (ILV) Toff variability. Thus, the aim of the present study was to investigate if the dominant T wave (DTW) may be used to enhance Toff-identification reliability. DTWs and 15-lead ECG T waves of 46 control healthy subjects (CHS) and 103 acute myocardial infarction patients (AMIP) were analyzed for Toff identification using Zhang et al.’s (M1) and Daskalov and Christov’s (M2) methods. Results indicate that IMV is significantly reduced when identifying Toff from the DTW rather than from single ECG leads in both populations (CHS: 5 ms vs. 5–15 ms; AMIP: 10 ms vs. 10–20 ms). Moreover, when analyzing ILV, Toff was found to be equivalent (correlation = 0.71–0.98; P − 14 ) to the median Toff among leads, but required only one identification instead of 15. Thus, the DTW can be used to enhance Toff-identification reliability.

For the assessment of glucose tolerance from IVGTT data in Zucker rat, minimal model methodology is reliable but time- and money-consuming. This study aimed to validate for the first time in Zucker rat, simple surrogate indexes of insulin sensitivity and secretion against the glucose-minimal-model insulin sensitivity index (SI) and against first- (Φ1) and second-phase (Φ2) β-cell responsiveness indexes provided by C-peptide minimal model. Validation of the surrogate insulin sensitivity index (ISI) and of two sets of coupled insulin-based indexes for insulin secretion, differing from the cut-off point between phases (FPIR3-SPIR3, t = 3 min and FPIR5-SPIR5, t = 5 min), was carried out in a population of ten Zucker fatty rats (ZFR) and ten Zucker lean rats (ZLR). Considering the whole rat population (ZLR+ZFR), ISI showed a significant strong correlation with SI (Spearman’s correlation coefficient, r = 0.88; P

Background: Fetal well-being evaluation may be accomplished by monitoring cardiac activity through fetal electrocardiography. Direct fetal electrocardiography (acquired through scalp electrodes) is the gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of indirect fetal electrocardiography (acquired through abdominal electrodes) is limited by its poor signal quality. Objective: Aim of this study was to evaluate the suitability of the Segmented-Beat Modulation Method to denoise indirect fetal electrocardiograms in order to achieve a signal-quality at least comparable to the direct ones. Method: Direct and indirect recordings, simultaneously acquired from 5 pregnant women during labor, were filtered with the Segmented-Beat Modulation Method and correlated in order to assess their morphological correspondence. Signal-to-noise ratio was used to quantify their quality. Results: Amplitude was higher in direct than indirect fetal electrocardiograms (median:104 µV vs. 22 µV; P=7.66·10-4), whereas noise was comparable (median:70 µV vs. 49 µV, P=0.45). Moreover, fetal electrocardiogram amplitude was significantly higher than affecting noise in direct recording (P=3.17·10-2) and significantly in indirect recording (P=1.90·10-3). Consequently, signal-to-noise ratio was initially higher for direct than indirect recordings (median:3.3 dB vs. -2.3 dB; P=3.90·10-3), but became lower after denoising of indirect ones (median:9.6 dB; P=9.84·10-4). Eventually, direct and indirect recordings were highly correlated (median: ρ=0.78; P-208), indicating that the two electrocardiograms were morphologically equivalent. Conclusion: Segmented-Beat Modulation Method is particularly useful for denoising of indirect fetal electrocardiogram and may contribute to the spread of this noninvasive technique in the clinical practice.

Understanding the fetal heart rate (FHR) in the second stage of labor is important to recognize critical clinical outcomes. During this time, fetus is subject to head compression that activate baroreceptor reflexes that cause FHR deceleration. Large deceleration areas (DA: measured as duration by depth), have been associated to critical fetal outcomes. A preliminary study on 33 pregnant women reported a significant inverse correlation between DA in the last 60 minutes before delivery and umbilical pH at birth (fetal-distress measure when 7.1). Results confirmed that Cases have significantly higher DA than Controls (5.32 cm2 vs 1.44 cm2; P < 0.05). The inverse correlation between DA in the last 60 minutes before delivery and pH at birth, was weak but significant (ρ=-0.23, P

The Zephyr BioHarness 3.0 (BH3) is a popular wearable system specifically designed for training optimization of professional athletes. BH3 provides the electrocardiogram (ECG BH3) and the heart-rate signal (HRSBH3). Aim of this study is to evaluate the reliability of HRSBH3 to assess its clinical applicability to the general population for cardiac-risk evaluations. Data were acquired from 10 healthy subjects (age: 34±17 years) during a 5-minutes rest. Since the tachogram represents the standard signal for studying the heart rate (HR) and its variability, ECG BH3 was elaborated in order to get the tachogram (HRSTG). HRSBH3 and HRSTG were compared in terms of mean HR (MHR, bpm), HR standard deviation (HRSD, bpm) and HRSD error (bpm). HRSBH3 and HRSTG provided comparable MHR (73.07±15.53 bpm vs 72.86±15.57 bpm, respectively) while HRSD by HRSBH3 was significantly lower than HRSD by HRS TG (4.51 ±2.29 bpm vs 5.63±2.99 bpm, respectively; P=0.0043). HRSD error was significantly greater than zero (0.20–3.00 bpm; P=0.0043); moreover, it was strongly correlated to HRSD by HRS TG (p=0.82, P=0.0036). Thus, HRS BH3 is appropriate only for sport applications based on MHR estimations, but not to clinical evaluations based on HRV measurements.

The purpose of the present study was to test the efficacy of the empiric index SPIR (Second-phase Insulin Release) in the quantification of second-phase insulin secretion in the Zucker Fatty Rat. SPIR index is defined as the area under the curve of insulin between 8 and 90 min after an Intravenous Glucose Tolerance Test (IVGTT). The validation of such index was performed against the second-phase β-cell responsiveness index (Φ 2 ) provided by C-peptide minimal model. To this aim, Φ 2 and SPIR were simultaneously computed from IVGTT data, measured in six Zucker fatty rats (ZFR), 7-to-9week-old, and seven age-matched Zucker lean rats (ZLR). SPIR index showed a significant linear correlation with Φ 2 (Pearson's correlation coefficient, r = 0.91, R-square = 0.82, P 2 (P 2 , in the evaluation of the second-phase insulin secretion and of its alteration in Zucker Fatty Rats. Thus, the study proposes the SPIR, as a suitable index for a simple, reliable and low-cost quantification of the second-phase insulin secretion in ZFR.

T-wave alternans (TWA), an electrophysiologic phenomenon associated with ventricular arrhythmias, is usually detected from selected ECG leads. TWA amplitude measured in the 12-standard and the 3-orthogonal (vectorcardiographic) leads were compared here to identify which lead system yields a more adequate detection of TWA as a noninvasive marker for cardiac vulnerability to ventricular arrhythmias. Our adaptive match filter (AMF) was applied to exercise ECG tracings from 58 patients with an implanted cardiac defibrillator, 29 of which had ventricular tachycardia or fibrillation during follow-up (cases), while the remaining 29 were used as controls. Two kinds of TWA indexes were considered, the single-lead indexes, defined as the mean TWA amplitude over each lead (MTWAA), and lead-system indexes, defined as the mean and the maximum MTWAA values over the standard leads and over the orthogonal leads. Significantly (P < 0.05) higher TWA in the cases versus controls was identified only occasionally by the single-lead indexes (odds ratio: 1.0-9.9, sensitivity: 24-76%, specificity: 76-86%), and consistently by the lead-system indexes (odds ratio: 4.5-8.3, sensitivity: 57-72%, specificity: 76%). The latter indexes also showed a significant correlation (0.65-0.83) between standard and orthogonal leads. Hence, when using the AMF, TWA should be detected in all leads of a system to compute the lead-system indexes, which provide a more reliable TWA identification than single-lead indexes, and a better discrimination of patients at increased risk of cardiac instability. The standard and the orthogonal leads can be considered equivalent for TWA identification, so that TWA analysis can be limited to one-lead system.

Background: T-wave alternans (TWA), a harbinger of sudden cardiac death, associates to a broad variety of pathologies. In a previous study, we observed the presence of unstable and low-amplitude TWA also in healthy subjects, and considered it as “physiological TWA.” The possible existence of different TWA characteristics between males and female is investigated in the present work. Methods: Resting ECG recordings from 142 control healthy subjects, 77 males and 65 females, were submitted to our adaptive match filter (AMF) based method for TWA detection and characterization in terms of duration, amplitude, and their product. The 99.5th percentile of these parameters distributions over the entire control population and over the male and female subgroups, were used to define thresholds which delimit a gender-independent and male- and female-related TWA normality regions, respectively, out of which abnormal TWA cases (TWA+) are expected to fall. Clinical usefulness of these regions was tested using a population of 151 coronary artery disease (CAD) patients, divided into 128 males and 23 females. Results: In our control-female population, TWA duration was significantly longer than in controlmale population (65 ± 13 beat vs 52 ± 14 beat; P < 10 −6 ). Our gender-related normality regions allowed identification of 36 (23.8%) TWA+ cases among the CAD patients, 17 more than those obtained from a gender-independent region. All these 17 patients were CAD males with overthreshold TWA duration. Conclusions: TWA is a gender-related phenomenon. Definition of gender-related TWA normality regions improves identification of patients at increased TWA stability (i.e., prolonged TWA duration) and, thus, at increased risk of arrhythmic events. Ann Noninvasive Electrocardiol 2010;15(4):328–336 repolarization variability; sudden cardiac death; ECG signal processing

Enhanced modified moving average method (EMMAM) and correlation method (CM) for microvolt TWA identification are compared by aid of simulated ECG tracings (cases of absence of TWA and presence of stationary or time-varying TWA) and ECG recordings from healthy subjects (H-group) and patients who survived an acute myocardial infarction (AMI-group). The two competing methods were found to be equivalent when analyzing clean ECGs affected by stationary TWA. Non-stationary TWA is correctly tracked by the CM, whereas it is identified as stationary by the EMMAM. Moreover, the EMMAM suffers for its tendency to identify as TWA noise and other kinds of repolarization variability. Such limitation is most likely the cause of its false-positive TWA production. Finally, only the CM incorporates a local threshold criterion in the TWA detection algorithm which allows better discrimination between H and AMI groups, who are well known to be at increased risk to develop TWA.

To develop a new method for non-invasive identification of patients prone to ventricular tachyarrhythmia and sudden cardiac death, an adaptive match-filter (AMF) was applied to detect and characterize T-wave alternans (TWA) in 200 coronary artery diseased (CAD) patients compared with 176 healthy (H) subjects. TWA was characterized in terms of duration (TWAD), amplitude (TWAA), and magnitude (TWAM, defined as the product of TWAD times TWAA). A criterion derived from these parameters, estimated over the H-population, allowed discrimination between a risk (TWA+) and a normality (NO TWA) zone in the TWAD-TWAA plane. To gain further ability to discriminate among different risk levels, the TWA+ zone was divided into four sub-zones respectively characterized by low duration and low amplitude (LDLA), low duration and high amplitude (LDHA), high duration and low amplitude (HDLA), and high duration and high amplitude (HDHA). With our methodology, 21 CAD-patients (10.5%) were identified as TWA+, 9 falling in the LDLA zone, 4 in the HDLA, 7 in the LDHA, and 1 in the HDHA. These results are in agreement with clinical expectations and pave the way to further clinical follow-up studies finalized to analyze pathophysiological implications and risk factors associated to each TWA+ zone.

In the present study we evaluated insulin action and secretion in a group of 7 young Zucker fatty rats (ZFR), and in a group of 8 spontaneously hypertensive rats (SHR), compared with two control groups of 7 young Zucker lean rats (ZLR) and 8 Wistar Kyoto rats (WKY), respectively. Our goal is to broaden the characterization of glucose tolerance, including insulin secretion, in two animal models used for the characterization of human metabolic syndrome: the ZFR and the SHR. Reliable estimates of insulin sensitivity index, SI, was provided by minimal model analysis of IVGTT data. To characterize insulin secretion we calculated an index based on IVGTT data: AIRG, i.e. the acute insulin response after glucose bolus, related to the first phase insulin secretion. The ZFR showed a significantly (p

Microvolt T-wave alternans (TWA) increases with heart rate (HR). Thus, TWA is usually analyzed during exercise. However, since TWA during recovery is usually not analyzed, it is not clear if TWA and HR are linked by a one-to-one correspondence, or if it does exist a TWA hysteresis on HR. To investigate such issue TWA was identified in ECG recordings of 266 patients with implanted cardio-defibrillator acquired during a bicycle ergometer test, which included a HR-increasing exercise and a HR-decreasing recovery, both characterized by a HR from 80 to 125 bpm. TWA was always found to have a positive association with HR but, at each HR, exercise TWA was typically different from recovery TWA. Specifically, TWA increased exponentially during exercise (fitting-exponential-curve correlation: ρ=0.99, P

Sudden cardiac death remains one of the leading causes of death in developed countries. Left ventricular ejection fraction (LVEF) and f99 are two noninvasive indexes of cardiovascular risk (traditional the former and innovative the latter) which, taken singularly, have not shown sufficiently high SCD predictive power to justify preventive actions. Thus, the aim of the present study was to investigate if their combination improves predictability of ventricular arrhythmias. To this aim, ECG recordings from 266 ICD patients, of which 76 developed ventricular tachycardia or fibrillation during the 4-year follow-up (ICD_Cases), and 190 did not (ICD_Controls). The ECGs of each patient was used to compute the f99, a repolarization index defined as the frequency at which the cumulative power energy reaches 99%. Eventually, a logistic regression between LVEF and f99 was performed in order to derive a combined predictor (CP) of ventricular arrhythmia. Goodness of each index was evaluated in terms of the area under the receiver operator curve (AUC). When used singularly, LVEF and f99 respectively provided an AUC of 0.67 and 0.64. When combined to get CP=−0.15–0.05·LVEF+0.03·f99, this provided an AUC of 0.71. In conclusion, use of logistic regression improves LVEF and f99 predictability of ventricular arrhythmias.

A C-peptide-based assessment of β-cell function was performed here in the Zucker fatty rat, a suitable animal model of human metabolic syndrome. To this aim, a 90-min intravenous glucose tolerance test (IVGTT) was performed in seven Zucker fatty rats (ZFR), 7-to-9 week-old, and seven age-matched Zucker lean rats (ZLR). The minimal model of C-peptide (CPMM), originally introduced for humans, was adapted to Zucker rats and then applied to interpret IVGTT data. For a comprehensive evaluation of glucose tolerance in ZFR, CPMM was applied in combination with the minimal model of glucose kinetics (GKMM). Our results showed that the present CPMM-based interpretation of data is able to: 1) provide a suitable fit of C-Peptide data; 2) achieve a satisfactory estimation of parameters of interest 3) quantify both insulin secretion by estimating the time course of pre-hepatic secretion rate, SR(t), and total insulin secretion, TIS, and pancreatic sensitivity by means of three specific indexes of β-cell responsiveness to glucose stimulus (first-phase, Ф(1), second-phase, Ф(2), and steady-state, Ф(ss), never assessed in Zucker rats before; 4) detect the significant enhancement of insulin secretion in the ZFR, in face of a severe insulin-resistant state, previously observed only using a purely experimental approach. Thus, the methodology presented here represents a reliable tool to assess β-cell function in the Zucker rat, and opens new possibilities for the quantification of further processes involved in glucose homeostasis such as the hepatic insulin degradation.

Current techniques evaluating beat-to-beat variability of repolarization rely on accurate determination of T wave endpoints. This study proposes a T wave endpoint-independent method to quantify repolarization variability in a standard 12-lead ECG using a wavelet transformation. Our method was used to identify repolarization variability in long QT syndrome patients (LQTS) with the SCN5A sodium channel gene mutation. Using wavelet transformations based on the second Gaussian derivative, we evaluated repolarization variability in 11 LQTS patients with the mutation, 13 noncarrier family members, and 28 unrelated healthy subjects. Time-domain repolarization variability parameters (SDRTo, SDRTm) and wavelet parameters describing temporal (beat-to-beat) variability of repolarization in time (TVT) and in amplitude (TVA) were analyzed. Reproducibility of wavelet parameters and relationship of wavelet-based variability with heart rate and preceding RR interval were investigated. The wavelet-based method quantified beat-to-beat variability of the entire repolarization segment (regardless of QT interval identification) providing insight into variability in repolarization morphology. Our method showed that SCN5A carriers have significantly increased repolarization variability in amplitude (23% +/14% vs 8 +/- 4%, P < 0.001) and in time (14 +/- 17 ms vs 3 +/-2 ms, P < 0.004) compared to noncarriers. Variability of repolarization amplitude was found to be heart rate dependent with variability decreasing with increasing heart rate. Relative error describing reproducibility of TVA and TVT was < or = 5% and < or =10%, respectively. Our method quantifies repolarization variability in amplitude and in time without the need to identify T or U wave endpoints. Wavelet-detected repolarization variability contributes to phenotypic identification of SCN5A carriers, with more pronounced beat-to-beat variability in repolarization amplitude than in time.