1,721,171 research outputs found
Data for: Surface electromyography low-frequency content: assessment in isometric conditions after electrocardiogram cancellation by the segmented-beat modulation method
No full textData description“Signals.mat” is a Matlab file, that contains the signals of the clinical study. The file contains a Matlab structure called “Signals”, that has 10 fields, one for each subject. The fields are entitled S1-S10. Each subject repeats the movement (Functional Reach) three times, thus for each subject, there are three sub-fields, called “first”, ”second” and “third”, that refer to the repetition. Each field contains a matrix with 6 rows, that provides the simultaneously acquired signals. Specifically, from the first to the sixth row, they are: left clavicle, sternocleidomastoideus, erectores spinae at L4 level, rectus abdominis, rectus femoris and tibialis anterior.Sampling frequency is 2000Hz and the length of the signals is 30s.For any further information, please contact Laura Burattini, PhD ([email protected]).Details can be found in the corresponding paper:“Sbrollini A., Strazza A., Candelaresi S., Marcantoni I., Morettini M., Fioretti S., Di Nardo F., Burattini L. Surface electromyography low-frequency content: assessment in isometric conditions after electrocardiogram cancellation by the segmented-beat modulation method”
Data for: Surface electromyography low-frequency content: assessment in isometric conditions after electrocardiogram cancellation by the segmented-beat modulation method
No full textData description“Signals.mat” is a Matlab file, that contains the signals of the clinical study. The file contains a Matlab structure called “Signals”, that has 10 fields, one for each subject. The fields are entitled S1-S10. Each subject repeats the movement (Functional Reach) three times, thus for each subject, there are three sub-fields, called “first”, ”second” and “third”, that refer to the repetition. Each field contains a matrix with 6 rows, that provides the simultaneously acquired signals. Specifically, from the first to the sixth row, they are: left clavicle, sternocleidomastoideus, erectores spinae at L4 level, rectus abdominis, rectus femoris and tibialis anterior.Sampling frequency is 2000Hz and the length of the signals is 30s.For any further information, please contact Laura Burattini, PhD ([email protected]).Details can be found in the corresponding paper:“Sbrollini A., Strazza A., Candelaresi S., Marcantoni I., Morettini M., Fioretti S., Di Nardo F., Burattini L. Surface electromyography low-frequency content: assessment in isometric conditions after electrocardiogram cancellation by the segmented-beat modulation method”.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
Classification of drug-induced hERG potassium-channel block from electrocardiographic T-wave features using artificial neural networks
Full text linkBackground: 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, ΔERD30% and ΔTS/A, respectively, were computed as difference
between values at each postdose time‐point and the predose time‐point. Thus, the
dataset contained 286 ΔERD30%‐ΔTS/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
Neonatal Clinical Outcomes: a Comparative Analysis
No full textThe most popular neonatal clinical outcomes, which are blood pH (PH), base excess (BE) and Apgar after 5 minutes from birth (AP5), may provide contrasting information. Thus, aim of the paper is to perform a critical evaluation and comparison of PH, BE and AP5. Reliability of neonatal clinical outcomes was evaluated in relation to perinatal features. Neonatal and fetal cardiotocographic data of 391 newborns (CTU-CHB Intrapartum Cardiotocography Database) were analyzed. Newborns were classified as positive (i.e., as showing critical conditions) if PH<7.10 or BE<-10mmol/l or AP5<7, as negative (i.e., as showing healthy conditions) otherwise. Agreement between pairs of neonatal clinical outcomes was evaluated by computing the correlation coefficient. Fetal decelerations were characterized in terms of rate of occurrence, depth, mean, duration, and area. Correlation between PH and BE, PH and AP5 and BE and AP5 was 0.83, 0.45 and 0.38 (P<0.01), respectively; 329 newborns (84%) were equally classified by all neonatal clinical outcomes, 5 as positive and 324 as negative. Deceleration depth and rate of occurrence were comparable among positive/negative classes, while deceleration mean, duration and area were systematically higher in the positive than in the negative classes, also statistically only for PH classification. Positive class by PH counted the highest number of small newborns; large newborns were similarly distributed over all positive classes. Objective neonatal clinical outcomes, and in particular PH, seems to be more reliable than subjective clinical outcomes, and thus should be preferable for describing neonatal health status
Cardiorespiratory DB: Collection of cardiorespiratory data acquired during normal breathing, deep breathing and breath holding
Full text linkThe database is constituted by 50 datasets containing cardiorespiratory signals acquired from 50 healthy volunteer subjects (one dataset for each subject; 23 males and 27 females; age: 23±5 years) while performing normal breathing, deep breathing, and breath holding, and two spreadsheet files, namely the “SubjectsInfo.xlsx” and “DBInfo.xlsx” containing the metadata of subjects (including demographic data) and of acquired signals, respectively. Cardiorespiratory signals consisted in simultaneously recorded 12-lead electrocardiograms acquired by the clinical M12 Global InstrumentationR digital Holter ECG recorder, and single-lead electrocardiograms and respiration signals acquired by the wearable chest strap BioHarness 3.0 by Zephyr. The database may be useful to: (1) validate the use of wearable sensors in the acquisition of cardiorespiratory data during different respiration kinds, including apnea; (2) investigate the physiological association between cardiovascular and respiratory systems; (3) validate algorithms able to indirectly extract the respiration signal from the electrocardiogram; (4) study the fatigue level induced by a series of controlled respiration patterns; and (5) investigate the effect of COVID-19 infection on the cardiorespiratory system
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Symbolic Analysis of Heart-Rate Variability during Training and Competition in Short Distance Running
No full textDuring physical exercise, the assessment of cardiac autonomic regulation through the analysis of heart-rate variability is difficult due to non-stationarity of RR intervals. The short duration of stationary epoch of RR-interval series makes not possible the computation of classical time-and frequency-domain parameters. Symbolic analysis can deal with short epoch of RR interval; thus, this study aims to apply symbolic analysis to analyze heart-rate variability of short-distance runners during training and competition. Data consists of RR-intervals extracted from 30s-long electrocardiograms acquired by KardiaMobile of Alivecor in 8 short-distance runners (1/7 M/F, 17[16;20] years) during training and competition. Symbolic analysis classifies a reduced number (in this study, three) of consecutive RR intervals in four patterns according to the sign and number of variations: no variation (0V); one variation (1V); two like variations (2LV); two unlike variations (2UV). An increase in low amount of variations (0V or 1V patterns) is usually linked with increased sympathetic control and vagal withdrawal, while an increase in high amount of variations (2LV and 2UV patterns) is usually linked with sympathetic withdrawal and increased vagal control. In our results, pattern 0V /2LV increases/decreases from rest to post exercise and decreases/increases during recovery, in both training and competition. Thus, our results confirm the opposite trends between low and high variations of symbolic patterns. In conclusion, symbolic analysis seems to be an efficient tool to characterize heart-rate variability during physical exercise at different level of psychophysical stress
Review on Cardiorespiratory Complications after SARS-CoV-2 Infection in Young Adult Healthy Athletes
Full text linkThis review analyzes scientific data published in the first two years of the COVID-19 pandemic with the aim to report the cardiorespiratory complications observed after SARS-CoV-2 infection in young adult healthy athletes. Fifteen studies were selected using PRISMA guidelines. A total of 4725 athletes (3438 males and 1287 females) practicing 19 sports categories were included in the study. Information about symptoms was released by 4379 (93%) athletes; of them, 1433 (33%) declared to be asymptomatic, whereas the remaining 2946 (67%) reported the occurrence of symptoms with mild (1315; 45%), moderate (821; 28%), severe (1; 0%) and unknown (809; 27%) severity. The most common symptoms were anosmia (33%), ageusia (32%) and headache (30%). Cardiac magnetic resonance identified the largest number of cardiorespiratory abnormalities (15.7%). Among the confirmed inflammations, myocarditis was the most common (0.5%). In conclusion, the low degree of symptom severity and the low rate of cardiac abnormalities suggest that the risk of significant cardiorespiratory involvement after SARS-CoV-2 infection in young adult athletes is likely low; however, the long-term physiologic effects of SARS-CoV-2 infection are not established yet. Extensive cardiorespiratory screening seems excessive in most cases, and classical pre-participation cardiovascular screening may be sufficient
Model-Based Estimation of Electrocardiographic QT Interval from Phonocardiographic Heart Sounds in Healthy Subjects
Full text linkThe 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<10-13; model validation: p=0.6, P<10-10); median error is 1 ms (<0.5 in %). Thus, the proposed model provides a reliable estimation of QT interval from PCG heart sounds in healthy subjects
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