1,721,033 research outputs found
A novel measure of atrial fibrillation organization based on symbolic analysis
Full text linkMeasures of electrical activity organization in atrial electrogram (AEG) are used to guide the ablation treatment in subjects with atrial fibrillation (AF). We proposed an improved morphological index for measuring the degree of organization in AEGs. As for other indexes, the metric provides an estimate of the probability of founding couples of similar waves and it increases with a higher AF organization. However, it also considers the order of arrival of the wavefronts on a set of bipolar electrodes (BE). Doing so, the index is inherently influenced by the direction of propagation of the wavefronts. To quantify organization, the AEGs were encoded with sequences of words of six symbols: three describing the order of arrival of the wavefronts on the BEs, while the others depending on the shape of each wave. The organization degree (OD) of each AEG was finally obtained as a function of the entropy of the sequence of words. The method was tested on 10 subjects before and after infusion of isoproterenol (ISO). During sinus rhythm, the effects of ISO did not significantly altered the organization of the atria (on average OD= 0.75 before and 0.74 after). Instead, in atrial fibrillation, ISO significantly reduced the level of organization (OD= 0.35 before vs 0.32 after, p < 0.05, paired t-test). The results were coherent with the pharmacological effects expected from the drug
Linear-Sigmoidal modelling of accelerometer features and Tinetti score for automatic fall risk assessment
No full textFalling in elderly is a worldwide major problem and it can lead to severe injuries or death. Despite the effort made to ensure home environments safe and foster healthy lifestyles, it is still necessary to provide methodologies that can be used at home for detect risk factors associated with falls. In this study, we proposed a new simple non-linear model, i.e., Linear-Sigmoidal model (LS), easy to fit and simple to interpret, used to model accelerometer features and outcome of the clinical scale Tinetti (clinical scale for fall risk prediction). Also, subjects with a score ≤ 18 were considered as high risk of falling. One-hundred-twelve subjects underwent to a Tinetti test while wearing a 3D axis accelerometer at the chest, and the Tinetti score used as gold standard. Ninety subjects were used as training set and twenty-two ones were employed to test the model. The same sets were used to assess the performance of the standard linear regression (LR). Seven accelerometer features and the body mass index were used in the model regression. LS resulted better than LR in terms of model agreement (R(2): 0.76 vs 0.72) and classification accuracy (0.91 vs 0.86) on the test set
Composition of feature extraction methods shows interesting performances in discriminating wakefulness and NREM sleep
No full textIntracranial electroencephalography (iEEG) is an invasive technique used to explore the cortical activity of the brain.
In this letter, we focused on features of iEEG signals recorded during wakefulness and non-rapid eye movement (NREM) sleep in order to find differences between the two states, respectively. We preliminary screened the data using standard deviation analysis (STD). Then, we compared and combined STD values with coefficients from wavelet decomposition (Daubechies mother wavelet of order 4). Resulting parameters were classified using an artificial neural network. STD analysis underlined two brain areas [superior temporal sulcus (STS) and intraparietal-sulcus and parietal transverse (IPS)] with different electrical activity in the two states.STDvalues of STS and IPS channels were highly correlated in time;therefore, only STSwas then used further in the features extraction analysis. Approximation and detail coefficients from Daubechies decomposition were used alone or in combination with the STD value. The overall accuracy of the pattern recognition was higher (98.57%), when features from different methods were used in combination. Our test was able to automatically recognize wake or NREM sleep status with very good discrimination performances using one single iEEG electrode
Effects of the series length on Lempel-Ziv complexity during sleep
No full textLempel-Ziv Complexity (LZC) has been demonstrated to be a powerful complexity measure in several biomedical applications. During sleep, it is still not clear how many samples are required to ensure robustness of its estimate when computed on beat-to-beat interval series (RR). The aims of this study were: i) evaluation of the number of necessary samples in different sleep stages for a reliable estimation of LZC; ii) evaluation of the LZC when considering inter-subject variability; and iii) comparison between LZC and Sample Entropy (SampEn). Both synthetic and real data were employed. In particular, synthetic RR signals were generated by means of AR models fitted on real data. The minimum number of samples required by LZC for having no changes in its average value, for both NREM and REM sleep periods, was 104 (p1000 when a tolerance of 5% is considered satisfying. The influence of the inter-subject variability on the LZC was first assessed on model generated data confirming what found (>104; p<;0.01) for both NREM and REM stage. However, on real data, without differentiate between sleep stages, the minimum number of samples required was 1.8×104. The linear correlation between LZC and SampEn was computed on a synthetic dataset. We obtained a correlation higher than 0.75 (p<;0.01) when considering sleep stages separately, and higher than 0.90 (p<;0.01) when stages were not differentiated. Summarizing, we suggest to use LZC with the binary quantization and at least 1000 samples when a variation smaller than 5% is considered satisfying, or at least 104 for maximal accuracy. The use of more than 2 levels of quantization is not recommended
Assessment of Spatial Heterogeneity of Ventricular Repolarization after Quinidine in Healthy Subjects
Full text linkWhen spatial heterogeneity of ventricular repolarization (SHVR) increases, vulnerability to ventricular arrhythmias, including lethal ones, has also been observed to increase. Drug-induced multi-ion-channel blocks may increase SHVR. Aim of this study is to non-invasively assess whether quinidine, a strong hERG potassium channel blocker with weaker effects on calcium and late sodium currents, increases SHVR. We analyzed data from 21 healthy subjects that received both the drug and a placebo and underwent to 12 leads Holter monitoring. From the recording, three 10-s ECGs were extracted at each of 16 predefined time-points. SHVR was assessed by the ν- index, which evaluates the standard deviation of the repolarization times from multi-lead ECG recordings. At any time point, a value of ν-index was computed for each of the three 10s ECGs and averaged if the difference in the mean RR of the 10s ECGs was lower than 50 ms. The ν- index did not change after the placebo (ν-index pre-dose = 29.2 ± 9.9 ms vs. ν-index post-dose1h = 26.7 ± 10.3, ns), whereas, after quinidine, it significantly increased one hour post-dose (ν-index pre-dose = 29.5 ± 10.2 ms vs. ν- index post-dose1h = 46.5 ± 33.8 ms, p = 0.01). Quinidine had its maximum effect on the ν-index 2.5 h after dose (ν-index post-dose2.5h = 53.6 ± 39.6 ms)
Quantification of ventricular repolarization heterogeneity during sotalol administration using the V-index
No full textA methodological assessment of phase-rectified signal averaging through simulated beat-to-beat interval time series
Full text linkAverage cardiac acceleration (AC) and deceleration (DC) capacity, as computed by Phase-Rectified Signal Averaging (PRSA), were introduced to detect quasi-periodic oscillations in RR series. Calculation of AC and DC depends on three parameters (T, Land s). The aim of the study was to provide further insights on AC/DC and on the appropriate selection of these parameters. Numerical simulations were focused on: i) changing the frequency of the oscillations detected by AC/DC; ii) testing the difference between AC and DC on synthetic data generated by AR models, fitted on real RR series; and iii) the effect of different growing and decreasing trends (lack of time-reversal symmetry). When computed on series generated by AR models, AC and DC were quantitatively equivalent, independently of the power spectrum (p < 0.05). The parameter s, more than T, affected the results, while values of L > s were equivalent. In fact, s selected the oscillations to which AC/DC resulted maximally sensitive. On the contrary, sawtooth-like series, with different growth and decrease rates, showed a marked difference between AC and DC. AC and DC are not simply related to spectral contents. Indeed, AC and DC are linked to the asymmetries between the rates of growth and decrease of heart rate, and might quantify differently underlying regulatory mechanisms
Accelerometric-based Features as Surrogate of Tinetti test
Full text linkINTRODUCTION
Risk of falling is estimated by visual inspection of patient’s movements using clinical scales, e.g., Tinetti test, Berg Balance Scale, Timed Up & Go etc. However, a continuous evaluation of such risk requires both subject hospitalization and expert clinical personnel. We believe that continuous automatic monitoring of the fall risk would provide rapid intervention as well as a reduction of the health care system costs. The main goal of this study is then to determine whether features extracted from low cost accelerometric signals can predict the physician assessments during a Tinetti test.
METHODS
Thirty-seven subjects were enrolled at the rehabilitation and medical research center INRCA (Istituto Nazionale Riposo e Cura Anziani), Casatenovo, Italy. Subjects using breathing supports, walkers and crutches were excluded from the study. All participants signed the informed consent. The median population age at time of the test was 75 (IQR = 81 - 70) years. 3D-axis accelerometric signals were collected using a wearable device (±8g, 12 bits, sampling rate 50 Hz, Geneactiv, Activinsights Limited, UK) positioned at the chest using an elastic band. Each subject underwent a Tinetti test divided in 8 motor tasks [2]. The Tinetti score was assigned by an expert physician and used as gold standard. For the present study only 4 items of the full test were considered (two for balance and two for gait): 1) Rise from the chair (score 0=unable, 1=able with arms, 2=able); 2) Immediate standing balance (score 0=unsteady, 1=steady with supports, 2=steady); 3) Step symmetry (score 0=asymmetric, 1=symmetric); and 4) Step continuity (score 0=discontinuous, 1=continuous). Features were extracted from the accelerometric signals. Sit to Stand Time and Balance after Standing (standard deviation of the vector magnitude within 5s after standing) were computed for the balance part. Step Symmetry and Step Regularity were determined on the vertical axis during walking phase as in [1]. ROC analysis was used to test features’ power in classifying the score assigned to each item by the physician. The area under the ROC curve (AUC) was computed for each combination of scores.
RESULTS
Proportion and age of people with high risk of falling (Tinetti score ≤ 18) were not statistically different to those with low risk (0.46 vs 0.54; median age 76 vs 74; p > 0.05). Male proportion was higher than that of female (0.78 vs 0.22; p 0.79) while Sit to Stand Time, Balance after Standing and Step Symmetry were sufficiently predictive (AUC > 0.60). Table 1. Area under the ROC curve for each feature. AUC was computed only when the number of
subjects for each score was at least 5 (NS=number of subjects < 5).
Test Item Feature Score 0 vs 1 0 vs 2 1 vs 2
1) Rise from the chair Sit to Stand Time NS NS 0.68
2) Immediate standing balance Balance after Standing NS 0.64 NS
3) Step symmetry Step Symmetry 0.60 - -
4) Step continuity Step Regularity 0.79 - -
DISCUSSION
Accelerometric-based features can provide useful information on the body movements as well as correctly classify the physician’s item assessments.
REFERENCES
[1] Moe-Nilssen R, Helbostad JL. J Biomech 2004; 37:121–126
[2] Rivolta MW, et al. EMBC 2015; 6935-6938
Use of detrended fluctuation analysis for sleep vs wake classification from heart rate variability
No full textPilot Test of a New Personal Health System Integrating Environmental and Wearable Sensors for Telemonitoring and Care of Elderly People at Home (SMARTA Project)
No full textBackground: The increase in life expectancy is accompanied by a growing number of elderly subjects affected by chronic comorbidities, a health issue which also implies important socioeconomic consequences. Shifting from hospital or community dwelling care towards a home personalized healthcare paradigm would promote active aging with a better quality of life, along with a reduction in healthcare-related costs. Objective: The aim of the SMARTA project was to develop and test an innovative personal health system integrating standard sensors as well as innovative wearable and environmental sensors to allow home telemonitoring of vital parameters and detection of anomalies in daily activities, thus supporting active aging through remote healthcare. Methods: A first phase of the project consisted in the definition of the health and environmental parameters to be monitored (electrocardiography and actigraphy, blood pressure and oxygen saturation, weight, ear temperature, glycemia, home interaction monitoring -water tap, refrigerator, and dishwasher), the feedbacks for the clinicians, and the reminders for the patients. It was followed by a technical feasibility analysis leading to an iterative process of prototype development, sensor integration, and testing. Once the prototype had reached an advanced stage of development, a group of 32 volunteers - including 15 healthy adult subjects, 13 elderly people with cardiac diseases, and 4 clinical operators - was recruited to test the system in a real home setting, in order to evaluate both technical reliability and user perception of the system in terms of effectiveness, usability, acceptance, and attractiveness. Results: The testing in a real home setting showed a good perception of the SMAR-TA system and its functionalities both by the patients and by the clinicians, who appreciated the user interface and the clinical governance system. The moderate system reliability of 65-70% evidenced some technical issues, mainly related to sensor integration, while the patient's user interface showed excellent reliability (100%). Conclusions: Both elderly people and clinical operators considered the SMARTA system a promising and attractive tool for improving patients' healthcare while reducing related costs and preserving quality of life. However, the moderate reliability of the system should prompt further technical developments in terms of sensor integration and usability of the clinical operator's user interface
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