Assessment of spatial heterogeneity of ventricular repolarization after multi-channel blocker drugs in healthy subjects

Background and objectives: In contrast to potassium channel blockers, drugs affecting multiple channels seem to reduce torsadogenic risks. However, their effect on spatial heterogeneity of ventricular repolarization (SHVR) is still matter of investigation. Aim of this work is to assess the effect of four drugs blocking the human ether-\ue0-go-go-related gene (hERG) potassium channel, alone or in combination with other ionic channel blocks, on SHVR, as estimated by the V-index on short triplicate 10 s ECG. Methods: The V-index is an estimate of the standard deviation of the repolarization times of the myocytes across the entire myocardium, obtained from multi-lead surface electrocardiograms. Twenty-two healthy subjects received a pure hERG potassium channel blocker (dofetilide) and 3 other drugs with additional varying degrees of sodium and calcium (L-type) channel block (quinidine, ranolazine, and verapamil), as well as placebo. A one-way repeated-measures Friedman test was performed to compare the V-index over time. Results: Computer simulations and Bland-Altman analysis supported the reliability of the estimates of V-index on triplicate 10 s ECG. Ranolazine, verapamil and placebo did not affect the V-index. On the contrary, after quinidine and dofetilide administration, an increase of V-index from predose to its peak value was observed (\u394\u394V-index values were 19 ms and 27 ms, respectively, p < 0.05). Conclusions: High torsadogenic drugs (dofetilide and quinidine) affected significantly the SHVR, as quantified by the V-index. The metric has therefore a potential in assessing drug arrhythmogenicity

Design and Validation of a Minimal Complexity Algorithm for Stair Step Counting

Wearable sensors play a significant role for monitoring the functional ability of the elderly and in general, promoting active ageing. One of the relevant variables to be tracked is the number of stair steps (single stair steps) performed daily, which is more challenging than counting flight of stairs and detecting stair climbing. In this study, we proposed a minimal complexity algorithm composed of a hierarchical classifier and a linear model to estimate the number of stair steps performed during everyday activities. The algorithm was calibrated on accelerometer and barometer recordings measured using a sensor platform worn at the wrist from 20 healthy subjects. It was then tested on 10 older people, specifically enrolled for the study. The algorithm was then compared with other three state-of-the-art methods, which used the accelerometer, the barometer or both. The experiments showed the good performance of our algorithm (stair step counting error: 13.8%), comparable with the best state-of-the-art (p > 0.05), but using a lower computational load and model complexity. Finally, the algorithm was successfully implemented in a low-power smartwatch prototype with a memory footprint of about 4 kB

Accelerometric-based Features as Surrogate of Tinetti test

INTRODUCTION Risk of falling is estimated by visual inspection of patient\u2019s 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 (\ub18g, 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\u2019 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 64 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\u2019s item assessments. REFERENCES [1] Moe-Nilssen R, Helbostad JL. J Biomech 2004; 37:121\u2013126 [2] Rivolta MW, et al. EMBC 2015; 6935-6938

Concurrent clustering and classification for assessing the risk of falling during ageing

During ageing, fall prevention represents one of the most sustainable plan of action to promote active ageing and reduce health care costs. To significantly prevent the falls, it is necessary to deploy predictive models that can accurately assess the risk of falling. However, current machine learning methodologies do not offer insights about the rules used for the assessment. Here, we proposed a method capable to concurrently cluster and classify data in the context of fall risk assessment. Such clustering provides support in analyzing the classification performed. We applied the method on a dataset composed by accelerometer signals collected using a wearable sensor from 90 subjects that underwent a Tinetti test (i.e., a clinical scale meant to assess the risk of falling). Thirty-three subjects had a Tinetti score <= 18 and considered has having high risk of falling. A training-validation-test procedure was designed to determine the classification accuracy of the proposed methodology. We evaluated the automatic clustering by observing how the subjects were splitted into three groups. The method achieved a test set accuracy of 0.85. The obtained clusters supported the presence of three macro groups, i.e., low risk, high risk and borderline

A novel measure of atrial fibrillation organization based on symbolic analysis

Measures 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

Evaluation of spatial heterogeneity of ventricular repolarization during coronary angioplasty

Percutaneous transluminal coronary angioplasty (PTCA) is a surgery procedure meant to open up blocked coronary arteries using balloon inflation. It also provides an excellent model to investigate the electrophysiological changes due to ischemia. In this work, we tested whether cardiac ischemia induced by prolonged balloon inflations might lead to changes in spatial heterogeneity of ventricular repolarization (SHVR), as measured by the V-index. With this aim, we analyzed the STAFF III dataset acquired during PTCA. Forty-subjects underwent a PTCA procedure with prolonged balloon inflations and the Vindex was estimated pre-, during and post- inflation. Two main results were found. First, V-index values during pre- and postinflation were not statistically significantly different (p > 0:05), suggesting a complete recovery after PTCA. Second, SHVR during the second part of the occlusion increased with respect to pre-inflation (median difference: 18.84 ms; p < 0:05). In conclusion, the V-index detected changes in SHVR due to the presence of early-stage cardiac ischemia

Refined Ventricular Activity Cancellation in Electrograms During Atrial Fibrillation by Combining Average Beat Subtraction and Interpolation

Many techniques have been developed to cancel the ventricular interference in atrial electrograms (AEG) during atrial fibrillation. In particular, average beat subtraction (ABS) and interpolation are among those mostly adopted. However, ABS usually leaves high power residues and discontinuity at the borders, whereas interpolation totally substitutes the residual activity with a forecasting that might fail at the center of the cancellation segment. In this study, we proposed a new algorithm to refine the ventricular estimate provided by ABS, in such a way that the residual activity should likely be distributed as the local atrial activity. Briefly, the local atrial activity is first modeled with an autoregressive (AR) process, then the estimate is refined by maximizing the log likelihood of the atrial residual activity according to the fitted AR model. We tested the new algorithm on both synthetic and real AEGs, and compared the performance with other four algorithms (two variants of ABS, interpolation and zero substitution). On synthetic data, our algorithm outperformed all the others in terms of average root mean square error (0.043 vs 0.046 for interpolation; p &lt; 0.05). On real data, our methodology outperformed two variants of ABS (p &lt; 0.05) and performed similarly to interpolation when considering the high power residues left (both &lt; 5%), and the log likelihood with the fitted AR model

A Novel Measure of Atrial Fibrillation Organization based on Symbolic Analysis,

Proceedings of Computing in Cardiology Conference, (2012) Krakow, Poland

Synthetic atrial electrogram generator

Atrial electrogram (AEGs) recorded invasively inside the atrium can be analyzed to assess the organization of the atrial electrical activity. These organization measures are commonly based either on the repeatability/regularity of the atrial activations or on the correlation/synchronicity among electrograms recorded in different sites. For many applications, it could be useful to have a synthetic AEG generator to test new methods on. So far, models capable to reproduce realistic AEGs do not exist yet. Aim of this study is to propose a unified approach to generate synthetic AEGs during sinus rhythm (SR) and atrial tachyarrhythmias, namely atrial fibrillation (AF) and atrial flutter (AFL). In particular, three different Wells organizations classes and different atrio-ventricular conductions will be considered during AF and AFL, respectively. A database of simulated signals during AF was created, containing AEGs with different degrees of Wells\u2019 organization and different dominant frequencies. These AEGs were tested using spectral analysis assessing spectral concentration (SC), and wave-morphology similarity (WMS). Both indexes were in agreement with those presented in the relevant literature

Composition of feature extraction methods shows interesting performances in discriminating wakefulness and NREM sleep

Intracranial 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