MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

Aims:Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods:A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology,duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results:QRS morphologies in limb leads are mainly determined by ventricular anatomy,while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration ismainly influenced by myocardial volume, while it is hardly affected by the torso anatomyor position. An average increase of 0.12±0.05 ms in QRS duration is obtained for eachcm3of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion:Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave theway toward their use as testbeds in silico clinical trial

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Вейвлет-анализ кардиосигналов в среде Matlab

This article is devoted to the problem of accurate detection of cardiosignal QRS-complexes for early diagnosis of various diseases of human cardiovascular system. For that purpose various algorithms based on either digital filtering methods or mathematical modeling of ECG signal particular sections are used. However, all listed methods have a number of disadvantages impairing the accuracy of QRS-complex determination. Yet wavelet transforms enabling accurate identification of local features for non-stationary signals are becoming more common in various fields of technology.The article presents wavelet spectrogram calculation by means of various wavelets and levels of decomposition in the Wavelet Toolbox environment. Based on wavelet coefficient amplitudes, the presence of jumps, discontinuities, i.e. QRS complex can be identified. In addition, by comparing the form of the QRS complex and the graph of the scaling function of different wavelets, the most optimal wavelet is determined for identifying the QRS complex, as well as noise suppression in cardiosignals.The obtained results can be used not only in electrocardiography, but also in solving problems of identification and processing of various types of signals. Рассмотрена проблема точного обнаружения QRS-комплексов кардиосигналов с целью раннего диаг­ностирования болезней сердечно-сосудистой системы человека. Для этой цели применяются различные алгоритмы, основанные либо на методах цифровой фильтрации, либо на математическом моделирова­нии отдельных участков электрокардиограммы. Однако указанные методы имеют ряд недостатков, снижающих точность определения QRS-комплексов. В то же время вейвлет-преобразования, позволяю­щие практически безошибочно идентифицировать локальные особенности нестационарных сигналов, находят все большее применение в различных областях техники.Представлено получение вейвлет-спектрограмм в среде Wavelet Toolbox с использованием различных вейвлетов и уровней декомпозиции. По амплитудам вейвлет-коэффициентов можно судить о наличии скачков, разрывов, т. е. идентифицировать QRS-комплекс. На основании сравнения вида QRS-комплекса и графиков масштабирующих функций различных вейвлетов определен наиболее оптимальный вейвлет для идентификации QRS-комплекса, а также шумоподавления в кардиосигналах.Полученные результаты могут применяться не только в электрокардиографии, но и при решении задач идентификации и обработки сигналов различного типа.

Techniques for Identification of Left Ventricular Asynchrony for Cardiac Resynchronization Therapy in Heart Failure

The most recent treatment option of medically refractory heart failure includes cardiac resynchronization therapy (CRT) by biventricular pacing in selected patients in NYHA functional class III or IV heart failure. The widely used marker to indicate left ventricular (LV) asynchrony has been the surface ECG, but seems not to be a sufficient marker of the mechanical events within the LV and prediction of clinical response. This review presents an overview of techniques for identification of left ventricular intra- and interventricular asynchrony. Both manuscripts for electrical and mechanical asynchrony are reviewed, partly predicting response to CRT. In summary there is still no gold standard for assessment of LV asynchrony for CRT, but both traditional and new echocardiographic methods have shown asynchronous LV contraction in heart failure patients, and resynchronized LV contraction during CRT and should be implemented as additional methods for selecting patients to CRT

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network