Quantitative Regular Expressions for Arrhythmia Detection Algorithms

Motivated by the problem of verifying the correctness of arrhythmia-detection algorithms, we present a formalization of these algorithms in the language of Quantitative Regular Expressions. QREs are a flexible formal language for specifying complex numerical queries over data streams, with provable runtime and memory consumption guarantees. The medical-device algorithms of interest include peak detection (where a peak in a cardiac signal indicates a heartbeat) and various discriminators, each of which uses a feature of the cardiac signal to distinguish fatal from non-fatal arrhythmias. Expressing these algorithms' desired output in current temporal logics, and implementing them via monitor synthesis, is cumbersome, error-prone, computationally expensive, and sometimes infeasible. In contrast, we show that a range of peak detectors (in both the time and wavelet domains) and various discriminators at the heart of today's arrhythmia-detection devices are easily expressible in QREs. The fact that one formalism (QREs) is used to describe the desired end-to-end operation of an arrhythmia detector opens the way to formal analysis and rigorous testing of these detectors' correctness and performance. Such analysis could alleviate the regulatory burden on device developers when modifying their algorithms. The performance of the peak-detection QREs is demonstrated by running them on real patient data, on which they yield results on par with those provided by a cardiologist.Comment: CMSB 2017: 15th Conference on Computational Methods for Systems Biolog

Smart Device for the Determination of Heart Rate Variability in Real Time

This work presents a first approach to the design, development, and implementation of a smart device for the real-time measurement and detection of alterations in heart rate variability (HRV). The smart device follows a modular design scheme, which consists of an electrocardiogram (ECG) signal acquisition module, a processing module and a wireless communications module. From five-minute ECG signals, the processing module algorithms perform a spectral estimation of the HRV. The experimental results demonstrate the viability of the smart device and the proposed processing algorithms.Fundación Pública Andaluza Progreso y Salud. Gobierno de Andalucía PI-0010-2013 y PI-0041-2014Ministerio de Economía y Competitividad (Instituto de Salud Carlos III) PI15 / 00306 y DTS15 / 00195CIBER-BBN INT-2-CAR

State of the art: iterative CT reconstruction techniques

Owing to recent advances in computing power, iterative reconstruction (IR) algorithms have become a clinically viable option in computed tomographic (CT) imaging. Substantial evidence is accumulating about the advantages of IR algorithms over established analytical methods, such as filtered back projection. IR improves image quality through cyclic image processing. Although all available solutions share the common mechanism of artifact reduction and/or potential for radiation dose savings, chiefly due to image noise suppression, the magnitude of these effects depends on the specific IR algorithm. In the first section of this contribution, the technical bases of IR are briefly reviewed and the currently available algorithms released by the major CT manufacturers are described. In the second part, the current status of their clinical implementation is surveyed. Regardless of the applied IR algorithm, the available evidence attests to the substantial potential of IR algorithms for overcoming traditional limitations in CT imaging

Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). A 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Starting from a single seed point placed manually or automatically anywhere in a coronary artery, a tracker follows the vessel centerline in two directions using the predictions of the CNN. Tracking is terminated when no direction can be identified with high certainty. The CNN was trained using 32 manually annotated centerlines in a training set consisting of 8 CCTA images provided in the MICCAI 2008 Coronary Artery Tracking Challenge (CAT08). Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93.7% with 96 manually annotated reference centerlines. Extracted centerline points were highly accurate, with an average distance of 0.21 mm to reference centerline points. In a second test set consisting of 50 CCTA scans, 5,448 markers in the coronary arteries were used as seed points to extract single centerlines. This showed strong correspondence between extracted centerlines and manually placed markers. In a third test set containing 36 CCTA scans, fully automatic seeding and centerline extraction led to extraction of on average 92% of clinically relevant coronary artery segments. The proposed method is able to accurately and efficiently determine the direction and radius of coronary arteries. The method can be trained with limited training data, and once trained allows fast automatic or interactive extraction of coronary artery trees from CCTA images.Comment: Accepted in Medical Image Analysi

Microbubble Cavitation Imaging

Ultrasound cavitation of microbubble contrast agents has a potential for therapeutic applications such as sonothrombolysis (STL) in acute ischemic stroke. For safety, efficacy, and reproducibility of treatment, it is critical to evaluate the cavitation state (moderate oscillations, stable cavitation, and inertial cavitation) and activity level in and around a treatment area. Acoustic passive cavitation detectors (PCDs) have been used to this end but do not provide spatial information. This paper presents a prototype of a 2-D cavitation imager capable of producing images of the dominant cavitation state and activity level in a region of interest. Similar to PCDs, the cavitation imaging described here is based on the spectral analysis of the acoustic signal radiated by the cavitating microbubbles: ultraharmonics of the excitation frequency indicate stable cavitation, whereas elevated noise bands indicate inertial cavitation; the absence of both indicates moderate oscillations. The prototype system is a modified commercially available ultrasound scanner with a sector imaging probe. The lateral resolution of the system is 1.5 mm at a focal depth of 3 cm, and the axial resolution is 3 cm for a therapy pulse length of 20 mu s. The maximum frame rate of the prototype is 2 Hz. The system has been used for assessing and mapping the relative importance of the different cavitation states of a microbubble contrast agent. In vitro (tissue-mimicking flow phantom) and in vivo (heart, liver, and brain of two swine) results for cavitation states and their changes as a function of acoustic amplitude are presented

Impact of atrial fibrillation on the cardiovascular system through a lumped-parameter approach

Atrial fibrillation (AF) is the most common arrhythmia affecting millions of people in the Western countries and, due to the widespread impact on the population and its medical relevance, is largely investigated in both clinical and bioengineering sciences. However, some important feedback mechanisms are still not clearly established. The present study aims at understanding the global response of the cardiovascular system during paroxysmal AF through a lumped-parameter approach, which is here performed paying particular attention to the stochastic modeling of the irregular heartbeats and the reduced contractility of the heart. AF can be here analyzed by means of a wide number of hemodynamic parameters and avoiding the presence of other pathologies, which usually accompany AF. Reduced cardiac output with correlated drop of ejection fraction and decreased amount of energy converted to work by the heart during blood pumping, as well as higher left atrial volumes and pressures are some of the most representative results aligned with the existing clinical literature and here emerging during acute AF. The present modeling, providing new insights on cardiovascular variables which are difficult to measure and rarely reported in literature, turns out to be an efficient and powerful tool for a deeper comprehension and prediction of the arrythmia impact on the whole cardiovascular system.Comment: 16 pages, 8 figures, 2 tables, Medical & Biological Engineering & Computing, 2014, Print ISSN: 0140-0118, Online ISSN: 1741-044