Comparison of different electrocardiography with vectorcardiography transformations

This paper deals with transformations from electrocardiographic (ECG) to vectorcardiographic (VCG) leads. VCG provides better sensitivity, for example for the detection of myocardial infarction, ischemia, and hypertrophy. However, in clinical practice, measurement of VCG is not usually used because it requires additional electrodes placed on the patient's body. Instead, mathematical transformations are used for deriving VCG from 12-leads ECG. In this work, Kors quasi-orthogonal transformation, inverse Dower transformation, Kors regression transformation, and linear regression-based transformations for deriving P wave (PLSV) and QRS complex (QLSV) are implemented and compared. These transformation methods were not yet compared before, so we have selected them for this paper. Transformation methods were compared for the data from the Physikalisch-Technische Bundesanstalt (PTB) database and their accuracy was evaluated using a mean squared error (MSE) and a correlation coefficient (R) between the derived and directly measured Frank's leads. Based on the statistical analysis, Kors regression transformation was significantly more accurate for the derivation of the X and Y leads than the others. For the Z lead, there were no statistically significant differences in the medians between Kors regression transformation and the PLSV and QLSV methods. This paper thoroughly compared multiple VCG transformation methods to conventional VCG Frank's orthogonal lead system, used in clinical practice.Web of Science1914art. no. 307

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Reconstructing Electrocardiogram Leads From a Reduced Lead Set Through the Use of Patient-Specific Transforms and Independent Component Analysis

In this exploration into electrocardiogram (ECG) lead reconstruction, two algorithms were developed and tested on a public database and in real-time on patients. These algorithms were based on independent component analysis (ICA). ICA was a promising method due to its implications for spatial independence of lead placement and its adaptive nature to changing orientation of the heart in relation to the electrodes. The first algorithm was used to reconstruct missing precordial leads, which has two key applications. The first is correcting precordial lead measurements in a standard 12-lead configuration. If an irregular signal or high level of noise is detected on a precordial lead, the obfuscated signal can be calculated from other nearby leads. The second is the reduction in the number of precordial leads required for accurate measurement, which opens up the surface of the chest above the heart for diagnostic procedures. Using only two precordial leads, the other four were reconstructed with a high degree of accuracy. This research was presented at the 33rd International Conference of the IEEE Engineering in Medicine and Biology Society in 2011.1 The second algorithm was developed to construct a full 12-lead clinical ECG from either three differential measurements or three standard leads. By utilizing differential measurements, the ECG could be reconstructed using wireless systems, which lack the common ground necessary for the standard measurement method. Using three leads distributed across the expanse of the space of the heart, all twelve leads were successfully reconstructed and compared against state of the art algorithms. This work has been accepted for publication in the IEEE Journal of Biomedical and Health Informatics.2 These algorithms show a proof of concept, one which can be further honed to deal with the issues of sorting independent components and improving the training sequences. This research also revealed the possibility of extracting and monitoring additional physiological information, such as a patient\u27s breathing rate from currently utilized ECG systems

Attractor reconstruction of an impact oscillator for parameter identification

Peer reviewedPreprin

Rate-Distortion Classification for Self-Tuning IoT Networks

Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the monitored signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples