P and R Wave Detection in Complete Congenital Atrioventricular Block

Complete atrioventricular block (type III AVB) is characterized by an absence of P wave transmission to ventricles. This implies that QRS complexes are generated in an autonomous way and are not coordinated with P waves. This work introduces a new algorithm for the detection of P waves for this type of pathology using non-invasive electrocardiographic surface leads. The proposed algorithm is divided into three stages. In the first stage, the R waves located by a QRS detector are used to generate the RR series and time references for the other stages of the algorithm. In the second stage, the ventricular activity (QT segment) is removed by using an adaptive filter that obtains an averaged pattern of the QT segment. In the third stage, a new P wave detector is applied to the residual signal obtained after QT cancellation in order to detect P wave locations and get the PP time series. Eight Holter records from patients with congenital type III AVB were used to verify the proposed algorithm. Although further improvements should be made to improve the algorithm¿s performance, the results obtained show high average values of sensitivity (90.52 %) and positive prediction (90.98%)

Biomagnetism using SQUIDs: status and perspectives

Biomagnetism involves the measurement and analysis of very weak local magnetic fields of living organisms and various organs in humans. Such fields can be of physiological origin or due to magnetic impurities or markers. This paper reviews existing and prospective applications of biomagnetism in clinical research and medical diagnostics. Currently, such applications require sensitive magnetic SQUID sensors and amplifiers. The practicality of biomagnetic methods depends especially on techniques for suppressing the dominant environmental electromagnetic noise, and on suitable nearly real-time data processing and interpretation methods. Of the many biomagnetic methods and applications, only the functional studies of the human brain (magnetoencephalography) and liver susceptometry are in clinical use, while functional diagnostics of the human heart (magnetocardiography) approaches the threshold of clinical acceptance. Particularly promising for the future is the ongoing research into low-field magnetic resonance anatomical imaging using SQUIDS

SQUID gradiometry for magnetocardiography using different noise cancellation techniques

Magnetocardiographic (MCG) measurements in unshielded environment require efficient noise cancellation techniques. We have applied two software gradiometry methods to analyze the time series of signal and reference data recorded outside magnetic shielding with high temperature superconducting quantum interference device (HTS SQUID) based gradiometers. One method uses adaptive frequency dependent gradiometer coefficients determined in the Fourier domain to subtract the reference from the signal data. The other method combines recently developed techniques for nonlinear projection with properties of the wavelet transform to extract noise in state space. The analyzed MCG data sets showed improved signal-to-noise ratios for both methods as compared to the data recorded with the electronic gradiometer. In this way, it is possible to increase the bandwidth from 130 Hz for our electronic gradiometer to 250 Hz without using any additional filtering

A Computationally Efficient SUPANOVA: Spline Kernel Based Machine Learning Tool

Summary. Many machine learning methods just consider the quality of prediction results as their final purpose. To make the prediction process transparent (reversible), spline kernel based methods were proposed by Gunn. However, the original solution method, termed SUpport vector Parsimonious ANOVA (SUPANOVA) was computationally very complex and demanding. In this paper, we propose a new heuristic to compute the optimal sparse vector in SUPANOVA that replaces the original solver for the convex quadratic problem of very high dimensionality. The resulting system is much faster without the loss of precision, as demonstrated in this paper on two benchmarks: the iris data set and the Boston housing market data benchmark.