Biomedical subjects

V X Afonso

Publications and source records attributed to V X Afonso.

4 recordsLinked to original sources

PubMed1999

Classification of premature ventricular complexes using filter bank features, induction of decision trees and a fuzzy rule-based system.

The classification of heart beats is important for automated arrhythmia monitoring devices. The study describes two different classifiers for the identification of premature ventricular complexes (PVCs) in surface ECGs. A decision-tree algorithm based on inductive learning from a training set and a fuzzy rule-based classifier are explained in detail. Traditional features for the classification task are extracted by analysing the heart rate and morphology of the heart beats from a single lead. In addition, a novel set of features based on the use of a filter bank is presented. Filter banks allow for time-frequency-dependent signal processing with low computational effort. The performance of the classifiers is evaluated on the MIT-BIH database following the AAMI recommendations. The decision-tree algorithm has a gross sensitivity of 85.3% and a positive predictivity of 85.2%, whereas the gross sensitivity of the fuzzy rule-based system is 81.3%, and the positive predictivity is 80.6%.

Decision Trees↗

PubMed1999

ECG beat detection using filter banks.

We have designed a multirate digital signal processing algorithm to detect heart beats in the electrocardiogram (ECG). The algorithm incorporates a filter bank (FB) which decomposes the ECG into subbands with uniform frequency bandwidths. The FB-based algorithm enables independent time and frequency analysis to be performed on a signal. Features computed from a set of the subbands and a heuristic detection strategy are used to fuse decisions from multiple one-channel beat detection algorithms. The overall beat detection algorithm has a sensitivity of 99.59% and a positive predictivity of 99.56% against the MIT/BIH database. Furthermore this is a real-time algorithm since its beat detection latency is minimal. The FB-based beat detection algorithm also inherently lends itself to a computationally efficient structure since the detection logic operates at the subband rate. The FB-based structure is potentially useful for performing multiple ECG processing tasks using one set of preprocessing filters.

Algorithms↗

PubMed1993

Applications of artificial neural networks for ECG signal detection and classification.

The authors have investigated potential applications of artificial neural networks for electrocardiographic QRS detection and beat classification. For the task of QRS detection, the authors used an adaptive multilayer perceptron structure to model the nonlinear background noise so as to enhance the QRS complex. This provided more reliable detection of QRS complexes even in a noisy environment. For electrocardiographic QRS complex pattern classification, an artificial neural network adaptive multilayer perceptron was used as a pattern classifier to distinguish between normal and abnormal beat patterns, as well as to classify 12 different abnormal beat morphologies. Preliminary results using the MIT/BIH (Massachusetts Institute of Technology/Beth Israel Hospital, Cambridge, MA) arrhythmia database are encouraging.

Arrhythmias, Cardiac↗

PubMed1992

The electrode system in impedance-based ventilation measurement.

In this paper, we determined which electrode types, sizes, and locations were best suited for impedance-based ventilation measurement. Optimal electrodes provide high signal-to-(motion) artifact ratio (SAR) and reliability by meeting the following criteria: 1) low baseline impedance, 2) high adhesion, 3) good physical stability, 4) large effective area, 5) thin with high flexibility. We compared 14 electrodes from two main groups: adhesive-gel and conductive rubber electrodes. Adhesive-gel electrodes are easy to apply, make good body contact, and do not slip during the course of an experiment. We found that higher SAR's are obtained when electrode area is increased by connecting several small electrodes together rather than by using a single electrode with a larger area. The peak SAR is achieved when two electrode arrays (area = 70 cm2) are centered at the 8th intercostal spaces on opposite midaxillary lines. To determine the optimal electrode locations, we placed 32 electrodes on the trunk and recorded impedance between 171 electrode combinations on ten normal adult subjects. Based on these data, we conclude that the SAR's are highest when one electrode is placed on the midpoint between the left and right second intercostal spaces on the sternum and the other electrode is placed in the opposite position on the back.

Adult↗