Frank J. Owens

Multi-component based cross correlation beat detection in electrocardiogram analysis

BackgroundThe first stage in computerised processing of the electrocardiogram is beat detection. This involves identifying all cardiac cycles and locating the position of the beginning and end of each of the identifiable waveform components. The accuracy at which beat detection is performed has significant impact on the overall classification performance, hence efforts are still being made to improve this process.MethodsA new beat detection approach is proposed based on the fundamentals of cross correlation and compared with two benchmarking approaches of non-syntactic and cross correlation beat detection. The new approach can be considered to be a multi-component based variant of traditional cross correlation where each of the individual inter-wave components are sought in isolation as opposed to being sought in one complete process. Each of three techniques were compared based on their performance in detecting the P wave, QRS complex and T wave in addition to onset and offset markers for 3000 cardiac cycles.ResultsResults indicated that the approach of multi-component based cross correlation exceeded the performance of the two benchmarking techniques by firstly correctly detecting more cardiac cycles and secondly provided the most accurate marker insertion in 7 out of the 8 categories tested.ConclusionThe main benefit of the multi-component based cross correlation algorithm is seen to be firstly its ability to successfully detect cardiac cycles and secondly the accurate insertion of the beat markers based on pre-defined values as opposed to performing individual gradient searches for wave onsets and offsets following fiducial point location.

Multiple motion estimation and segmentation in transparency

In this paper a mechanism for computing multiple motion models along with the corresponding regions of support for a sequence of images is presented. The mechanism is an indirect parametric motion estimation technique. Firstly, a robust technique based on the fundamental constraint equation of multiple optical flow is used to estimate a dense multiple vector field of the scene. Secondly, from the recovered low-level data, motion models and their corresponding regions of support are estimated through a variant of the expectation-maximisation (EM) algorithm. The proposed algorithm is shown to provide good motion estimates and regions of support.

Using known motion fields for image separation in transparency

The problem of separating moving image patterns from their transparent combination is considered. From two consecutive frames, the authors show how the separation of two image-components using their known motion fields can be achieved. The formulation is first developed for simple translation motion and then extended to more complex cases.

Implementation of a smart antenna system with an improved NCMA algorithm

This paper presents the possibility of using adaptive algorithms for digital beamforming purposes. A normalized constant modulus algorithm (NCMA) is implemented in a standard FPGA. In this way, a simple and non-hardware-intensive, smart antenna system in combination with a software-defined radio (SDR) has been realized for mobile FM reception. A new kind of algorithm initialization, leads to an improvement in startup behavior. The quality in signal separation makes the NCMA algorithm also suitable for MIMO purposes. The NCMA algorithm increases the reception quality for mobile communication systems dramatically.

A simultaneous full-duplex speech and electrocardiogram communications system

A system has been developed allowing the simultaneous communication of full-duplex speech and multiple electrocardiograms in real time. A single bandlimited channel is used, a dial-up PSTN line providing a 3 KHz bandwidth. The full-duplex speech is compressed to 2400 b/s using linear predictive coding whilst multiple electrographic signals are processed by a novel ECG compression technique. Extensive use of digital signal processing reduces the combined bit rate to less than 9600 b/s, allowing the use of a cost effective commercial modem. The communication system allows a hospital based clinician to provide diagnostic and treatment advice to a remote location, thus improving patient care.<<ETX>>

Evaluation of electrocardiogram beat detection algorithms: patient specific versus generic training

The present study discusses two different training techniques for electrocardiogram (ECG) beat detection algorithms. The first technique is a patient specific training method which uses data from the patients ECG signal to train the beat detector. The second technique is more generic as opposed to patient specific and uses ECG information from a database consisting of a number of patient records to train the detector. Four different beat detection algorithms were considered to facilitate the evaluation of the influence of the training techniques in relation to beat detection performance; a non-syntactic approach, a cross-correlation (CC) approach, a multi-component based CC technique and a multi-component based neural network (NN) technique. An ECG database containing approximately 3000 annotated beats was used for training and test. Superior results were attained with the patient specific training technique. The performance of the two multi-component based classifiers were increased by up to 22% for P-wave and T-wave detection for the patient specific training approach compared to the generic training approach.

The Use of Software Engineering Methods for Efficacious Test Program Creation: A Supportive Evidence Based Case Study

Within the semiconductor manufacturing chain the automated testing steps are coming increasingly into focus. Delivering enhanced functionality per IC is expected, with the costs per die being reduced, while, at the same time, the costs of semiconductor electrical tests increase disproportionately. In addition, the requirements for quality are significantly growing, in general, and in particular, being ensured by automated testing. Hence, the execution of test development and test method quality are becoming an important, competitive-advantage topic. This paper presents a case study that evidences such advantage by adopting software engineering methodologies in test program generation. A software cost model applied to test program development parameters, assessed in combination with Bayesian analysis and Gaussian statistical methods, is discussed in detail. Furthermore, the results obtained indicate the effectiveness of the proposed approach, evidencing a remarkable effort reduction, and address quality robustness in semiconductor test engineering.

Evaluation of multi-component Electrocardiogram beat detection algorithms: Implications of three different noise artifacts

Motion artifacts, caused by changes in the electrode-skin impedance, electromyographic (EMG) interference, caused by muscle contractions, and possible baseline drifts are three of the most common sources of noise present in ECG recordings. The present study investigates the effects of these noise sources on the performance of ECG beat detection algorithms. Four different beat detection methods were used to evaluate the influence of noise sources with varying signal to noise ratios (SNRs). A database consisting of recordings from approximately 100 subjects consisting of approximately 3000 cardiac cycles was used for evaluation. Hence, 1200 records were subsequently tested by the detectors after adding three different noise sources with four different SNRs of 24 dB, 12 dB, 6 dB and -6 dB to the original 100 records. The four classifiers achieved beat detection results from 98% down to 68% for correctly detected QRS-complexes at SNRs between 24 dB and 6 dB.

Combination of multi-component and single-component based electrocardiogram beat detection algorithms

The accuracy of the beat detector is very important for the overall system performance in computerised processing of the Electrocardiogram (ECG). In the present study we introduce the concept of multi-component based beat detection using Cross Correlation (CC) and Neural Networks (NN) techniques. In addition, a new beat detection algorithm is proposed. A database containing 2966 cardiac cycles was used to validate the approaches. Results have shown the ability of achieving a 7% improvement in detecting individual waveform components. Furthermore, the new approach was able to reduce the number of incorrectly detected waveform components by a factor higher than 15.