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Time-frequency wavelet theory is used for the detection of life threatening electrocardiography (ECG) arrhythmias. This is achieved through the use of the raised cosine wavelet transform (RCWT). The RCWT is found to be useful in differentiating between ventricular fibrillation, ventricular tachycardia and atrial fibrillation. Ventricular fibrillation is(More)
Noninvasive techniques to record the activation wave from the His-Purkinje system have so far depended largely on signal averaging. Although this approach produces representative signals, any beat-to-beat variations are removed by the averaging process. These beat-to-beat variations are important in the diagnosis of many heart abnormalities, particularly(More)
The detection of ventricular late potentials is a subject of some clinical interest. Most techniques currently being investigated rely on signal averaging to extract the microvolt signals from the considerable amounts of noise which are present. Although this approach produces useful results, it does remove any beat-to-beat variations from the signal, and(More)
OBJECTIVE Our experiments explored the effect of visual stimuli degradation on cognitive workload. APPROACH We investigated the subjective assessment, event-related potentials (ERPs) as well as electroencephalogram (EEG) as measures of cognitive workload. MAIN RESULTS These experiments confirm that degradation of visual stimuli increases cognitive(More)
In this article, a new ECG data compression technique is proposed. The method relies on modelling quasi-periodic ECG signals as a dynamic Fourier series. Fourier coefficients are continuously estimated using either an FFT algorithm or the adaptive least mean square algorithm. Results from simulated normal and pathological ECGs are presented and discussed.(More)
We propose a novel noniterative technique for fetal electrocardiogram extraction. The polynomial networks technique is used to nonlinearly map the MECG signal recorded at the thorax area to the ECG signals recorded at the abdomen. The FECG component is then extracted by subtracting the nonlinearly mapped MECG component from the abdominal ECG signal. Visual(More)
This paper represents an ongoing investigation for surface myoelectric signal segmentation and classification. The classical moving average technique augmented with principal components analysis and time-frency analysis were used for segmentation. Multiresolution wavelet analysis was adopted as an effective feature extraction technique while artificial(More)
In this article, a new ECG data compression technique is proposed. The method relies on modelling quasi-periodic ECG signals as a dynamic Fourier series. Fourier coefficients are continuously estimated using the adaptive least-mean-square algorithm. Results from simulated normal and pathological ECGs are presented and discussed. A comparison with other(More)
Principal component analysis has long been used for a variety of signal processing applications, including signal compression. Neural network implementations of principal component analysis provide a means for unsupervised feature discovery and dimension reduction. In this paper, we describe a method for the compression of ECG data using principal component(More)
The ECG response of a human body exposed to vertical vibrations was investigated. The per cent normalized difference (PND) between the R-wave amplitude before and after vibrations was monitored. Results obtained from a representative subject show an oscillatory decay of the PND behaviour with time. This behaviour can be modelled as a linear second order(More)