Cardiovascular disease (CVD) is currently the biggest single cause of mortality in the developed world, hence, the early detection of its onset is vital for effective prevention therapies. Aortic stiffness as measured by aortic pulse wave velocity (PWV) has been shown to be an independent predictor of CVD, however, the measurement of PWV is complex and time consuming. Recent studies have shown that pulse contour characteristics depend on arterial properties such as arterial stiffness. This paper presents a method for estimating PWV from the digital volume pulse (DVP), a waveform that can be rapidly and simply acquired by measuring the transmission of infra-red light through the finger pulp. PWV and DVP were measured on 461 subjects attending a clinic in South East London. Techniques for extracting features from the DVP contour based on physiology and information theory were compared. Low and high stiffness were defined according to a threshold level of PWV chosen to be 10 m/s. Using a support vector machine-based classifier, it is possible to achieve high overall classification rates on unseen data. Further, the use of support vector regression techniques lead to a direct real-valued estimate of PWV which outperforms previous methods based on multilinear regression. We, therefore, conclude that support vector machine-based classification and regression techniques provide effective prediction of arterial stiffness from the simple measurement of the digital volume pulse. This technique could be usefully employed as a cheap and effective CVD screening technique for use in general practice clinics.
High false-negative rates of the Papanicolauo (so-called 'Pap') smear test and the shortage of colposcopists have led to the desire to find alternative non-expert (automated) approaches for accurately testing cervical smears for signs of cancer. Fourier-Transform Infra-Red (FTIR) spectroscopy has been shown to offer the potential for improving the accuracy (i.e. sensitivity and specificity) of these tests. This paper details the application of the machine learning methodology of Support Vector Machines (SVM) using FTIR data to enhance and improve upon the standard Pap test. A cohort of 53 subjects was used to test the veracity of both the Pap smear results and the FTIR based classifier against the findings of the colposcopists. The Pap test achieved an overall classification of 43 %, whereas our method achieved a rate of 72%
Diaphragmatic electromyogram (EMGdi) signals give important information about the respiratory muscle pump, can be used as an indicator of neural respiratory drive, and have been postulated as a method of designing neurally-activated intelligent ventilators. However diaphragmatic EMG signals measured with an esophageal catheter tend to be contaminated by electrical signals from the heart-electrocardiogram (ECG). This paper presents a novel method of rapidly separating and enhancing the Electromyogram signals from the combined EMG and ECG signals recorded from an esophageal catheter based sensor. Independent Component Analysis (ICA) is used to separate the EMG and ECG signals, then further processing is used to extract the frequency of the patient's breathing and the relative magnitudes of diaphragmatic muscle activity. These signals have two applications, firstly in artificial ventilator systems and as a diagnostic tool for health professionals.
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