Multi-parameter patient monitors (MPMs) have become increasingly important in providing quality healthcare to patients. It is well known in the medical community that there exists an intrinsic relationship between different vital parameters in a healthy person, these include heart rate, blood pressure, respiration rate and oxygen saturation. For example, an increase in blood pressure would lead to a decrease in the heart rate, and vice versa. Although it is likely to improve the performance of MPM systems, this fact is not explored in engineering research. In this work, experiments show that deriving additional features to capture the intrinsic relationship between the vital parameters, the alarm accuracy (sensitivity), no-alarm accuracy (specificity) and the overall performance of MPMs can be improved. The geometric mean of the product of all the vital parameters taken in pairs of two was used to capture the intrinsic relationship between the different parameters. An improvement of 10.55% for sensitivity, 0.32% for specificity and an overall performance improvement of 1.03% was obtained, compared to the baseline system using classification and regression tree with the four vital parameters.

Support vector machine (SVM) is a popular machine learning algorithm used extensively in machine fault diagnosis. In this paper, linear, radial basis function (RBF), polynomial, and sigmoid kernels are experimented to diagnose inter-turn faults in a 3kVA synchronous generator. From the preliminary results, it is observed that the performance of the baseline systemis not satisfactory since the statistical features are nonlinear and does not match to the kernels used. In this work, the features are linearized to a higher dimensional space to improve the performance of fault diagnosis system for a synchronous generator using feature mapping techniques, sparse coding and locality constrained linear coding (LLC). Experiments and results show that LLC is superior to sparse coding for improving the performance of fault diagnosis of a synchronous generator. For the balanced data set, LLC improves the overall fault identification accuracy of the baseline RBF system by 22.56%, 18.43% and 17.05% for the R, Y and Bphase faults respectively.

Background: Impaired autonomic function (AF) can result in adverse cardiovascular events during the perioperative period. Literature suggests that patients with intracranial space-occupying lesions experience impaired AF depending on the site of tumour and associated raised intracranial pressure (ICP). The complex interaction between general anaesthetics, AF and intracranial tumours with raised ICP has not been extensively studied. Objective: This study was aimed at evaluating the cardiac AF (in terms of heart rate variability [HRV]) in patients undergoing surgery for supratentorial tumours, at baseline and at different propofol effect site concentrations (Ce) during anaesthetic induction and the results were compared with patients undergoing non-cranial surgeries. Materials and Methods: In this prospective observational study, consecutive adult patients undergoing surgeries for supratentorial tumour (study group) and brachial plexus injury (control group) were recruited. Electrocardiogram was recorded for 5 min at three time points – before propofol induction, at propofol Ce 2 μg/ml and at Ce 4 μg/ml. Results: Forty-five patients were recruited, 24 in study group and 21 in control group. In spite of similar baseline heart rate and blood pressure, low frequency (LF), high frequency (HF) and total power were significantly higher in control group. Baseline LF/HF, though higher in patients with intracranial tumour (craniotomy: 2.2 ± 2.2, control: 1.2 ± 1.1), was not significantly different between the two groups (P = 0.197). HRV variables in both the groups changed the same way in response to the increasing propofol Ce. Conclusion: HRV measurements were significantly different at baseline between the two groups. Following propofol administration, haemodynamic changes and HRV changes were similar in both the groups and also between the two groups.