[show abstract][hide abstract] ABSTRACT: The most common automated blood pressure devices determine systolic, diastolic and mean arterial blood pressure (SBP, DBP and MAP) by analysing the oscillometric pulse waveform. The aim of this study was to assess the variability of the amplitude of oscillometric pulse waveform characteristics at SBP, DBP and MAP. Sixty oscillometric waveforms from twenty subjects were analysed. Manual SBP and DBP were obtained with a manual sphygmomanometer, from which manual MAP was calculated from the empirical equation. Automated MAP was estimated from the maximum oscillometric pulse. The oscillometric pulse amplitudes, normalised to the maximum pulse, at above SBP, SBP, MAP, DBP and below DBP were then determined. The cuff pressures at half the maximum oscillometric pulse amplitude were also measured. There were significant differences in normalised oscillometric pulse amplitude between SBP and DBP (mean Â± SD: 0.45 Â± 0.10 vs 0.80 Â± 0.12, P < 0.001), and between manual and automated MAP (0.89 Â± 0.09 vs 1.00 Â± 0, P < 0.001). Manual SBP (118 Â± 11 mmHg) and DBP (76 Â± 9 mmHg) were significantly different from the cuff pressures at half the maximum oscillometric pulse amplitude (117 Â± 12 mmHg and 66 Â± 10 mmHg), with the paired differences of 1 Â± 5 mmHg (P < 0.05) and 10 Â± 7 mmHg (P < 0.001) respectively. Significant differences between manual and automated MAP were also observed, with the paired difference of 3 Â± 6 mmHg (P < 0.001). In conclusion, there are large variations in the pulse characteristics at SBP, DBP and MAP. This complicates a reliable automatic estimation of these values.
[show abstract][hide abstract] ABSTRACT: Acute hypotensive episodes (AHE) are serious clinical events in intensive care units. We present an algorithm for automated computer prediction of AHE in patients using mean arterial pressure (MAP). We defined an AHE index based on the observation that patients with documented AHE experienced more transient reductions in MAP compared to those without AHE. The algorithm was developed and tested using the PhysioNet/Computers in Cardiology Challenge 2009 data sets. The algorithm, which classifies records with at least one occurrence of a reduction in MAP to 65 mmHg for at least 75% of a 30 minute window as AHE positive, correctly classified 8 out of 10 records in test set A and 28/40 records in test set B. Using MAP alone the algorithm achieved modest accuracy for prediction of AHE in patients. The algorithm could be improved by taking account of temporal changes in MAP.
[show abstract][hide abstract] ABSTRACT: Using the additional information from multi-lead body surface potential recordings we aimed to study ECG features to predict the extent of infarcted myocardium as part of the 2007 PhysioNet/Computers in Cardiology Challenge. We studied potential and QT maps through key stages of the ventricular cycle assessing the 2 training and 2 test cases. Clinical assessment of the ECGs was provided by three cardiologists. QRS axis was abnormal in training case 1. ST was elevated in training case 1 and test case 2. T wave axis was abnormal in training case 2 and test case 1. T wave axis was different to QRS axis in training case 1. Cardiologists agreed that training cases 1 and 2 were anterior and inferior infarctions respectively, while they considered both test cases to be normal variations. The maps, however, showed significant abnormalities in the test cases.
[show abstract][hide abstract] ABSTRACT: The aim was to develop a fully automatic QT interval measurement algorithm for the 2006 PhysioNet/ Computers in Cardiology Challenge. The algorithm determined the Q onset and T offset points from the average beat in each lead. The QT interval measurement was calculated from the median Q onset and T offset points. Manual measurements were also made. The mean (sd) difference between automatic and reference measurements was -49 (38) ms and between manual and reference measurements was -6 (48) ms.
[show abstract][hide abstract] ABSTRACT: Current RR time series simulations are distinguishable from real data by automatic algorithms. We hypothesised that RR time series simulations could be improved by using time series data from naturally occurring phenomena. 20 records of annual river flow data for the river Tyne in north eastern England were obtained. Each river flow data record was used to generate a single 24 h simulated RR time series with the property of self similarity. We compared the standard frequency parameters ULF, VLF, LF and HF normalised to the total power, for the simulated RR, with those from physiological data from 20 subjects. The river flow data produced realistic simulations of RR time series with significant differences between physiological and simulated series for VLF only. Time series data from river flow or other naturally occurring phenomena may provide useful components in producing RR time series with more realistic characteristics than current artificially generated data
[show abstract][hide abstract] ABSTRACT: We assessed the relationship between QT dispersion and a heart
failure survival score (HFSS). 12-lead ECGs were recorded to computer
during 18 cardiac transplant assessment sessions on 17 patients. The
HFSS was calculated for each subject from clinical data. QT intervals
were measured manually and by an automatic technique. HFSS ranged from
6.27 to 9.80, with five assessments classified as low-risk
(HFSS⩾8.1), seven classified as medium-risk (7.2<HFSS<8.1) and
six classified as high-risk (HFSS⩽7.2). Dispersion measured manually
ranged from 29 to 92 ms with mean (range) for the low-, medium- and
high-risk groups of 47 (29-59), 49 (35-61) and 60 (32-92) ms
respectively. There were no significant differences between risk groups
with manual measurement. Biphasic and ST offsets presented measurement
difficulties for automated measurements