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Previous studies have demonstrated increased arterial stiffness (AS) in paediatric heart transplant recipients (HTR) by using established SphygmoCor technology. [ABSTRACT FROM PUBLISHER]

The fetal ECG (fECG) is one of the most valuable tools for monitoring the health of the fetus throughout pregnancy. However, its clinical use has been limited by the difficulty in analysing such non-invasive fECG recordings. [ABSTRACT FROM PUBLISHER]

The aim of this pilot study was to determine the expected normative values of two main features from multi-site photoplethysmography (PPG) waveforms, namely the pulse transit times to foot (PTTf) and to peak (PTTp), and explore their overall trends with ageing. These measures reflect the presence of arterial stiffness and peripheral arterial disease, important factors in cardiovascular risk. PPG waveforms were acquired from the right and left ear lobes, index fingers, and great toes in 31 healthy subjects (19 males and 12 females, mean age 50 years old and range 30–70 years). The electrocardiogram was also simultaneously recorded to provide a cardiac timing reference for the computation of PTTf and PTTp. Values from right and left sides were averaged for subsequent analysis. Linear regressions with age were investigated utilising Pearson's correlation analysis. PTTf for ear, finger and toe sites were 139±10 ms, 192±14 ms, and 282±24 ms, respectively. For PTTp the values were 460±67 ms, 431±40 ms, and 510±29 ms, respectively. Toe PTTf significantly decreased with age (r=−0.59, p<0.001). In contrast, finger PTTp significantly increased with age (r=0.46, p<0.01). In conclusion, multi-site PPG shows potential to assess the increase in cardiovascular risk linked to physiological or early vascular ageing. [ABSTRACT FROM PUBLISHER]

The aim of this study was to develop a new algorithm to predict individual patient mortality with improved accuracy with respect to established methods from data collected over the first 48 hours of admission to the Intensive Care Unit. A binary classifier was developed to participate in Event 1 of the PhysioNet/Computing in Cardiology Challenge 2012. The algorithm development was undertaken using only posterior knowledge from the training dataset (Set-A), containing 41 demographic and clinical variables from 4000 ICU patients. For each variable a feature was defined as the average (across all available measurements of the given variable) likelihood of being part of the “survivors” group. To select features with highest discrimination ability (“survivors” vs. “non-survivors”), a forward sequential selection criterion with logistic cost function was adopted and repeated for cross-validation on N (=10) “leave Mout” (M=50%) random partitions of Set-A. Features that were selected in more than one partition were considered (#Feat = 32). A logistic regression model was used for classification. The score was defined as the lowest between sensitivity and positive predictive value in classification. The proposed method scored 54.9% on Set-A and 44.0% on the test set (Set-B), outperforming the established method SAPS-I (29.6% on Set-A, 31.7% on Set-B). [ABSTRACT FROM PUBLISHER]

For the application of acquiring ECGs from mobile telephones by unskilled users it would be beneficial if the mobile device could assess ECG quality and inform the user if the quality was acceptable. Using the PhysioNet/Computing in Cardiology Challenge 2011 dataset we identified several ECG features that were commonly observed in the training set ‘unacceptable’ category for algorithmic development: flat baseline (FB), saturation (SA), baseline drift (BD), low amplitude (LA), high amplitude (HA) and steep slope (SS). For the training set with each feature detection applied separately the following scores were achieved: FB 76.2%, SA 80.9%, BD 61.3%, LA 75.6%, HA 74.1% and SS 77.5%. With all features combined a score of 91.4% was achieved. For the test set the algorithm classified 181 records as unacceptable and 319 records as acceptable and the score was 85.7%. [ABSTRACT FROM PUBLISHER]