Early feasibility and usability study of a novel obstetric blood loss quantifying device.

Objective: To study the accuracy and usability of a novel obstetric blood loss quantifying tool in clinical settings.

Methods: A mixed-methods study was conducted in an Irish tertiary maternity unit. The accuracy of measuring the blood content (hemoglobin concentration) of elective Caesarean section birth waste with a novel obstetric blood loss quantifying device was compared, using Bland-Altman and correlation analysis, with staff volumetry and a reference hemoglobinometer.

Automatic detection of EEG artefacts arising from head movements using EEG and gyroscope signals.

Contamination of EEG signals by artefacts arising from head movements has been a serious obstacle in the deployment of automatic neurological event detection systems in ambulatory EEG. In this paper, we present work on categorizing these head-movement artefacts as one distinct class and on using support vector machines to automatically detect their presence. The use of additional physical signals in detecting head-movement artefacts is also investigated by means of support vector machines classifiers implemented with gyroscope waveforms.

Dynamic, location-based channel selection for power consumption reduction in EEG analysis.

The objective of this study is to develop methods to dynamically select EEG channels to reduce power consumption in seizure detection while maintaining detection accuracy. A method is proposed whereby a number of primary screening channels are predefined. Depending on the classification results of those channels, further channels are selected for analysis.

EEG compression using JPEG2000: how much loss is too much?

Compression of biosignals is an important means of conserving power in wireless body area networks and ambulatory monitoring systems. In contrast to lossless compression techniques, lossy compression algorithms can achieve higher compression ratios and hence, higher power savings, at the expense of some degradation of the reconstructed signal. In this paper, a variant of the lossy JPEG2000 algorithm is applied to Electroencephalogram (EEG) data from the Freiburg epilepsy database.

Dynamic channel selection to reduce computational burden in seizure detection.

Ambulatory physiological monitoring devices benefit patients, medical staff and hospitals by allowing patients to return home with the devices for monitoring. The main problem associated with designing such devices is that of power consumption. Wireless communications and complex processing are generally part of such devices and are power hungry components.

Automatic detection of EEG artefacts arising from head movements.

The need for reliable detection of artefacts in raw and processed EEG is widely acknowledged. In this paper, we present the results of an investigation into appropriate features for artefact detection in the REACT ambulatory EEG system. The study focuses on EEG artefacts arising from head movement.

Ambulatory REACT: real-time seizure detection with a DSP microprocessor.

REACT (Real-Time EEG Analysis for event deteCTion) is a Support Vector Machine based technology which, in recent years, has been successfully applied to the problem of automated seizure detection in both adults and neonates. This paper describes the implementation of REACT on a commercial DSP microprocessor; the Analog Devices Blackfin®. The primary aim of this work is to develop a prototype system for use in ambulatory or in-ward automated EEG analysis.

Age-independent seizure detection.

This paper examines whether an appropriate algorithm, developed for use with neonatal data, could also be used, without alteration, for the detection of seizures in adults with epilepsy. The performance of a feature extraction and SVM classifier system is evaluated on databases of 17 neonatal patients and 15 adult patients. Mean ROC curve areas of 0.

Energy-efficient low duty cycle MAC protocol for wireless body area networks.

This paper presents an energy-efficient medium access control protocol suitable for communication in a wireless body area network for remote monitoring of physiological signals such as EEG and ECG. The protocol takes advantage of the static nature of the body area network to implement the effective time-division multiple access (TDMA) strategy with very little amount of overhead and almost no idle listening (by static, we refer to the fixed topology of the network investigated). The main goal is to develop energy-efficient and reliable communication protocol to support streaming of large amount of data.

Gaussian process modeling of EEG for the detection of neonatal seizures.

Gaussian process (GP) probabilistic models have attractive advantages over parametric and neural network modeling approaches. They have a small number of tuneable parameters, can be trained on relatively small training sets, and provide a measure of prediction certainty. In this paper, these properties are exploited to develop two methods of highlighting the presence of neonatal seizures from electroencephalograph (EEG) signals.

An evaluation of automated neonatal seizure detection methods.

Objective: To evaluate 3 published automated algorithms for detecting seizures in neonatal EEG.

Methods: One-minute, artifact-free EEG segments consisting of either EEG seizure activity or non-seizure EEG activity were extracted from EEG recordings of 13 neonates. Three published neonatal seizure detection algorithms were tested on each EEG recording.