Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Multicategory classification of 11 neuromuscular diseases based on microarray data using support vector machine.
We applied multicategory machine learning methods to classify 11 neuromuscular disease groups and one control group based on microarray data. To develop multicategory classification models with optimal parameters and features, we performed a systematic evaluation of three machine learning algorithms and four feature selection methods using three-fold cross validation and a grid search. This study included 114 subjects of 11 neuromuscular diseases and 31 subjects of a control group using microarray data with 22,283 probe sets from the National Center for Biotechnology Information (NCBI). ⋯ In addition, a gene symbol, SPP1 was selected as the top-ranked gene by the BW method. We confirmed relationships between the gene (SPP1) and Duchenne muscular dystrophy (DMD) from a previous study. With our models as clinically helpful tools, neuromuscular diseases could be classified quickly using a computer, thereby giving a time-saving, cost-effective, and accurate diagnosis.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Seizure detection using wavelet decomposition of the prediction error signal from a single channel of intra-cranial EEG.
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal leads to two detail signals and one approximate signal. ⋯ The AdaBoost classifier achieves a sensitivity of 98.75% and an average FPR of 0.075 per hour. These results are obtained with leave-one-out cross-validation. In addition, for 13 out of 18 patients, the AdaBoost classifier requires only one feature, while it requires 4 features for the remaining 5 patients.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Signal quality quantification and waveform reconstruction of arterial blood pressure recordings.
Arterial blood pressure (ABP) is an important vital sign of the cardiovascular system. As with other physiological signals, its measurement can be corrupted by different sources of noise, interference, and artifact. Here, we present an algorithm for the quantification of signal quality and for the reconstruction of the ABP waveform in noise-corrupted segments of the measurement. ⋯ In segments of poor signal quality, the ABP wavelets are then reconstructed on the basis of the expected cycle duration and envelope information derived from neighboring ABP wavelet segments. The algorithm was tested on two datasets of ABP waveform signals containing both invasive radial artery ABP and noninvasive ABP waveforms. Our results show that the approach is efficient in identifying the noisy segments (accuracy, sensitivity and specificity over 95%) and reliable in reconstructing beats that were artificially corrupted.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
ETD: an extended time delay algorithm for ventricular fibrillation detection.
Ventricular fibrillation (VF) is the most serious type of heart attack which requires quick detection and first aid to improve patients' survival rates. To be most effective in using wearable devices for VF detection, it is vital that the detection algorithms be accurate, robust, reliable and computationally efficient. ⋯ In this paper, we propose an extended time-delay (ETD) algorithm for VF detection and conduct experiments comparing the performance of ETD against five good VF detection algorithms, including TD, using the popular Creighton University (CU) database. Our study shows that (1) TD and ETD outperform the other four algorithms considered and (2) with the same sensitivity setting, ETD improves upon TD in three other quality measures for up to 7.64% and in terms of aggregate accuracy, the ETD algorithm shows an improvement of 2.6% of the area under curve (AUC) compared to TD.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Multicenter StudyWELCOME – innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with comorbidities.
We propose WELCOME, an innovative integrated care platform using wearable sensors and smart cloud computing for Chronic Obstructive Pulmonary Disease (COPD) patients with co-morbidities. WELCOME aims to bring about a change in the reactive nature of the management of chronic diseases and its comorbidities, in particular through the development of a patient centred and proactive approach to COPD management. The aim of WELCOME is to support healthcare services to give early detection of complications (potentially reducing hospitalisations) and the prevention and mitigation of comorbidities (Heart Failure, Diabetes, Anxiety and Depression). ⋯ Informal carers will also be supported in dealing with their patients. On the other hand, welcome smart cloud platform is the heart of the proposed system where all the medical records and the monitoring data are managed and processed via the decision support system. Healthcare professionals will be able to securely access the WELCOME applications to monitor and manage the patient's conditions and respond to alerts on personalized level.