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
BioWatch - a wrist watch based signal acquisition system for physiological signals including blood pressure.
A wrist watch based system, which can measure electrocardiogram (ECG) and photoplethysmogram (PPG), is presented in this work. By using both ECG and PPG we also measure pulse transit time (PTT), which studies show to correlate well with blood pressure (BP). ⋯ We also validate measurements on different postures and show the value of calibrating the device for each posture. This system, called BioWatch, can potentially facilitate continuous and ubiquitous monitoring of ECG, PPG, heart rate, blood oxygenation and BP.
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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
Physiological trajectory of patients pre and post ICU discharge.
The intensive care unit (ICU) admits the most severely ill patients, and the goal of the unit can be interpreted as stabilizing patient physiology. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff. ⋯ A model of physiological normality was developed using data from the day of hospital discharge, and patients were retrospectively evaluated throughout their stay using this model. We show that the physiology of patients being cared for in the ICU improves very rapidly in the three days prior to discharge, and furthermore, that this recovery continues during their stay on the ward, albeit at a slower rate.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Mobile DIORAMA-II: infrastructure less information collection system for mass casualty incidents.
In this paper we introduce DIORAMA-II system that provides real time information collection in mass casualty incidents. Using a mobile platform that includes active RFID tags and readers as well as Smartphones, the system can determine the location of victims and responders. The system provides user friendly multi dimensional user interfaces as well as collaboration tools between the responders and the incident commander. ⋯ All responders that participated in all trials were very satisfied. They felt in control of the incident and mentioned that the system significantly reduced their stress level during the incident. They all mentioned that they would use the system in an actual incident.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Computing network-based features from physiological time series: application to sepsis detection.
Sepsis is a systemic deleterious host response to infection. It is a major healthcare problem that affects millions of patients every year in the intensive care units (ICUs) worldwide. Despite the fact that ICU patients are heavily instrumented with physiological sensors, early sepsis detection remains challenging, perhaps because clinicians identify sepsis by using static scores derived from bed-side measurements individually, i.e., without systematically accounting for potential interactions between these signals and their dynamics. ⋯ Then, for each connectivity matrix, we computed the eigenvalue decomposition. We found that, even though raw PTS measurements may have indistinguishable distributions in non-sepsis and early sepsis states, the median /I of the eigenvalues computed from the same data is statistically different (p <; 0.001) in the two states and the evolution of /I may reflect the disease progression. Although preliminary, these findings suggest that network-based features computed from continuous PTS data may be useful for early sepsis detection.