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 2011
Early detection of spontaneous blood loss using amplitude modulation of Photoplethysmogram.
The present study was designed to investigate can the amplitude modulation (AM) of Photoplethysmogram (PPG) be used as an indicator of blood loss and if so what is the best PPG probe site. PPG from ear, finger and forehead probe sites, standard ECG, and Finapres blood pressure waveforms were continuously recorded from 8 healthy volunteers during baseline, blood withdrawal of 900 ml followed by the blood reinfusion. The instantaneous amplitude modulations present in heart rate (AM(HR)) and breathing rate (AM(BR)) band frequencies of PPG were extracted from high-resolution time-frequency spectrum. ⋯ In addition, significant increases in AM(BR) were found due to blood loss in ear and finger PPG signals. Even without baseline AM(HR) values, 900 ml blood loss detection was shown possible with specificity and sensitivity both 87.5% from ear PPG signals. The present technique has great potential to serve as a valuable tool in the intraoperative and trauma settings to detect hemorrhage.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Sleep apnoea detection in children using PPG envelope-based dynamic features.
Photopletysmography signal has been developed for monitoring of Obstructive Sleep Apnoea, in particular, whenever an apneic episode occurs, that is reflected by decreases in the photopletysmography signal amplitude fluctuation. However, other physiological events such as artifacts and deep inspiratory gasp produce sympathetic activation, being unrelated to apnea. Thus, its high sensitivity can produce misdetections and overestimate apneic episodes. ⋯ A time-evolving version of the standard linear multivariate decomposition is discussed to perform stochastic dimensionality reduction. As a result, performed outcomes of accuracy bring enough evidence that if using a subset of cepstral-based dynamic features, then patient classification accuracy is 83.3%. Therefore, photoplethysmography--based detection provides an adequate scheme for obstructive sleep apnea diagnosis.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Entropy measures for discrimination of 'awake' Vs 'anaesthetized' state in recovery from general anesthesia.
Approximate Entropy (ApEn) and Permutation Entropy (PE) have been recently introduced for assessment of anesthetic depth. Both measures have previously been shown to track changes in the electrical brain activity related to the administration of anesthetic agents. In this paper ApEn and PE are compared for the automatic classification of 'awake' and 'anesthetized' state using a Support Vector Machine to assess their robustness for potential use in a device for monitoring awareness during general anesthesia. It was found that both measures provide linearly separable features and we are able to discriminate between the two states with accuracy greater than 96% using either of the two entropy measures.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Wavelet transform cardiorespiratory coherence detects patient movement during general anesthesia.
Heart rate variability (HRV) may provide anesthesiologists with a noninvasive tool for monitoring nociception during general anesthesia. A novel wavelet transform cardiorespiratory coherence (WTCRC) algorithm has been developed to calculate estimates of the linear coupling between heart rate and respiration. WTCRC values range from 1 (high coherence, no nociception) to 0 (low coherence, strong nociception). ⋯ Values below this threshold were treated as successful detection. The algorithm was found to detect movement with sensitivity ranging from 95% (minimum WTCRC) to 65% (average WTCRC). The WTCRC algorithm thus shows promise for noninvasively monitoring nociception during general anesthesia, using only heart rate and respiration.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2011
Service oriented architecture to support real-time implementation of artifact detection in critical care monitoring.
The quality of automated real-time critical care monitoring is impacted by the degree of signal artifact present in clinical data. This is further complicated when different clinical rules applied for disease detection require source data at different frequencies and different signal quality. ⋯ The framework is instantiated through a Neonatal Intensive Care case study which assesses signal quality of physiological data streams prior to detection of late-onset neonatal sepsis. In this case study requirements and provisions of artifact and clinical event detection are determined for real-time clinical implementation, which forms the second important contribution of this paper.