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
Statistical approach for the detection of motion/noise artifacts in Photoplethysmogram.
Motion and noise artifacts (MNA) have been a serious obstacle in realizing the potential of Photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a statistical approach based on the computation of kurtosis and Shannon Entropy (SE) for the accurate detection of MNA in PPG data. The MNA detection algorithm was verified on multi-site PPG data collected from both laboratory and clinical settings. ⋯ For laboratory PPG data recorded from a finger with contrived artifacts, the accuracy was 88.8%. It was identified that the measurements from the forehead PPG sensor contained the most artifacts followed by finger and ear. The proposed MNA algorithm can be implemented in real-time as the computation time was 0.14 seconds using Matlab®.
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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
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.
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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
Biometric and mobile gait analysis for early diagnosis and therapy monitoring in Parkinson's disease.
Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. ⋯ The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists.