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 2015
Bayesian fusion of algorithms for the robust estimation of respiratory rate from the photoplethysmogram.
Respiratory rate (RR) is a key vital sign that is monitored to assess the health of patients. With the increase of the availability of wearable devices, it is important that RR is extracted in a robust and noninvasive manner from the photoplethysmogram (PPG) acquired from pulse oximeters and similar devices. However, existing methods of noninvasive RR estimation suffer from a lack of robustness, resulting in the fact that they are not used in clinical practice. ⋯ Our proposed methodology resulted in a mean-absolute-error (MAE) of 1.98 breaths per minute (bpm), outperformed other fusing strategies (mean fusion: 2.95 bpm; median fusion: 2.33 bpm; ML: 2.30 bpm). It also outperformed the best single algorithm (2.39 bpm) and the benchmark algorithm proposed for use with Capnobase (2.22 bpm). We conclude that the proposed fusion methodology can be used to combine RR estimates from multiple sources derived from the PPG, to infer a reliable and robust estimation of the respiratory rate in an unsupervised manner.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Seizure detection using regression tree based feature selection and polynomial SVM classification.
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients with low hardware complexity and low power consumption. In the proposed approach, we first compute the spectrogram of the input fragmented EEG signals from three or four electrodes. Each fragmented data clip is one second in duration. ⋯ The algorithm is tested using the intra-cranial EEG (iEEG) from the UPenn and Mayo Clinic's Seizure Detection Challenge database. It is shown that the proposed algorithm can achieve a sensitivity of 100.0%, an average area under curve (AUC) of 0.9818, a mean detection horizon of 5.8 seconds, and a specificity of 99.9% on using half of the training data for classification. The proposed approach also achieved a mean AUC of seizure detection and early seizure detection of 0.9136 on the testing data.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Quantitative EEG markers in severe post-resuscitation brain injury with therapeutic hypothermia.
Therapeutic hypothermia has been regarded as one of the most effective post-cardiac arrest (CA) treatments to improve survival and functional recovery. However, many clinical prognostic markers after resuscitation have become less reliable under hypothermia. In this study, we applied and compared two developed quantitative measures - information quantity (IQ) and sub-band IQ (SIQ) - to evaluate the accuracy of EEG markers on predicting cortical recovery under therapeutic hypothermia. ⋯ Contrary to IQ recovery but similarly to NDS scores, the SIQ recovery was not significantly different between the hypothermia (0.66±0.04) and normothermia (0.64±0.04) groups (p>0.05). IQ could identify the presence of high-frequency oscillations during the recovery from severe brain injury. We demonstrated that while SIQ was able to provide additional sub-band EEG information related to the recovery of different brain functions, both early IQ and SIQ markers are able to accurately predict neurologic outcome after CA.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2015
Sleep apnea detection using time-delayed heart rate variability.
Sleep apnea is a sleep disorder distinguished by repetitive absence of breathing. Compared with the traditional expensive and cumbersome methods, sleep apnea diagnosis or screening with physiological information that can be easily acquired is needed. This paper describes algorithms using heart rate variability (HRV) to automatically detect sleep apneas as long as it can be easily acquired with unobtrusive sensors. ⋯ Experiments were conducted with a data set of 23 sleep apnea patients using support vector machine (SVM) classifiers and cross validations. Results show that using eleven HRV features with a time delay of 1.5 minutes rather than the features without time delay for SA detection, the overall accuracy increased from 74.9% to 76.2% and the Cohen's Kappa coefficient increased from 0.49 to 0.52. Further, an accuracy of 94.5% and a Kappa of 0.89 were achieved when applying subject-specific classifiers.
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Conf Proc IEEE Eng Med Biol Soc · Jan 2014
Randomized Controlled TrialAltered cortical causality after remifentanil administration in healthy volunteers: a novel approach for pharmaco-EEG.
Alterations in cortical causality information flow induced by remifentanil infusion in healthy volunteers was investigated in a placebo-controlled double-blind cross-over study. For each of the 21 enrolled male subjects, 2.5 minutes of resting electroencephalography (EEG) data were collected before and after infusion of remifentanil and placebo. Additionally, to assess cognitive function and analgesic effect, continuous reaction time (CRT) and bone pressure and heat pain were assessed, respectively. ⋯ Furthermore, several of the PSI features altered by remifentanil were correlated to changes in both CRT and pain scores. The results indicate that remifentanil administration influence the information flow between several brain areas. Hence, the EEG causality approach offers the potential to assist in deciphering the cortical effects of remifentanil administration.