On Geometric Alignment in Low Doubling Dimension

In real-world, many problems can be formulated as the alignment between two geometric patterns. Previously, a great amount of research focus on the alignment of 2D or 3D patterns, especially in the field of computer vision. Recently, the alignment of geometric patterns in high dimension finds several novel applications, and has attracted more and more attentions. However, the research is still rather limited in terms of algorithms. To the best of our knowledge, most existing approaches for high dimensional alignment are just simple extensions of their counterparts for 2D and 3D cases, and often suffer from the issues such as high complexities. In this paper, we propose an effective framework to compress the high dimensional geometric patterns and approximately preserve the alignment quality. As a consequence, existing alignment approach can be applied to the compressed geometric patterns and thus the time complexity is significantly reduced. Our idea is inspired by the observation that high dimensional data often has a low intrinsic dimension. We adopt the widely used notion "doubling dimension" to measure the extents of our compression and the resulting approximation. Finally, we test our method on both random and real datasets, the experimental results reveal that running the alignment algorithm on compressed patterns can achieve similar qualities, comparing with the results on the original patterns, but the running times (including the times cost for compression) are substantially lower

Outcome Prediction of Postanoxic Coma:A Comparison of Automated Electroencephalography Analysis Methods

BACKGROUND: To compare three computer-assisted quantitative electroencephalography (EEG) prediction models for the outcome prediction of comatose patients after cardiac arrest regarding predictive performance and robustness to artifacts. METHODS: A total of 871 continuous EEGs recorded up to 3 days after cardiac arrest in intensive care units of five teaching hospitals in the Netherlands were retrospectively analyzed. Outcome at 6 months was dichotomized as "good" (Cerebral Performance Category 1-2) or "poor" (Cerebral Performance Category 3-5). Three prediction models were implemented: a logistic regression model using two quantitative features, a random forest model with nine features, and a deep learning model based on a convolutional neural network. Data from two centers were used for training and fivefold cross-validation (n = 663), and data from three other centers were used for external validation (n = 208). Model output was the probability of good outcome. Predictive performances were evaluated by using receiver operating characteristic analysis and the calculation of predictive values. Robustness to artifacts was evaluated by using an artifact rejection algorithm, manually added noise, and randomly flattened channels in the EEG. RESULTS: The deep learning network showed the best overall predictive performance. On the external test set, poor outcome could be predicted by the deep learning network at 24 h with a sensitivity of 54% (95% confidence interval [CI] 44-64%) at a false positive rate (FPR) of 0% (95% CI 0-2%), significantly higher than the logistic regression (sensitivity 33%, FPR 0%) and random forest models (sensitivity 13%, FPR, 0%) (p < 0.05). Good outcome at 12 h could be predicted by the deep learning network with a sensitivity of 78% (95% CI 52-100%) at a FPR of 12% (95% CI 0-24%) and by the logistic regression model with a sensitivity of 83% (95% CI 83-83%) at a FPR of 3% (95% CI 3-3%), both significantly higher than the random forest model (sensitivity 1%, FPR 0%) (p < 0.05). The results of the deep learning network were the least affected by the presence of artifacts, added white noise, and flat EEG channels. CONCLUSIONS: A deep learning model outperformed logistic regression and random forest models for reliable, robust, EEG-based outcome prediction of comatose patients after cardiac arrest

Association between somatosensory evoked potentials and EEG in comatose patients after cardiac arrest

Objective: To analyze the association between SSEP results and EEG results in comatose patients after cardiac arrest, including the added value of repeated SSEP measurements. Methods: Continuous EEG was measured in 619 patients during the first 3–5 days after cardiac arrest. SSEPs were recorded daily in the first 55 patients, and on indication in later patients. EEGs were visually classified at 12, 24, 48, and 72 h after cardiac arrest, and at the time of SSEP. Outcome at 6 m was dichotomized as good (Cerebral Performance Category 1–2) or poor (CPC 3–5). SSEP and EEG results were related to outcome. Additionally, SSEP results were related to the EEG patterns at the time of SSEP. Results: Absent SSEP responses and suppressed or synchronous EEG on suppressed background ≥24 h after cardiac arrest were invariably associated with poor outcome. SSEP and EEG identified different patients with poor outcome (joint sensitivity 39% at specificity 100%). N20 responses were always preserved in continuous traces at >8 Hz. Absent SSEPs did not re-emerge during the first five days. Conclusions: SSEP and EEG results may diverge after cardiac arrest. Significance: SSEP and EEG together identify more patients without chance of recovery than one of these alone

EEG functional connectivity contributes to outcome prediction of postanoxic coma

Objective: To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. Methods: Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5). Results: We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity. Conclusion: Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. Significance: Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest

EEG functional connectivity contributes to outcome prediction of postanoxic coma

Objective To investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest. Methods Prospective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5). Results We included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity. Conclusion Functional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma. Significance Functional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.Depto. de Radiología, Rehabilitación y FisioterapiaFac. de MedicinaTRUEpu

Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG

Background: Despite application of the multimodal European Resuscitation Council and European Society of Intensive Care Medicine algorithm, neurological prognosis of patients who remain comatose after cardiac arrest remains uncertain in a large group of patients. In this study, we investigate the additional predictive value of visual and quantitative brain magnetic resonance imaging (MRI) to electroencephalography (EEG) for outcome estimation of comatose patients after cardiac arrest. Methods: We performed a prospective multicenter cohort study in patients after cardiac arrest submitted in a comatose state to the intensive care unit of two Dutch hospitals. Continuous EEG was recorded during the first 3 days and MRI was performed at 3 ± 1 days after cardiac arrest. EEG at 24 h and ischemic damage in 21 predefined brain regions on diffusion weighted imaging and fluid-attenuated inversion recovery on a scale from 0 to 4 were related to outcome. Quantitative MRI analyses included mean apparent diffusion coefficient (ADC) and percentage of brain volume with ADC < 450 × 10−6 mm2/s, < 550 × 10−6 mm2/s, and < 650 × 10−6 mm2/s. Poor outcome was defined as a Cerebral Performance Category score of 3–5 at 6 months. Results: We included 50 patients, of whom 20 (40%) demonstrated poor outcome. Visual EEG assessment correctly identified 3 (15%) with poor outcome and 15 (50%) with good outcome. Visual grading of MRI identified 13 (65%) with poor outcome and 25 (89%) with good outcome. ADC analysis identified 11 (55%) with poor outcome and 3 (11%) with good outcome. EEG and MRI combined could predict poor outcome in 16 (80%) patients at 100% specificity, and good outcome in 24 (80%) at 63% specificity. Ischemic damage was most prominent in the cortical gray matter (75% vs. 7%) and deep gray nuclei (45% vs. 3%) in patients with poor versus good outcome. Conclusions: Magnetic resonance imaging is complementary with EEG for the prediction of poor and good outcome of patients after cardiac arrest who are comatose at admission

Additional predictive value of optic nerve sheath diameter for neurological prognosis after cardiac arrest: a prospective cohort study

Abstract Background The goal is to estimate the additional value of ultrasonographic optic nerve sheath diameter (ONSD) measurement on days 1–3, on top of electroencephalography (EEG), pupillary light reflexes (PLR), and somatosensory evoked potentials (SSEP), for neurological outcome prediction of comatose cardiac arrest patients. We performed a prospective longitudinal cohort study in adult comatose patients after cardiac arrest. ONSD was measured on days 1–3 using ultrasound. Continuous EEG, PLR, and SSEP were acquired as standard care. Poor outcome was defined as cerebral performance categories 3–5 at 3–6 months. Logistic regression models were created for outcome prediction based on the established predictors with and without ONSD. Additional predictive value was assessed by increase in sensitivity for poor (at 100% specificity) and good outcome (at 90% specificity). Results We included 100 patients, 54 with poor outcome. Mean ONSD did not differ significantly between patients with good and poor outcome. Sensitivity for predicting poor outcome increased by adding ONSD to EEG and SSEP from 25% to 41% in all patients and from 27% to 50% after exclusion of patients with non-neurological death. Conclusions ONSD on days 1–3 after cardiac arrest holds potential to add to neurological outcome prediction. Trial registration: clinicaltrials.gov, NCT04084054. Registered 10 September 2019, https://www.clinicaltrials.gov/study/NCT04084054

EEG reactivity testing for prediction of good outcome in patients after cardiac arrest

OBJECTIVE: To determine the additional value of EEG reactivity (EEG-R) testing to EEG background pattern for prediction of good outcome in adult patients after cardiac arrest (CA). METHODS: In this post hoc analysis of a prospective cohort study, EEG-R was tested twice a day, using a strict protocol. Good outcome was defined as a Cerebral Performance Category score of 1-2 within 6 months. The additional value of EEG-R per EEG background pattern was evaluated using the diagnostic odds ratio (DOR). Prognostic value (sensitivity and specificity) of EEG-R was investigated in relation to time after CA, sedative medication, different stimuli, and repeated testing. RESULTS: Between 12 and 24 hours after CA, data of 108 patients were available. Patients with a continuous (n = 64) or discontinuous (n = 19) normal voltage background pattern with reactivity were 3 and 8 times more likely to have a good outcome than without reactivity (continuous: DOR, 3.4; 95% confidence interval [CI], 0.97-12.0; p = 0.06; discontinuous: DOR, 8.0; 95% CI, 1.0-63.97; p = 0.0499). EEG-R was not observed in other background patterns within 24 hours after CA. In 119 patients with a normal voltage EEG background pattern, continuous or discontinuous, any time after CA, prognostic value was highest in sedated patients (sensitivity 81.3%, specificity 59.5%), irrespective of time after CA. EEG-R induced by handclapping and sternal rubbing, especially when combined, had highest prognostic value. Repeated EEG-R testing increased prognostic value. CONCLUSION: EEG-R has additional value for prediction of good outcome in patients with discontinuous normal voltage EEG background pattern and possibly with continuous normal voltage. The best stimuli were clapping and sternal rubbing

EEG reactivity testing for prediction of good outcome in patients after cardiac arrest

OBJECTIVE: To determine the additional value of EEG reactivity (EEG-R) testing to EEG background pattern for prediction of good outcome in adult patients after cardiac arrest (CA). METHODS: In this post hoc analysis of a prospective cohort study, EEG-R was tested twice a day, using a strict protocol. Good outcome was defined as a Cerebral Performance Category score of 1-2 within 6 months. The additional value of EEG-R per EEG background pattern was evaluated using the diagnostic odds ratio (DOR). Prognostic value (sensitivity and specificity) of EEG-R was investigated in relation to time after CA, sedative medication, different stimuli, and repeated testing. RESULTS: Between 12 and 24 hours after CA, data of 108 patients were available. Patients with a continuous (n = 64) or discontinuous (n = 19) normal voltage background pattern with reactivity were 3 and 8 times more likely to have a good outcome than without reactivity (continuous: DOR, 3.4; 95% confidence interval [CI], 0.97-12.0; p = 0.06; discontinuous: DOR, 8.0; 95% CI, 1.0-63.97; p = 0.0499). EEG-R was not observed in other background patterns within 24 hours after CA. In 119 patients with a normal voltage EEG background pattern, continuous or discontinuous, any time after CA, prognostic value was highest in sedated patients (sensitivity 81.3%, specificity 59.5%), irrespective of time after CA. EEG-R induced by handclapping and sternal rubbing, especially when combined, had highest prognostic value. Repeated EEG-R testing increased prognostic value. CONCLUSION: EEG-R has additional value for prediction of good outcome in patients with discontinuous normal voltage EEG background pattern and possibly with continuous normal voltage. The best stimuli were clapping and sternal rubbing