Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review

Background

Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG.

Methods

We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool.

Results

We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures.

Conclusion

The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG.

Significance

We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.

Nonaneurysmal perimesencephalic subarachnoid hemorrhage on noncontrast head CT: An accuracy, inter-rater, and intra-rater reliability study

BACKGROUND AND PURPOSE

To evaluate the reliability and accuracy of nonaneurysmal perimesencephalic subarachnoid hemorrhage (NAPSAH) on Noncontrast Head CT (NCCT) between numerous raters.

MATERIALS AND METHODS

45 NCCT of adult patients with SAH who also had a catheter angiography (CA) were independently evaluated by 48 diverse raters; 45 raters performed a second assessment one month later. For each case, raters were asked: 1) whether they judged the bleeding pattern to be perimesencephalic; 2) whether there was blood anterior to brainstem; 3) complete filling of the anterior interhemispheric fissure (AIF); 4) extension to the lateral part of the sylvian fissure (LSF); 5) frank intraventricular hemorrhage; 6) whether in the hypothetical presence of a negative CT angiogram they would still recommend CA. An automatic NAPSAH diagnosis was also generated by combining responses to questions 2-5. Reliability was estimated using Gwet's AC1 (κG), and the relationship between the NCCT diagnosis of NAPSAH and the recommendation to perform CA using Cramer's V test. Multi-rater accuracy of NCCT in predicting negative CA was explored.

RESULTS

Inter-rater reliability for the presence of NAPSAH was moderate (κG = 0.58; 95%CI: 0.47, 0.69), but improved to substantial when automatically generated (κG = 0.70; 95%CI: 0.59, 0.81). The most reliable criteria were the absence of AIF filling (κG = 0.79) and extension to LSF (κG = 0.79). Mean intra-rater reliability was substantial (κG = 0.65). NAPSAH weakly correlated with CA decision (V = 0.50). Mean sensitivity and specificity were 58% (95%CI: 44%, 71%) and 83 % (95%CI: 72 %, 94%), respectively.

CONCLUSION

NAPSAH remains a diagnosis of exclusion. The NCCT diagnosis was moderately reliable and its impact on clinical decisions modest.

From basic sciences and engineering to epileptology: A translational approach

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal-processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)-enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data-sharing initiatives.

Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography

Predicting seizure recurrence risk is critical to the diagnosis and management of epilepsy. Routine electroencephalography (EEG) is a cornerstone of the estimation of seizure recurrence risk. However, EEG interpretation relies on the visual identification of interictal epileptiform discharges (IEDs) by neurologists, with limited sensitivity. Automated processing of EEG could increase its diagnostic yield and accessibility. The main objective was to develop a prediction model based on automated EEG processing to predict one-year seizure recurrence in patients undergoing routine EEG. We retrospectively selected a consecutive cohort of 517 patients undergoing routine EEG at our institution (training set) and a separate, temporally shifted cohort of 261 patients (testing set). We developed an automated processing pipeline to extract linear and non-linear features from the EEGs. We trained machine learning algorithms on multichannel EEG segments to predict one-year seizure recurrence. We evaluated the impact of IEDs and clinical confounders on performances and validated the performances on the testing set. The receiver operating characteristic area-under-the-curve for seizure recurrence after EEG in the testing set was 0.63 (95% CI 0.55-0.71). Predictions were still significantly above chance in EEGs with no IEDs. Our findings suggest that there are changes other than IEDs in the EEG signal embodying seizure propensity.

Diagnostic accuracy of ancillary tests for death by neurologic criteria: a systematic review and meta-analysis

PURPOSE

Ancillary tests are frequently used in death determination by neurologic criteria (DNC), particularly when the clinical neurologic examination is unreliable. Nevertheless, their diagnostic accuracy has not been extensively studied. Our objective was to synthesize the sensitivity and specificity of commonly used ancillary tests for DNC.

SOURCE

We performed a systematic review and meta-analysis by searching MEDLINE, EMBASE, Cochrane databases, and CINAHL Ebsco from their inception to 4 February 2022. We selected cohort and case-control studies including patients with 1) clinically diagnosed death by neurologic criteria or 2) clinically suspected death by neurologic criteria who underwent ancillary testing for DNC. We excluded studies without a priori diagnostic criteria and studies conducted solely on pediatric patients. Accepted reference standards were clinical examination, four-vessel conventional angiography, and radionuclide imaging. Data were directly extracted from published reports. We assessed the methodological quality of studies with the QUADAS-2 tool and estimated ancillary test sensitivities and specificities using hierarchical Bayesian models with diffuse priors.

PRINCIPAL FINDINGS

Overall, 137 records met the selection criteria. One study (0.7%) had a low risk of bias in all QUADAS-2 domains. Among clinically diagnosed death by neurologic criteria patients (n = 8,891), ancillary tests had similar pooled sensitivities (range, 0.82-0.93). Sensitivity heterogeneity was greater within (σ = 0.10-0.15) than between (σ = 0.04) ancillary test types. Among clinically suspected death by neurologic criteria patients (n = 2,732), pooled ancillary test sensitivities ranged between 0.81 and 1.00 and specificities between 0.87 and 1.00. Most estimates had high statistical uncertainty.

CONCLUSION

Studies assessing ancillary test diagnostic accuracy have an unclear or high risk of bias. High-quality studies are required to thoroughly validate ancillary tests for DNC.

STUDY REGISTRATION

PROSPERO (CRD42013005907); registered 7 October 2013.

Computer-assisted analysis of routine electroencephalogram to identify hidden biomarkers of epilepsy: protocol for a systematic review

INTRODUCTION

The diagnosis of epilepsy frequently relies on the visual interpretation of the electroencephalogram (EEG) by a neurologist. The hallmark of epilepsy on EEG is the interictal epileptiform discharge (IED). This marker lacks sensitivity: it is only captured in a small percentage of 30 min routine EEGs in patients with epilepsy. In the past three decades, there has been growing interest in the use of computational methods to analyse the EEG without relying on the detection of IEDs, but none have made it to the clinical practice. We aim to review the diagnostic accuracy of quantitative methods applied to ambulatory EEG analysis to guide the diagnosis and management of epilepsy.

METHODS AND ANALYSIS

The protocol complies with the recommendations for systematic reviews of diagnostic test accuracy by Cochrane. We will search MEDLINE, EMBASE, EBM reviews, IEEE Explore along with grey literature for articles, conference papers and conference abstracts published after 1961. We will include observational studies that present a computational method to analyse the EEG for the diagnosis of epilepsy in adults or children without relying on the identification of IEDs or seizures. The reference standard is the diagnosis of epilepsy by a physician. We will report the estimated pooled sensitivity and specificity, and receiver operating characteristic area under the curve (ROC AUC) for each marker. If possible, we will perform a meta-analysis of the sensitivity and specificity and ROC AUC for each individual marker. We will assess the risk of bias using an adapted QUADAS-2 tool. We will also describe the algorithms used for signal processing, feature extraction and predictive modelling, and comment on the reproducibility of the different studies.

ETHICS AND DISSEMINATION

Ethical approval was not required. Findings will be disseminated through peer-reviewed publication and presented at conferences related to this field.

PROSPERO REGISTRATION NUMBER

CRD42022292261.

Facial palsy after temporal lobectomy for epilepsy: illustrative cases

BACKGROUND

Facial palsy is a rare, unexpected complication of temporal lobectomy (TL) for intractable epilepsy. Even without direct manipulation, the facial nerve fibers may be at risk of injury during supratentorial surgery, including TL.

OBSERVATIONS

The authors presented two cases of facial palsy after unremarkable TL. In the first case, the palsy appeared in a delayed fashion and completely resolved within weeks. In the second case, facial nerve dysfunction was observed immediately after surgery, followed by progressive recovery over 2 years. The second patient had a dehiscence of the roof of the petrous bone overlying the geniculate ganglion, which put the facial nerve at risk of bipolar coagulation thermal injury.

LESSONS

Two major mechanisms could explain the loss of facial nerve function after TL: surgery-related indirect inflammation of the nerve resulting in herpesvirus reactivation and delayed dysfunction (Bell's palsy) or indirect thermal damage to the geniculate ganglion through a dehiscent petrous roof.

Central neurogenic hyperventilation in conscious patients due to CNS neoplasm: a case report and review of the literature on treatment

Background

Central neurogenic hyperventilation (CNH) is increasingly reported in conscious patients with a CNS neoplasm. We aimed to synthesize the available data on the treatment of this condition to guide clinicians in their approach.

Methods

We describe the case of a 39-year-old conscious woman with CNH secondary to glioma brainstem infiltration for whom hyperventilation was aborted with hydromorphone, dexamethasone, and brainstem radiotherapy. We then performed a review of the literature on the treatment of CNH in conscious patients due to a CNS neoplasm.

Results

A total of 31 studies reporting 33 cases fulfilled the selection criteria. The underlying neoplasm was lymphoma in 15 (45%) and glioma in 13 (39%) patients. Overall, CNH was aborted in 70% of cases. Opioids and sedatives overall seemed useful for symptom relief, but the benefit was often of short duration when the medication was administered orally or subcutaneously. Methadone and fentanyl were successful but rarely used. Chemotherapy was most effective in patients with lymphoma (89%), but not glioma (0%) or other neoplasms (0%). Patients with lymphoma (80%) and other tumors (100%) responded to radiotherapy more frequently than patients with glioma (43%). Corticosteroids were moderately effective. Subtotal surgical resection was successful in the 3 cases for which it was attempted.

Conclusion

Definitive treatment of the underlying neoplasm may be more successful in aborting hyperventilation. Variable rates of palliation have been observed with opioids and sedatives. Treatment of CNH is challenging but successful in a majority of cases.

Rise of Raman spectroscopy in neurosurgery: a review

SIGNIFICANCE

Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons.

AIM

We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices.

APPROACH

We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting.

RESULTS

The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing.

CONCLUSIONS

The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

Feature engineering applied to intraoperative in vivo Raman spectroscopy sheds light on molecular processes in brain cancer: a retrospective study of 65 patients

Raman spectroscopy is a promising tool for neurosurgical guidance and cancer research. Quantitative analysis of the Raman signal from living tissues is, however, limited. Their molecular composition is convoluted and influenced by clinical factors, and access to data is limited. To ensure acceptance of this technology by clinicians and cancer scientists, we need to adapt the analytical methods to more closely model the Raman-generating process. Our objective is to use feature engineering to develop a new representation for spectral data specifically tailored for brain diagnosis that improves interpretability of the Raman signal while retaining enough information to accurately predict tissue content. The method consists of band fitting of Raman bands which consistently appear in the brain Raman literature, and the generation of new features representing the pairwise interaction between bands and the interaction between bands and patient age. Our technique was applied to a dataset of 547 in situ Raman spectra from 65 patients undergoing glioma resection. It showed superior predictive capacities to a principal component analysis dimensionality reduction. After analysis through a Bayesian framework, we were able to identify the oncogenic processes that characterize glioma: increased nucleic acid content, overexpression of type IV collagen and shift in the primary metabolic engine. Our results demonstrate how this mathematical transformation of the Raman signal allows the first biological, statistically robust analysis of in vivo Raman spectra from brain tissue.

Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

SIGNIFICANCE

Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met.

AIM

To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2  /  CH3 deformation, and the amide bands.

APPROACH

A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy.

RESULTS

The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively.

CONCLUSIONS

The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.

Development and first in-human use of a Raman spectroscopy guidance system integrated with a brain biopsy needle

Navigation-guided brain biopsies are the standard of care for diagnosis of several brain pathologies. However, imprecise targeting and tissue heterogeneity often hinder obtaining high-quality tissue samples, resulting in poor diagnostic yield. We report the development and first clinical testing of a navigation-guided fiberoptic Raman probe that allows surgeons to interrogate brain tissue in situ at the tip of the biopsy needle prior to tissue removal. The 900 μm diameter probe can detect high spectral quality Raman signals in both the fingerprint and high wavenumber spectral regions with minimal disruption to the neurosurgical workflow. The probe was tested in three brain tumor patients, and the acquired spectra in both normal brain and tumor tissue demonstrated the expected spectral features, indicating the quality of the data. As a proof-of-concept, we also demonstrate the consistency of the acquired Raman signal with different systems and experimental settings. Additional clinical development is planned to further evaluate the performance of the system and develop a statistical model for real-time tissue classification during the biopsy procedure.

Evaluation of the real-world impact of rotational thromboelastometry-guided transfusion protocol in patients undergoing proximal aortic surgery

BACKGROUND

Complex aortic procedures are potentially associated with important blood loss and coagulopathy. The aim of this study was to assess the impact of rotational thromboelastometry (ROTEM, Tem International GmBH, Munich, Germany) on transfusion requirements after proximal aortic operations in a real-world setting.

METHODS

This single-center retrospective analysis based on 385 consecutive patients undergoing cardiac surgeries involving the aortic root, ascending aorta, or aortic arch compared 197 controls managed according to routine transfusion protocol before the introduction of the ROTEM in 2012 with 188 patients operated afterward. With the use of a 1:1 propensity score match, 224 patients were included in paired analysis (112 in each group). The primary end point was erythrocytes transfusion rate. The secondary end points comprised the transfusion of other allogeneic blood products, number of units transfused, postoperative blood loss, massive transfusion rate, and use of other hemostatic products.

RESULTS

ROTEM implementation was associated with a trend toward reduction in the rate of erythrocytes transfusion (57% vs 46%, P = .08) and a decreased median number of units transfused for erythrocytes (1.0 [0.0-4.0] unit vs 0.0 [0.0-2.0] unit, P = .03) and plasma (0.0 [0.0-4.0] unit vs 0.0 [0.0-2.0] unit, P = .04). After sensitivity analysis, ROTEM displayed a comparable rate of erythrocytes transfusion (58% vs 47%, P = .15).

CONCLUSIONS

In a real-world setting, ROTEM-based algorithm implementation could help reduce excess erythrocytes transfusion for complex aortic procedures. We advocate for a strict adherence and concerted team effort to maximize the benefits of such addition to patients' management.