Deep learning for neonatal seizure detection: a friend rather than foe
Authors: Pasquale Striano, Carlo Minetti
Journal: The Lancet Child & Adolescent Health (2020)

The use of computer-assisted diagnosis is nothing new, as it was proposed in the 1990s,1Mazoué JG Diagnosis without doctors.J Med Philos. 1990; 15: 559-579Crossref PubMed Scopus (16) Google Scholar but it was based on systems that required more processing time and were more complex to use. However, in the past decade, this vision has made great strides, not only in medicine, as a result of the development of sophisticated deep-learning methods based on the input of a set of data in a computer that will correlate them more or less independently.2Schwalbe N Wahl B Artificial intelligence and the future of global health.Lancet. 2020; 395: 1579-1586Summary Full Text Full Text PDF PubMed Scopus (138) Google Scholar In particular, deep learning offers the benefits of identifying patterns in complex data and out-of-sample prediction and has demonstrated superior performances in solving many problems in various fields of medicine—eg, radiology, pathology, and drug discovery—compared with traditional machine-learning techniques.2Schwalbe N Wahl B Artificial intelligence and the future of global health.Lancet. 2020; 395: 1579-1586Summary Full Text Full Text PDF PubMed Scopus (138) Google Scholar Machine learning applied to epilepsy is still in its nascent stages, but there have been promising results in automated seizure detection from electroencephalography (EEG), imaging analysis, pre-surgical planning, and prediction of medication response and clinical outcomes using a wide range of data sources.3Abbasi B Goldenholz DM Machine learning applications in epilepsy.Epilepsia. 2019; 60: 2037-2047Crossref PubMed Scopus (146) Google Scholar, 4Siddiqui MK Morales-Menendez R Huang X Hussain N A review of epileptic seizure detection using machine learning classifiers.Brain Inform. 2020; 7: 5Crossref PubMed Scopus (114) Google Scholar, 5Sisodiya SM Whelan CD Hatton SN et al.The ENIGMA-Epilepsy working group: Mapping disease from large data sets.Hum Brain Mapp. 2020; (published online May 29.)DOI:10.1002/hbm.25037Crossref PubMed Scopus (26) Google Scholar However, few neonatal seizure detection algorithms have been developed to assist health-care professionals with objective decision support.6Mathieson SR Stevenson NJ Low E et al.Validation of an automated seizure detection algorithm for term neonates.Clin Neurophysiol. 2016; 127: 156-168Crossref PubMed Scopus (43) Google Scholar, 7Sharpe C Davis SL Reiner GE et al.Assessing the feasibility of providing a real-time response to seizures detected with continuous long-term neonatal electroencephalography monitoring.J Clin Neurophysiol. 2019; 36: 9-13Crossref PubMed Scopus (21) Google Scholar Newborn babies commonly show a range of movements; some unusual repetitive movements might be mistaken as seizures and the diagnosis of neonatal seizures is often challenging for clinicians. In The Lancet Child & Adolescent Health, Andreea Pavel and colleagues8Pavel AM Rennie JM de Vries LS et al.A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.Lancet Child Adolesc Health. 2020; (published online Aug 27.)https://doi.org/10.1016/S2352-4642(20)30239-XSummary Full Text Full Text PDF PubMed Scopus (47) Google Scholar present results of the first multicentre trial addressing the performance of an automated seizure detection algorithm (Algorithm for Neonatal Seizure Recognition [ANSeR] software system) supporting the real-time diagnosis of seizures in newborn infants. After repeated training and offline testing using several datasets of neonatal EEGs, the authors did a randomised, two-arm, parallel controlled study across eight European countries, using a very large dataset (13 827 h of continuous EEG) in term neonates with seizures of various causes. Eligible neonates were randomly assigned to receive continuous EEG monitoring with the aid of the algorithm or routine EEG monitoring alone. Overall, 258 neonates (128 in the algorithm group and 130 in the non-algorithm group) were included in the study analysis. The primary outcome of measure—ie, recognition of a neonate with seizures—did not differ between the two groups, as electrographic seizures were identified in 70 infants (32 [25·0%] neonates in the algorithm group vs 38 [29·2%] in the non-algorithm group). This finding might be explained by the high level of experience in EEG monitoring and interpretation at the recruiting hospitals. However, among the overall 659 h in which seizures occurred (seizure hours), the proportion of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8–77·3] of 268 h vs 177 [45·3%; 34·5–58·3] of 391 h), and this increase was even greater at weekends than on week days. This result suggests that a machine-learning algorithm might be helpful as a real-time support tool for the recognition of neonatal seizures, which is vital because they are often a sign of an underlying neurological condition.9Murray D Boylan G Ali I Ryan C Murphy B Connoly S Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures.Arch Dis Child Fetal Neonatal Ed. 2008; 93: 187-191Crossref PubMed Scopus (320) Google Scholar However, the study by Pavel and colleagues8Pavel AM Rennie JM de Vries LS et al.A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial.Lancet Child Adolesc Health. 2020; (published online Aug 27.)https://doi.org/10.1016/S2352-4642(20)30239-XSummary Full Text Full Text PDF PubMed Scopus (47) Google Scholar has some limitations. First, the investigated cohort was heterogeneous in terms of seizure burden and causes. Second, preterm neonates in whom seizure detection is much more challenging were not included, and EEG is a sensitive method for assessing their prognosis and risk of neurological sequelae. Third, the impact of ANSeR in determining treatment and clinical outcomes is uncertain. Nevertheless, the topic of this paper is relevant to a huge audience of neonatologists and neonatal and paediatric neurologists, and neurophysiologists. This technology can be particularly important when it is necessary to make a quick decision, formulate a rapid diagnosis, and start immediate treatment—for example, in neurocritical care of newborns in whom the early identification of the seizures is challenging due to the difficulty in recognising seizures clinically and the fact that neonates with severe encephalopathies often have subclinical seizures.10Nunes ML Yozawitz EG Zuberi S et al.Neonatal seizures: Is there a relationship between ictal electroclinical features and etiology? A critical appraisal based on a systematic literature review.Epilepsia Open. 2019; 4: 10-29Crossref PubMed Scopus (33) Google Scholar Machine-learning algorithms can filter millions of data to detect not only details that the human eye is unable to grasp, but also allows development of new methods to interpret data. Moreover, applying an algorithm can be an economic solution to reduce disparities between industrialised and developing countries, accelerating the achievement of the health-related sustainable development goals. Some fear that machines can replace doctors, but it is more reasonable to imagine that artificial and human intelligence will work synergistically and that algorithms will end up helping doctors instead of replacing them. Although several ethical, regulatory, and practical concerns remain to be addressed in the future, these models have potential to improve health outcomes if they can be developed sufficiently in the future and if we can better understand how they work. PS has received speaker fees and participated at advisory boards for Biomarin, Zogenyx, and GW Pharmaceuticals, and has received research funding by ENECTA, GW Pharmaceuticals, Kolfarma, and Eisai. CM declares no competing interests. We developed this work within the framework of the DINOGMI Department of Excellence of Ministero dell' Istruzione, Università e Ricerca, 2018-2022 (legge 232 del 2016). A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trialANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required. Full-Text PDF Open Access