In 2017, the International League Against Epilepsy (ILAE) Classification of Epilepsies described the “genetic generalized epilepsies” (GGEs), which contained the “idiopathic generalized epilepsies” (IGEs). The goal of this paper is to delineate the four syndromes comprising the IGEs, namely childhood absence epilepsy, juvenile absence epilepsy, juvenile myoclonic epilepsy, and epilepsy with generalized tonic–clonic seizures alone. We provide updated diagnostic criteria for these IGE syndromes determined by the expert consensus opinion of the ILAE’s Task Force on Nosology and Definitions (2017–2021) and international external experts outside our Task Force. We incorporate current knowledge from recent advances in genetic, imaging, and electroencephalographic studies, together with current terminology and classification of seizures and epilepsies. Patients that do not fulfill criteria for one of these syndromes, but that have one, or a combination, of the following generalized seizure types: absence, myoclonic, tonic‐clonic and myoclonic‐tonic‐clonic seizures, with 2.5–5.5 Hz generalized spike‐wave should be classified as having GGE. Recognizing these four IGE syndromes as a special grouping among the GGEs is helpful, as they carry prognostic and therapeutic implications.

Epilepsy syndromes have been recognized for >50 years, as distinct electroclinical phenotypes with therapeutic and prognostic implications. Nonetheless, no formally accepted International League Against Epilepsy (ILAE) classification of epilepsy syndromes has existed. The ILAE Task Force on Nosology and Definitions was established to reach consensus regarding which entities fulfilled criteria for an epilepsy syndrome and to provide definitions for each syndrome. We defined an epilepsy syndrome as “a characteristic cluster of clinical and electroencephalographic features, often supported by specific etiological findings (structural, genetic, metabolic, immune, and infectious).” The diagnosis of a syndrome in an individual with epilepsy frequently carries prognostic and treatment implications. Syndromes often have age‐dependent presentations and a range of specific comorbidities. This paper describes the guiding principles and process for syndrome identification in both children and adults, and the template of clinical data included for each syndrome. We divided syndromes into typical age at onset, and further characterized them based on seizure and epilepsy types and association with developmental and/or epileptic encephalopathy or progressive neurological deterioration. Definitions for each specific syndrome are contained within the corresponding position papers.

The goal of this paper is to provide updated diagnostic criteria for the epilepsy syndromes that have a variable age of onset, based on expert consensus of the International League Against Epilepsy Nosology and Definitions Taskforce (2017–2021). We use language consistent with current accepted epilepsy and seizure classifications and incorporate knowledge from advances in genetics, electroencephalography, and imaging. Our aim in delineating the epilepsy syndromes that present at a variable age is to aid diagnosis and to guide investigations for etiology and treatments for these patients.

Depressive disorder (DD) is a mental illness affecting more than 300 million people worldwide, whereas social stigma and subtle, variant symptoms impede diagnosis. Psychomotor retardation is a common component of DD with a negative impact on motor function, usually reflected on patients’ routine activities, including, nowadays, their interaction with mobile devices. Therefore, such interactions constitute an enticing source of information towards unsupervised screening for DD symptoms in daily life. In this vein, this paper proposes a machine learning-based method for discriminating between subjects with depressive tendency and healthy controls, as denoted by self-reported Patient Health Questionnaire-9 (PHQ-9) compound scores, based on typing patterns captured in-the-wild. The latter consisted of keystroke timing sequences and typing metadata, passively collected during natural typing on touchscreen smartphones by 11/14 subjects with/without depressive tendency. Statistical features were extracted and tested in univariate and multivariate classification pipelines to reach a decision on subjects’ status. The best-performing pipeline achieved an AUC = 0.89 (0.72–1.00; 95% Confidence Interval) and 0.82/0.86 sensitivity/specificity, with the outputted probabilities significantly correlating (>0.60) with the respective PHQ-9 scores. This work adds to the findings of previous research associating typing patterns with psycho-motor impairment and contributes to the development of an unobtrusive, high-frequency monitoring of depressive tendency in everyday living.