ObjectiveTo systematically review, identify and report the screening tools used for early identification of developmental delay in low- and middle-income countries.DesignSystematic review.Data sourcesFour bibliographic databases: Medline (1946 to 13 July 2020), Embase (1974 to 13 July 2020), Scopus (1823 to 11 July 2020) and PsycINFO (1987 to July week 1 2020).Eligibility criteriaPeer-reviewed original articles published in English addressing validated culturally sensitive developmental screening tools among children aged <5 years were included in this review.Data extraction and synthesisOne author (CK, medical librarian) developed the search strategy. Three authors conducted the database search (phase I: CK; phase II: IJ and MKI). Three authors (TF, IJ and MKI) independently screened the title and abstracts. TF, MKI and GK independently performed the full-text review of the screened articles. During each step of the study selection process, disagreements were resolved through discussion. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was used to guide the systematic review. Data extraction and analysis were performed using MS Excel. Meta-analysis was not possible due to heterogeneity of the study findings.ResultsWe identified 3349 articles, of which 18 studies from 10 countries, reporting 16 screening tools, were selected for qualitative synthesis. Six cultural contexts were explored. Twelve general, two motor and two speech-language tools were identified. Seven of them found to be parent-completed ones. Five screening tools (American Speech-Language and Hearing Association, Guide for Monitoring Child Development, Infant Neurological International Battery, New Delhi-Development Screening Questionnaire and Woodside Screening Technique) reported relatively higher sensitivity (82.5%–100%) and specificity (83%–98.93%).ConclusionsLimited number of culturally sensitive developmental screening tools were validated for children aged <5 years in low- and middle-income countries. Revising existing screening tools in different ethnic and cultural settings and subsequent validation with normative value should be a research priority.
This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.
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