BackgroundNoisy breathing is a common presenting symptom in children. The purpose of this study is to (a) assess parental ability to label wheeze, (b) compare the ability of parents of children with and without asthma to label wheeze and (c) determine factors affecting parental ability to label wheeze correctly.MethodsThis cross-sectional study in a tertiary hospital in Kuala Lumpur, Malaysia involved parents of children with asthma. Parents of children without asthma were the control group. Eleven validated video clips showing wheeze, stridor, transmitted noises, snoring or normal breathing were shown to the parents. Parents were asked, in English or Malay, “What do you call the sound this child is making?” and “Where do you think the sound is coming from?”ResultsTwo hundred parents participated in this study: 100 had children with asthma while 100 did not. Most (71.5 %) answered in Malay. Only 38.5 % of parents correctly labelled wheeze. Parents were significantly better at locating than labelling wheeze (OR 2.4, 95 % CI 1.64–3.73). Parents with asthmatic children were not better at labelling wheeze than those without asthma (OR1.04, 95 % CI 0.59–1.84). Answering in English (OR 3.4, 95 % CI 1.69–7.14) and having older children with asthma (OR 9.09, 95 % CI 3.13–26.32) were associated with correct labelling of wheeze. Other sounds were mislabelled as wheeze by 16.5 % of respondents.ConclusionParental labelling of wheeze was inaccurate especially in the Malay language. Parents were better at identifying the origin of wheeze rather than labelling it. Physicians should be wary about parental reporting of wheeze as it may be inaccurate.Electronic supplementary materialThe online version of this article (doi:10.1186/s12887-016-0616-8) contains supplementary material, which is available to authorized users.

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Epilepsy is one of the most common neurological disorders; it affects millions of people globally. Because of the risks to health that it causes, the study and analysis of epilepsy have been given considerable attention in the biomedical field. In a neurological diagnosis, an automated device for detecting seizures or epilepsy from an electroencephalogram (EEG) signal has a significant role. This research work proposes a very large scale integration implementation system for the automatic detection of seizures. Before classification, feature extraction was performed by discrete wavelet transform (DWT) and on-chip classification was performed by a linear support vector machine. The polyphase architecture of Daubechies fourth-order wavelet three-level DWT was used to minimize computational time. The systolic array architecture-based support vector machine classifier using parallel processing helps to minimize the computational complexity of the proposed method. This research work uses an open access EEG dataset. Hardware implementation was done on a field-programmable gate array (FPGA). Efficient results were produced compared with the existing system on chip (SoC) and FPGA seizure detection systems.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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