Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.

Spectrum Disorder (ASD) is a neurodevelopmental condition which affects a persons cognition and behaviour. It is a lifelong condition which cannot be cured completely using any intervention to date. However, early diagnosis and follow-up treatments have a major impact on autistic people. Unfortunately, the current diagnostic practices, which are subjective and behaviour dependent, delay the diagnosis at an early age and makes it harder to distinguish autism from other developmental disorders. Several works of literature explore the possible behaviour-independent measures to diagnose ASD. Abnormalities in EEG can be used as reliable biomarkers to diagnose ASD. This work presents a low-cost and straightforward diagnostic approach to classify ASD based on EEG signal processing and learning models. Possibilities to use a minimum number of EEG channels have been explored. Statistical features are extracted from noise filtered EEG data before and after Discrete Wavelet Transform. Relevant features and EEG channels were selected using correlation-based feature selection. Several learning models and feature vectors have been studied and possibilities to use the minimum number of EEG channels have also been explored. Using Random Forest and Correlation-based Feature Selection, an accuracy level of 93% was obtained.

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Several B+-tree variants have been developed to exploit the performance potential of byte-addressable non-volatile memory (NVM). In this paper, we attentively investigate the properties of B+-tree and find that, a conventional B+-tree node is a linear structure in which key-value (KV) pairs are maintained from the zero offset of the node. These pairs are shifted in a unidirectional fashion for insertions and deletions. Inserting and deleting one KV pair may inflict a large amount of write amplifications due to shifting KV pairs. This badly impairs the performance of in-NVM B+-tree. In this paper, we propose a novel circular design for B+-tree. With regard to NVM's byte-addressability, our Circ-tree design embraces tree nodes in a circular structure without a fixed base address, and bidirectionally shifts KV pairs in a node for insertions and deletions to minimize write amplifications. We have implemented a prototype for Circ-Tree and conducted extensive experiments. Experimental results show that Circ-Tree significantly outperforms two state-of-the-art in-NVM B+-tree variants, i.e., NV-tree and FAST+FAIR, by up to 1.6× and 8.6×, respectively, in terms of write performance. The end-to-end comparison by running YCSB to KV store systems built on NV-tree, FAST+FAIR, and Circ-Tree reveals that Circ-Tree yields up to 29.3% and 47.4% higher write performance, respectively, than NV-tree and FAST+FAIR.

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