Autism Spectrum Disorder (ASD) starts showing symptoms in the early formative years of an individual, affecting brain development and negatively impacting social and communication skills. Subjective diagnostic methods for ASD detection require lengthy questionnaires, trained medical personnel, and occupational therapists, and are subject to observer variability. Recent years have seen a rise in the usage of machine learning techniques for detecting ASD, which stems from a requirement for objective and accurate detection methods. This research analyzes the performance of various deep convolutional architectures for the detection of ASD. The primary objective of this work is to select a method capable of performing automatic feature extraction and classification with a relatively high degree of accuracy. Several experiments were conducted with different stateof-the-art deep architectures, out of which the ResNet50 performed the best, with an average accuracy of 81%. The performances of these architectures were analyzed in terms of precision, recall, and accuracy.

Diastasis recti abdominis (DRA) is more prevalent in women during pregnancy and postpartum. However, there is a lack of awareness regarding this condition among women. The prevalence of DRA is high in late pregnancy and reduces during postpartum. The purpose of this study is to provide an overview of the treatment strategies for DRA and to discuss the significance of the technology towards better diagnosis and treatment. This work investigated 77 research articles published in the recognized research databases. The study aims to analyze the diagnostic and treatment procedures and the role of technology within them. The management strategy for DRA can either be conservative or surgical. Exercise therapy has been shown to improve functional impairments. These exercises focus on recruiting the abdominal muscles. Electromyography and Ultrasound imaging have been employed as useful tools in assessing the abdominal muscles effectively. This study has examined the treatment methods for DRA to obtain a better understanding of the existing methods. Further investigation and experimentation into therapeutic exercises is strongly recommended to identify the best set of exercises for a faster resolution. Further studies regarding the role of technology to assess therapeutic exercises would be worthwhile.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a deficit of social relationships, interaction, sense of imagination, and constrained interests. Early diagnosis of ASD will aid in devising appropriate training procedures and placing those children in the normal stream. The objective of this research is to analyze the brain response for auditory/visual stimuli in Typically Developing (TD) and children with autism through electroencephalography (EEG). Brain dynamics in the EEG signal can be analyzed well with the help of nonlinear feature primitives. Recent research reveals that, application of fractal-based techniques proves to be effective to estimate of degree of nonlinearity in a signal. This research attempts to analyze the effect of brain dynamics with Higuchi Fractal Dimension (HFD). Also, the performance of the fractal based techniques depends on the selection of proper hyper-parameters involved in it. One of the key parameters involved in computation of HFD is the time interval parameter ‘k’. Most of the researches arbitrarily fixes the value of ‘k’ in the range of all channels. This research proposes an algorithm to estimate the optimal value of the time parameter for each channel. Sub-band analysis was also carried out for the responding channels. Statistical analysis on the experimental reveals that a difference of 30% was observed between autistic and Typically Developing children.

Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due to the subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizing the properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineered features to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quite complex, as the degree of differentiation varies according to each modality. This can be addressed well by Convolutional Neural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from images effectively for image classification. But it loses the context of local information among the pixels that need to be retained for segmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The major contribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segment ischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region by suppressing the details of other regions. Hence the proposed FCN with attention mechanism was able to segment ischemic lesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried out on ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is an improvement of 5% compared to the existing works.

An electroencephalogram (EEG) test can be utilized to capture the electrical impulses in the human brain. EEG signal analysis is crucial in the detection and treatment of brain diseases. Autism is one of the neurological disorders that needs to be diagnosed in the early stages of life. Autistic behavior is difficult to differentiate and it can even lead to adverse effects in the daily routine of a kid. Recent advances in Artificial Intelligence have proven to be an effective way of diagnosing ASD. This research employs PyCaret framework to analyze the anomalies present in the EEG signal data in the context of differentiating Autistic children from Typically developing children. The different anomaly detection modules have been used to detect anomalies, compute their anomaly scores and visualize it. The goal of this study is to determine if PyCaret's anomaly detection module can aid the detection of ASD.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairment in sensory modulation. These sensory modulation deficits would ultimately lead them to difficulties in adaptive behavior and intellectual functioning. The purpose of this study was to observe changes in the nervous system with responses to auditory/visual and only audio stimuli in children with autism and typically developing (TD) through electroencephalography (EEG). In this study, 20 children with ASD and 20 children with TD were considered to investigate the difference in the neural dynamics. The neural dynamics could be understood by non-linear analysis of the EEG signal. In this research to reveal the underlying nonlinear EEG dynamics, recurrence quantification analysis (RQA) is applied. RQA measures were analyzed using various parameter changes in RQA computations. In this research, the cosine distance metric was considered due to its capability of information retrieval and the other distance metrics parameters are compared for identifying the best biomarker. Each computational combination of the RQA measure and the responding channel was analyzed and discussed. To classify ASD and TD, the resulting features from RQA were fed to the designed BiLSTM (bi-long short-term memory) network. The classification accuracy was tested channel-wise for each combination. T3 and T5 channels with neighborhood selection as FAN (fixed amount of nearest neighbors) and distance metric as cosine is considered as the best-suited combination to discriminate between ASD and TD with the classification accuracy of 91.86%, respectively.

To record all electrical activity of the human brain, an electroencephalogram (EEG) test using electrodes attached to the scalp is conducted. Analysis of EEG signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. One of the brain diseases found in early ages include autism. Autistic behaviours are hard to distinguish, varying from mild impairments, to intensive interruption in daily life. The non-linear EEG signals arising from various lobes of the brain have been studied with the help of a robust technique called Detrended Fluctuation Analysis (DFA). Here, we study the EEG signals of Typically Developing (TD) and children with Autism Spectrum Disorder (ASD) using DFA. The Hurst exponents, which are the outputs of DFA, are used to find out the strength of self-similarity in the signals. Our analysis works towards analysing if DFA can be a helpful analysis for the early detection of ASD.

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