Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task.

The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to medium age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of 89.4% and 88.4% are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.

Characterisation of particulates and trace metals emitted by a dimethyl ether-fuelled genset engine prototype.

Diesel-fuelled CI engines are the primary sources of particulate matter (PM) emissions which harm human health and the urban environment. Elevated PM emission levels can cause respiratory diseases and deteriorate urban air quality and atmospheric visibility. DME, a carbon-neutral and oxygenated fuel, is fast merging as a strong alternative to diesel to reduce PM emissions. The absence of a direct carbon-carbon bond in the molecular structure of DME improves combustion and reduces PM emissions to negligible levels. DME and baseline diesel are experimentally evaluated in a single-cylinder CI genset engine prototype to find the particulate number-size, surface area-size and mass-size distributions. In addition, total particulate number (TPN), total particulate mass (TPM), count mean diameter (CMD) of particulates, particulate morphology and trace metals were assessed. DME genset engine emitted higher numbers of smaller diameter particles, with lower surface area and mass distribution than baseline diesel. For DME, total PM mass emission and CMD of particulates were lower due to particles being finer. Morphological analysis of particulates showed the presence of larger particles from diesel and less bunched agglomerates of nucleation mode particles from the DME genset engine prototype. The trace metal analysis of particulates showed higher presence of trace metals such as Si, Ca, and Na in DME-fuelled engine than in diesel. As an alternative to diesel, DME can reduce PM emissions from genset engines, significantly enhance urban air quality, and minimise the threat of respiratory diseases.

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.

Robust Estimation of Hypernasality in Dysarthria with Acoustic Model Likelihood Features.

Hypernasality is a common characteristic symptom across many motor-speech disorders. For voiced sounds, hypernasality introduces an additional resonance in the lower frequencies and, for unvoiced sounds, there is reduced articulatory precision due to air escaping through the nasal cavity. However, the acoustic manifestation of these symptoms is highly variable, making hypernasality estimation very challenging, both for human specialists and automated systems. Previous work in this area relies on either engineered features based on statistical signal processing or machine learning models trained on clinical ratings. Engineered features often fail to capture the complex acoustic patterns associated with hypernasality, whereas metrics based on machine learning are prone to overfitting to the small disease-specific speech datasets on which they are trained. Here we propose a new set of acoustic features that capture these complementary dimensions. The features are based on two acoustic models trained on a large corpus of healthy speech. The first acoustic model aims to measure nasal resonance from voiced sounds, whereas the second acoustic model aims to measure articulatory imprecision from unvoiced sounds. To demonstrate that the features derived from these acoustic models are specific to hypernasal speech, we evaluate them across different dysarthria corpora. Our results show that the features generalize even when training on hypernasal speech from one disease and evaluating on hypernasal speech from another disease (e.g., training on Parkinson's disease, evaluation on Huntington's disease), and when training on neurologically disordered speech but evaluating on cleft palate speech.

Detection of hypernasality based on vowel space area.

This study proposes a method for differentiating hypernasal-speech from normal speech using the vowel space area (VSA). Hypernasality introduces extra formant and anti-formant pairs in vowel spectrum, which results in shifting of formants. This shifting affects the size of the VSA. The results show that VSA is reduced in hypernasal-speech compared to normal speech. The VSA feature plus Mel-frequency cepstral coefficient feature for support vector machine based hypernasality detection leads to an accuracy of 86.89% for sustained vowels and 89.47%, 90.57%, and 91.70% for vowels in contexts of high pressure consonants /k/, /p/, and /t/, respectively.