Speech assessment is an important part of the rehabilitation process for patients with aphasia (PWA). Mandarin speech lucidity features such as articulation, fluency, and tone influence the meaning of the spoken utterance and overall speech clarity. Automatic assessment of these features is important for an efficient assessment of the aphasic speech. Hence, in this paper, a standardized automatic speech lucidity assessment method for Mandarin-speaking aphasic patients using a machine learning based technique is presented. The proposed assessment method adopts the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE) standard as a guideline. Quadrature based high-resolution time-frequency images with a convolutional neural network (CNN) are utilized to develop a method that can map the relationship between the severity level of aphasic patients’ speech and the three speech lucidity features. The results show a linear relationship with statistically significant correlations between the normalized true-class output activations (TCOA) of the CNN model and patients’ articulation, fluency, and tone scores, i.e., 0.71 ( p < 0.001), 0.60 ( p < 0.001) and 0.58 ( p < 0.001), respectively. The linearity of the proposed Mandarin aphasic speech assessment method and its significant correlation with the speech severity levels show the efficacy of the method in predicting the severity of impaired Mandarin speech. The outcome of this research envisages assisting speech-language pathologists in Mandarin-speech impairment assessment and promoting early support discharge; hence could alleviate the stress that the healthcare system is currently experiencing in China nationwide. The framework of the proposed Mandarin aphasic speech assessment method can be readily extended to other languages.

Fibre optic sensors protect resources and critical infrastructure in commercial and defence applications. Distributed fibre optic sensors can be designed using various sensing technologies, such as Mach–Zehnder interferometers (MZIs), Michelson Interferometers, and phase-sensitive optical time domain reflectometers (Φ-OTDR). The ability to eliminate nuisance alarms without compromising the probability of detection (POD) is critical for accepting perimeter intrusion detection systems (PIDS). In this paper, we discuss the importance of sensor installation, the validity of intrusion tests, the effects of the signal-to-noise ratio (SNR) and frequency contents on time lag estimation, quantification of the POD and nuisance alarm rate (NAR), and the need to validate intrusion recognition algorithms in realistic environments. Moreover, this paper demonstrates the precision of intrusion localisation at various locations along the perimeter, both during torrential rain and under calm weather conditions. In a longitudinal study, this paper also demonstrates the effectiveness of level crossings (LCs)-based intrusion detection algorithms, integrated into Mach–Zehnder (MZ)-distributed sensors, at a practical site. During the longitudinal investigation, we found that nuisance alarms could be suppressed for rainfall rates exceeding 225 mm/day while detecting intrusions and nuisances simultaneously. The intrusion location spread in quiet and rainy conditions was within ±10 m with a 95% confidence level of 0.81. Additionally, the convolutional neural network (CNN) architectures, AlexNet, ResNet-50, VGG-16, and GoogLeNet, were investigated in terms of performance and suitability to MZ-based PIDS. The CNN models can discriminate between intrusion and torrential rain events with a 98.04% accuracy rate. The latency analysis revealed that the LCs-based algorithm outperformed the CNN models in terms of processing time. This research is intended to guide the development of PIDS and their algorithms.

Speech assessment is an essential part of the rehabilitation procedure for patients with aphasia (PWA). It is a comprehensive and time-consuming process that aims to discriminate between healthy individuals and aphasic patients, determine the type of aphasia syndrome, and determine the patients’ impairment severity levels (these are referred to here as aphasia assessment tasks). Hence, the automation of aphasia assessment tasks is essential. In this study, the performance of three automatic speech assessment models based on the speech dataset-type was investigated. Three types of datasets were used: healthy subjects’ dataset, aphasic patients’ dataset, and a combination of healthy and aphasic datasets. Two machine learning (ML)-based frameworks, classical machine learning (CML) and deep neural network (DNN), were considered in the design of the proposed speech assessment models. In this paper, the DNN-based framework was based on a convolutional neural network (CNN). Direct or indirect transformation of these models to achieve the aphasia assessment tasks was investigated. Comparative performance results for each of the speech assessment models showed that quadrature-based high-resolution time-frequency images with a CNN framework outperformed all the CML frameworks over the three dataset-types. The CNN-based framework reported an accuracy of 99.23 ± 0.003% with the healthy individuals’ dataset and 67.78 ± 0.047% with the aphasic patients’ dataset. Moreover, direct or transformed relationships between the proposed speech assessment models and the aphasia assessment tasks are attainable, given a suitable dataset-type, a reasonably sized dataset, and appropriate decision logic in the ML framework.

The rehabilitation of aphasics is fundamentally based on the assessment of speech impairment. Developing methods for assessing speech impairment automatically is important due to the growing number of stroke cases each year. Traditionally, aphasia is assessed manually using one of the well-known assessment batteries, such as the Western Aphasia Battery (WAB), the Chinese Rehabilitation Research Center Aphasia Examination (CRRCAE), and the Boston Diagnostic Aphasia Examination (BDAE). In aphasia testing, a speech-language pathologist (SLP) administers multiple subtests to assess people with aphasia (PWA). The traditional assessment is a resource-intensive process that requires the presence of an SLP. Thus, automating the assessment of aphasia is essential. This paper evaluated and compared custom machine learning (ML) speech recognition algorithms against off-the-shelf platforms using healthy and aphasic speech datasets on the naming and repetition subtests of the aphasia battery. Convolutional neural networks (CNN) and linear discriminant analysis (LDA) are the customized ML algorithms, while Microsoft Azure and Google speech recognition are off-the-shelf platforms. The results of this study demonstrated that CNN-based speech recognition algorithms outperform LDA and off-the-shelf platforms. The ResNet-50 architecture of CNN yielded an accuracy of 99.64 ± 0.26% on the healthy dataset. Even though Microsoft Azure was not trained on the same healthy dataset, it still generated comparable results to the LDA and superior results to Google’s speech recognition platform.

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

Goal: The purpose of this work is to improve malaria diagnosis efficiency by integrating smartphones with microscopes. This integration involves image acquisition and algorithmic detection of malaria parasites in various thick blood smear (TBS) datasets sourced from different global regions, including low-quality images from Sub-Saharan Africa. Methods: This approach combines image segmentation and a convolutional neural network (CNN) to distinguish between white blood cells, artifacts, and malaria parasites. A portable system integrates a microscope with a graphical user interface to facilitate rapid malaria detection from smartphone images. We trained the CNN model using open-source data from the Chittagong Medical College Hospital, Bangladesh. Results: The validation process, using microscopic TBS from both the training dataset and an additional dataset from Sub-Saharan Africa, demonstrated that the proposed model achieved an accuracy of 97.74% ± 0.05% and an F1-score of 97.75% ± 0.04%. Remarkably, our proposed model with AlexNet surpasses the reported literature performance of 96.32%. Conclusions: This algorithm shows promise in aiding malaria-stricken regions, especially those with limited resources.