This paper introduces a novel concept of understanding cerebral dynamics by exploring the acoustic signals generated by blood flow in the brain (BFB) and mechanical resonant frequencies produced by the brain. The concept of this paper will be a groundbreaking approach to brain signal analysis through acoustic signals of BFB. Traditional methods for brain wave capture and analysis mostly depend on fMRI and EEG signals, which are now very popular and have some limitations in accessibility, cost, and real-time analysis capabilities. In this study, we seek the existing gaps in the current brain signal-capturing methods, and our theoretical underpinnings hypothesize that sound produced by blood flow in the brain (BFB) can be an innovative approach to capture the brain signal in a more user-friendly and accessible way. The feasibility of capturing and analyzing these BFB-sound is also discussed in this paper. We proposed a theoretical framework to capture the BFB sound through the human ear. The successful completion of this concept architecture will serve in different applications, from diagnosing neurological disorders to monitoring brain health, underscoring its potential to revolutionize non-invasive brain diagnostics. By synthesizing current knowledge and proposing innovative techniques, this paper aims to pave the way for new frontiers in understanding brain function through the brain sound generated by blood flow and captured from the human ears.

The burgeoning fields of machine learning (ML) and quantum machine learning (QML) have shown remarkable potential in tackling complex problems across various domains. However, their susceptibility to adversarial attacks raises concerns when deploying these systems in security-sensitive applications. In this study, we present a comparative analysis of the vulnerability of ML and QML models, specifically conventional neural networks (NN) and quantum neural networks (QNN), to adversarial attacks using a malware dataset. We utilize a software supply chain attack dataset known as ClaMP and develop two distinct models for QNN and NN, employing Pennylane for quantum implementations and TensorFlow and Keras for traditional implementations. Our methodology involves crafting adversarial samples by introducing random noise to a small portion of the dataset and evaluating the impact on the models' performance using accuracy, precision, recall, and F1 score metrics. Based on our observations, both ML and QML models exhibit vulnerability to adversarial attacks. While the QNN's accuracy decreases more significantly compared to the NN after the attack, it demonstrates better performance in terms of precision and recall, indicating higher resilience in detecting true positives under adversarial conditions. We also find that adversarial samples crafted for one model type can impair the performance of the other, highlighting the need for robust defense mechanisms. Our study serves as a foundation for future research focused on enhancing the security and resilience of ML and QML models, particularly QNN, given its recent advancements. A more extensive range of experiments will be conducted to better understand the performance and robustness of both models in the face of adversarial attacks.

The paper presents chaos-theory-based human attention level detection from the electroencephalogram (EEG) signals. In the medical field, “human attention level” can be referred to as the “attentional state,”, which helps to understand an individual's attention capacity in various crucial moments. In this study, we have deployed secondary analysis on existing methods by implementing chaos theory on the PhysioNet dataset. We investigate different vital parameters and values to predict human attention level from the EEG dataset. We calculated time delay, embedding dimension, and correlation dimension from the participants' EEG data to determine the parameters' values: specifically, for detecting of human attention level from EEG signals. By calculating the 95% confidence interval (CI), the time delay has an average of 2.50 seconds, and the embedding dimension and correlation dimension have an average value of 4.41 and 2.23, respectively. We also observed a similar embedded signal in the reconstructed phase space (RPS) of participant's EEG signals. The statistical and chaos-based plot can potentially investigate human attention parameters and develop a robust EEG signal prediction system. Overall, this proposed framework serves as a resource on the latest nonlinearity detection techniques to detect human attention levels utilizing EEG signal analysis. Clinical Relevance - The effectiveness of the chaos-based non-linear analysis method for detecting human attention levels in EEG signals depends on its potential impact on diagnosis and treatment, integration into clinical practice, benefits, and risks.

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart’s cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.

Accurate and valid health information is crucial for effective medical management. Failure to collect adequate information from physical and mental health examinations can be a barrier to Virtual medical platforms and telemedicine. In this paper, we propose the non-invasive “Dream” project prototype to monitor and record heart rate (HR), heart rate-variation (HRV) (for physical health), and stress (for mental health) using only a smart-phone. This non-invasive mobile application, “Dream” uses the front camera to capture video to calculate HR-HRV and stress. The full “Dream” project encompasses our previous facial video HR-HRV and stress work. We have also compared our proposed “Dream” project with 39 works in this area. We found a significant positive difference between our proposed “Dream” project compared to other projects in respect to user accessibility, application, cost-effectiveness, hospitalization monitoring, and human health status. Furthermore, we can apply “Dream” in remote human health monitoring, driver monitoring, and creating vital sign records non-invasively without a health care assistant. Especially during a pandemic, this virtual health monitoring system can be useful for scaled-up telemedicine to serve the remote population.

The elderly population experiences great variability in health, disability, age-related changes, polymorbidity, and associated polypharmacy. Polypharmacy refers to the simultaneous use of multiple drugs to treat a single condition. Polypharmacy often leads to high healthcare costs, unpropitious events, confusion, and errors in the management of the individual’s health. The remote monitoring approach to medicine, especially for elderly populations in rural regions, with limited access to care, has significantly increased. Continuous monitoring of appropriate medication prescription, consistent review of medication lists, and re-evaluation of patient needs are crucial for ensuring that polypharmacy is minimized, and therefore the patient wellbeing is maximized. This study aims to present a comprehensive catalog of information in uniform terminology, define the general definition and features of polypharmacy in elderly people from rural areas, and enable the reader to use that information optimally for their specific application. Our study reveals the current understanding of polypharmacy in elderly individuals from rural regions. We proposed a novel system design for the remote monitoring of elderly patients to minimize polypharmacy. We aim to develop a unique mobile-based application for patient uses and a web-based application for doctors. Our applications will communicate with the medical IoT devices connected with the patients to obtain data. By using our application, providers can monitor their elderly patients’ health data and will be able to make better informed decisions for prescribing medications. Our approach will help elderly people in rural areas and their providers minimize adverse effects due to polypharmacy.