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 measurement of Blood Urea Nitrogen (BUN) is an important test for examining the kidney condition. BUN is a waste product that is eliminated from the body by the kidneys. Increased BUN level often indicates renal diseases. The invasive blood test is used as the standard BUN testing method. In this paper, we have demonstrated an effective and innovative method for noninvasive BUN prediction from Ten-second fingertip video. We use the Ten-second fingertip video of human subjects to create photo plethysmography (PPG) features through signal processing and feature engineering techniques. We utilize Generative Adversarial Networks (GAN), a type of Generative AI, as a data augmentation method to create synthetic data based on our original data. Then, we use the PPG features and Gold standard BUN level of subj ects to develop four regression models before and after the implementation of GAN. We compare all models' performance in terms of MAE, MSE, and RMSE. Before applying GAN, ANN outperforms other models with an MAE of 1.17, MSE of 2.62, and RMSE of 1.62. We find that GAN implementation significantly improves every model's performance and provides lower MAE, MSE, and RMSE. After applying GAN, ANN performs best among all other models and shows the lowest MAE of 0.69, MSE of 1.03, and RMSE of 1.01. With these outstanding results, our noninvasive method of BUN prediction has the potential to offer enhanced patient comfort, early detection, continuous and remote monitoring of renal health.

The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.