Phonocardigraphy (PCG) is the graphical representation of heart sounds. The PCG signal contains useful information about the functionality and the condition of the heart. It also provides an early indication of potential cardiac abnormalities. Extracting cardiac information from heart sounds and detecting abnormal heart sounds to diagnose heart diseases using the PCG signal can play a vital role in remote patient monitoring. In this paper, we have combined different signal processing techniques and a deep learning method to denoise, compress, segment, and classify PCG signals effectively and accurately. First, the PCG signal is denoised and compressed by using a multi-resolution analysis based on the Discrete Wavelet Transform (DWT). Then, a segmentation algorithm, based on the Shannon energy envelope and zero-crossing, is applied to segment the PCG signal into four major parts: the first heart sound (S1), the systole interval, the second heart sound (S2), and the diastole interval. Finally, Mel-scaled power spectrogram and Mel-frequency cepstral coefficients (MFCC) are employed to extract informative features from the PCG signal, which are then fed into a classifier to classify each PCG signal into a normal or an abnormal signal by using a deep learning approach. For the classification, a 5-layer feed-forward Deep Neural Network (DNN) model is used, and overall testing accuracy of around 97.10% is achieved. Besides providing valuable information regarding heart condition, this signal processing approach can help cardiologists take appropriate and reliable steps toward diagnosis if any cardiovascular disorder is found in the initial stage.

This study introduces a novel framework for stress detection, leveraging the synergy of physiological signals and facial expressions through advanced machine learning techniques. Employing a suite of models including Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Convolutional Neural Networks (CNNs) such as VGG16 and Custom CNN models, we undertake a comprehensive analysis across varied data durations. Our findings highlights the superiority of LSTM networks, which consistently outperform SVMs across metrics, particularly excelling in longer data sequences with notable improvements of 4 % in average in test accuracy, precision, recall, and F1 scores. This highlights the critical advantage of deep learning in capturing complex temporal patterns inherent in stress manifestations. Moreover, our exploration reveals that VGG16 surpasses custom CNNs, achieving a remarkable test accuracy of 87%, thereby setting a new standard in stress detection through facial expression analysis. This research not only advances the state of the art in stress classification but also underscores the transformative potential of multimodal data integration in understanding stress. By demonstrating significant improvements over existing methods [1, 4, 19], this work paves the way for innovative, AI-driven approaches to stress management, emphasizing the critical role of multimodal representations in enhancing the accuracy and reliability of stress detection systems. Also, we have harnessed the three Explainable AI tools (XAI), i.e., SHAP, LIME and Permutation Importance to illuminate the decision-making processes of complex AI models, aiding in the detection and reduction of biases. Through this pioneering effort, we contribute to the broader endeavor of improving mental health and well-being with technology.

This paper explores the wavelength-dependent reflectivity of alternating high and low refractive index multilayers with a thickness profile defined by a pseudo-random, maximum length sequence (MLS). An MLS contains all possible combinations of a binary sequence save one; thus, a multilayer with an MLS profile contains a superposition of a broad range of periods. The range of periodicities in an MLS multilayer should make these systems more effective broad wavelength reflectors as compared to purely periodic counterparts. We compute the reflection characteristics of MLS and periodic dielectric sequences at visible wavelengths over a range of incident angles using the transfer matrix method (TMM), a recursive multilayer calculation method. The materials SiO₂ and TiO₂ are chosen as the low and high refractive index materials, respectively, because these materials are commonly used in optical multilayers and because their wavelength-dependent refractive index is well known. Our results show that it is possible to create an MLS structure with high average reflectivity across the entire visible spectrum (400 nm – 700 nm) at all incident angles and polarizations. Finally, we compare the reflection characteristics of dielectric multilayers with metallic reflectors whose refractive index is based on a Brendel-Bormann (BB) model. The comparison shows that a seventh order MLS aperiodic multilayer exhibits slightly higher average reflectivity over the visible spectum than silver or aluminum metallic reflectors.