Real-Time Virtual Pathology Using Signal Analysis and Synthesis

This dissertation discusses the modeling and simulation (M& S) research in the area of real-time virtual pathology using signal analysis and synthesis. The goal of this research is to contribute to the research in the M&S area of generating simulated outputs of medical diagnostics tools to supplement training of medical students with human patient role players. To become clinically competent physicians, medical students must become skilled in the areas of doctor-patient communication, eliciting the patient\u27s history, and performing the physical exam. The use of Standardized Patients (SPs), individuals trained to realistically portray patients, has become common practice. SPs provide the medical student with a means to learn in a safe, realistic setting, while providing a way to reliably test students\u27 clinical skills. The range of clinical problems an SP can portray, however, is limited. SPs are usually healthy individuals with few or no abnormal physical findings. Some SPs have been trained to simulate physical abnormalities, such as breathing through one lung, voluntarily and increasing blood pressure. But, there are many abnormalities that SPs cannot simulate. The research encompassed developing methods and algorithms to be incorporated into the previous work of McKenzie, el al. [1]–[3] for simulating abnormal heart sounds in a Standardized Patient (SP), which may be utilized in a modified electronic stethoscope. The methods and algorithms are specific to the real-time modeling of human body sounds through modifying the sounds from a real person with various abnormalities. The main focus of the research involved applying methods from tempo and beat analysis of acoustic musical signals for heart signal analysis, specifically in detecting the heart rate and heartbeat locations. In addition, the research included an investigation and selection of an adaptive noise cancellation filtering method to separate heart sounds from lung sounds. A model was developed to use a heart/lung sound signal as input to efficiently and accurately separate heart sound and lung sound signals, characterize the heart sound signal when appropriate, replace the heart or lung sound signal with a reference pathology signal containing an abnormality such as a crackle or murmur, and then recombine the original heart or lung sound signal with the modified pathology signal for presentation to the student. After completion of the development of the model, the model was validated. The validation included both a qualitative assessment and a quantitative assessment. The qualitative assessment drew on the visual and auditory analysis of SMEs, and the quantitative assessment utilized simulated data to verify key portions of the model

Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea

This paper presents a new real-time automated infrared video monitoring technique for detection of breathing anomalies, and its application in the diagnosis of obstructive sleep apnea. We introduce a novel motion model to detect subtle, cyclical breathing signals from video, a new 3-D unsupervised self-adaptive breathing template to learn individuals' normal breathing patterns online, and a robust action classification method to recognize abnormal breathing activities and limb movements. This technique avoids imposing positional constraints on the patient, allowing patients to sleep on their back or side, with or without facing the camera, fully or partially occluded by the bed clothes. Moreover, shallow and abdominal breathing patterns do not adversely affect the performance of the method, and it is insensitive to environmental settings such as infrared lighting levels and camera view angles. The experimental results show that the technique achieves high accuracy (94% for the clinical data) in recognizing apnea episodes and body movements and is robust to various occlusion levels, body poses, body movements (i.e., minor head movement, limb movement, body rotation, and slight torso movement), and breathing behavior (e.g., shallow versus heavy breathing, mouth breathing, chest breathing, and abdominal breathing). © 2013 IEEE

Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

COVID-19 activity screening by a smart-data-driven multi-band voice analysis

COVID-19 is a disease caused by the new coronavirus SARS-COV-2 which can lead to severe respiratory infections. Since its first detection it caused more than six million worldwide deaths. COVID-19 diagnosis non-invasive and low-cost methods with faster and accurate results are still needed for a fast disease control. In this research, 3 different signal analyses have been applied (per broadband, per sub-bands and per broadband & sub-bands) to Cough, Breathing & Speech signals of Coswara dataset to extract non-linear patterns (Energy, Entropies, Correlation Dimension, Detrended Fluctuation Analysis, Lyapunov Exponent & Fractal Dimensions) for feeding a XGBoost classifier to discriminate COVID-19 activity on its different stages. Classification accuracies ranged between 83.33% and 98.46% have been achieved, surpassing the state-of-art methods in some comparisons. It should be empathized the 98.46% of accuracy reached on pair Healthy Controls vs all COVID-19 stages. The results shows that the method may be adequate for COVID-19 diagnosis screening assistance.info:eu-repo/semantics/acceptedVersio

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG