EXTRACTION OF VITAL SIGNS USING REAL TIME VIDEO ANALYSIS FOR NEONATAL MONITORING

Video data is now commonly used for analysis in surveillance, security, medical and many other fields. The development of low cost but high-quality portable cameras has contributed significantly to this trend. One such trend includes non-invasive vital statistics monitoring of infants in Neonatal Intensive Care Units (NICU). National Center for Health Statistics Publications has reported a high infant death rate (23,215 in 2014). This statistic has drawn the interest of health system professionals. Due to occurrence of conditions like bradycardia, apnea and hypoxia, these preterm infants are kept in an NICU for constant monitoring. One of the problems faced at the NICU is the use of traditional sensors for vital statistics monitoring which might cause damage to the already fragile skin of these infants. A contact-less approach to record such vital signs can now be employed using a live video feed. Recent literature shows that it is possible to extract features from videos that are invisible to the human eye, employing various image and signal processing techniques. One of the algorithms demonstrated recently is the extraction of medical vital signs based on wavelet filtering of monochrome video data. The pumping of blood to various parts of body from the heart in rhythmic fashion causes subtle changes in the skin tone of humans. These changes are periodic in nature as the pumping action itself is periodic corresponding to the heart beat. Typical heartbeat of a human ranges from 50-200 beats per minutes (bpm) implying the range to be 0.83-3.33 Hz. Similarly, a video is a sequence of frames with frame rate ranging from 15-30 frames per seconds (fps). This creates the possibility of using videos to detect heart rates as the Nyquist Criterion is met with ease. The subtle changes in skin tones can be further processed and magnified. The mean gray level signal obtained from such a process has been found to be resembling the pulse rate waveform obtained from photoplethysmograph(PPG) sensor to measure pulse rate. The other approach is to use color channel domain like HSI (Hue, Saturation and Intensity). With the above concept, a video processing algorithm was designed in MATLAB. Short videos of several subjects with different skin tone were recorded for the analysis. In order to compare to the ground truth, pulse data were recorded at the same time using the photoplethysmographsensor of a wearable watch. Upon implementing the algorithm designed, on the videos, it was possible to extract waveforms from the videos that resembled the pulse waveform recorded from the ground truth measuring device. The percentage error was in the range of 0.2 to 1.4%. This led to the conclusion that video data can be analyzed to extract heart rate and with further study can be used for real time monitoring of cardiac activity of infants at an NICU

Smart Sensors for Healthcare and Medical Applications

This book focuses on new sensing technologies, measurement techniques, and their applications in medicine and healthcare. Specifically, the book briefly describes the potential of smart sensors in the aforementioned applications, collecting 24 articles selected and published in the Special Issue “Smart Sensors for Healthcare and Medical Applications”. We proposed this topic, being aware of the pivotal role that smart sensors can play in the improvement of healthcare services in both acute and chronic conditions as well as in prevention for a healthy life and active aging. The articles selected in this book cover a variety of topics related to the design, validation, and application of smart sensors to healthcare

Extraction of Heart Rate from Multimodal Video Streams of Neonates using Methods of Machine Learning

The World Health Organization estimates that more than one-tenth of births are premature. Premature births are linked to an increase of the mortality risk, when compared with full-term infants. In fact, preterm birth complications are the leading cause of perinatal mortality. These complications range from respiratory distress to cardiovascular disorders. Vital signs changes are often prior to these major complications, therefore it is crucial to perform continuous monitoring of this signals. Heart rate monitoring is particularly important. Nowadays, the standard method to monitor this vital sign requires adhesive electrodes or sensors that are attached to the infant. This contact-based methods can damage the skin of the infant, possibly leading to infections. Within this context, there is a need to evolve to remote heart rate monitoring methods. This thesis introduces a new method for region of interest selection to improve remote heart rate monitoring in neonatology through Photoplethysmography Imaging. The heart rate assessment is based on the standard photoplethysmography principle, which makes use of the subtle fluctuations of visible or infrared light that is reflected from the skin surface within the cardiac cycle. A camera is used, instead of the contact-based sensors. Specifically, this thesis presents an alternative method to manual region of interest selection using methods of Machine Learning, aiming to improve the robustness of Photoplethysmography Imaging. This method comprises a highly efficient Fully Convolutional Neural Network to select six different body regions, within each video frame. The developed neural network was built upon a ResNet network and a custom upsampling network. Additionally, a new post-processing method was developed to refine the body segmentation results, using a sequence of morphological operations and centre of mass analysis. The developed region of interest selection method was validated with clinical data, demonstrating a good agreement (78%) between the estimated heart rate and the reference

Non Invasive Tools for Early Detection of Autism Spectrum Disorders

Autism Spectrum Disorders (ASDs) describe a set of neurodevelopmental disorders. ASD represents a significant public health problem. Currently, ASDs are not diagnosed before the 2nd year of life but an early identification of ASDs would be crucial as interventions are much more effective than specific therapies starting in later childhood. To this aim, cheap an contact-less automatic approaches recently aroused great clinical interest. Among them, the cry and the movements of the newborn, both involving the central nervous system, are proposed as possible indicators of neurological disorders. This PhD work is a first step towards solving this challenging problem. An integrated system is presented enabling the recording of audio (crying) and video (movements) data of the newborn, their automatic analysis with innovative techniques for the extraction of clinically relevant parameters and their classification with data mining techniques. New robust algorithms were developed for the selection of the voiced parts of the cry signal, the estimation of acoustic parameters based on the wavelet transform and the analysis of the infant’s general movements (GMs) through a new body model for segmentation and 2D reconstruction. In addition to a thorough literature review this thesis presents the state of the art on these topics that shows that no studies exist concerning normative ranges for newborn infant cry in the first 6 months of life nor the correlation between cry and movements. Through the new automatic methods a population of control infants (“low-risk”, LR) was compared to a group of “high-risk” (HR) infants, i.e. siblings of children already diagnosed with ASD. A subset of LR infants clinically diagnosed as newborns with Typical Development (TD) and one affected by ASD were compared. The results show that the selected acoustic parameters allow good differentiation between the two groups. This result provides new perspectives both diagnostic and therapeutic

Applications and Experiences of Quality Control

The rich palette of topics set out in this book provides a sufficiently broad overview of the developments in the field of quality control. By providing detailed information on various aspects of quality control, this book can serve as a basis for starting interdisciplinary cooperation, which has increasingly become an integral part of scientific and applied research

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Development of Portable Diffuse Optical Spectroscopic Systems For Treatment Monitoring

The goal of this dissertation is to demonstrate the utility of portable, small-scale diffuse optical spectroscopic (DOS) systems for the diagnosis and treatment monitoring of various diseases. These systems employ near-infrared light (wavelength range of 650nm to 950nm) to probe human tissue and are sensitive to changes in scattering and absorption properties of tissues. The absorption is mainly influenced by the components of blood, namely oxy- and deoxy-hemoglobin (HbO2 and Hb) and parameters that can be derived from them (e.g. total hemoglobin concentration [THb] and oxygen saturation, StO2). Therefore, I focused on diseases in which these parameters change, which includes vascular diseases such as Peripheral Atrial Disease (PAD) and Infantile Hemangiomas (IH) as well as musculoskeletal autoimmune diseases such as Rheumatoid Arthritis (RA). In each of these specific diseases, current monitoring techniques are limited by their sensitivity to disease progression or simply do not exist as a quantitative metric. As part of this project, I first designed and built a wireless handheld DOS device (WHDD) that can perform DOS measurements at various tissue depths. This device was used in a 15-patient pilot study for infantile hemangiomas (IH) to differentiate diseased skin from normal skin and monitor the vascular changes during intervention. In another study, I compare the ultra-small form- factor WHDD’s ability to monitor synovitis and disease progression during a patient’s treatment of RA against the capabilities of a proven frequency domain optical tomographic (FDOT) system that has shown to differentiate patients with and without RA. Learning from clinical utility of the WHDD from these two studies, I adapted the WHDD technology to develop a compact multi- channel DOS measurement system to monitor perfusion changes in the lower extremities before and after surgical intervention for patients with peripheral artery disease (PAD). Using this multi- channel system, which we called the vascular optical spectroscopic measurement (VOSM) system, our group conducted a 20-subject pilot study to quantify its ability to monitor blood perfusion before and after revascularization of stenotic arteries in the lower extremities. This proof-of- concept study demonstrated how DOS may help vascular surgeons perform revascularization procedures in the operating room and assists in post-operative treatment monitoring of vascular diseases