Holographic laser Doppler imaging of pulsatile blood flow

We report on wide-field imaging of pulsatile motion induced by blood flow using heterodyne holographic interferometry on the thumb of a healthy volunteer, in real-time. Optical Doppler images were measured with green laser light by a frequency-shifted Mach-Zehnder interferometer in off-axis configuration. The recorded optical signal was linked to local instantaneous out-of-plane motion of the skin at velocities of a few hundreds of microns per second, and compared to blood pulse monitored by plethysmoraphy during an occlusion-reperfusion experiment.Comment: 5 pages, 5 figure

A Novel Real-Time Non-invasive Hemoglobin Level Detection Using Video Images from Smartphone Camera

Hemoglobin level detection is necessary for evaluating health condition in the human. In the laboratory setting, it is detected by shining light through a small volume of blood and using a colorimetric electronic particle counting algorithm. This invasive process requires time, blood specimens, laboratory equipment, and facilities. There are also many studies on non-invasive hemoglobin level detection. Existing solutions are expensive and require buying additional devices. In this paper, we present a smartphone-based non-invasive hemoglobin detection method. It uses the video images collected from the fingertip of a person. We hypothesized that there is a significant relation between the fingertip mini-video images and the hemoglobin level by laboratory gold standard. We also discussed other non-invasive methods and compared with our model. Finally, we described our findings and discussed future works

EXAMINING THE RELABILITY AND VALIDITY OF THE FITBIT CHARGE 2™ TECHNOLOGY ON HEART RATE DURING TREADMILL EXERCISE

The purpose of this study was to determine the reliability and validity of the Fitbit® Charge 2™ compared to a 4-lead ECG to monitor heart rate during exercise. All participants completed a VO2Max test to determine the participants’ cardiorespiratory fitness and appropriate work loads for the intensity protocol. Participants wore a Fitbit® Charge 2™ on the wrist and a 4-lead ECG. 16 participants were assigned to testing conditions: GPS stride length/manual stride length and hold/no-hold on the handrails of the treadmill. The participants completed a walking protocol including light (60% of HRR) intensities while wearing both devices. Intraclass Correlation Coefficient (ICC) was used to analyze the results. The ECG is a reliable and valid means to monitor heart rate. The Fitbit® Charge 2™ demonstrated poor reliability and validity to monitor heart rate

Transformation of Medical Diagnostics with Machine Learning by Considering the Example of Atrial Fibrillation Identification

The paper addresses the problem of detecting one of the most common cardiac arrhythmias atrial fibrillation with artificial intelligence. The arrhythmia increases the risk of suffering from a stroke massively. Because of this, it is essential to detect atrial fibrillation early. As the arrhythmia occurs in short sequences, it is only possible to detect the disease in long-term measurements for example with electrocardiography. All common current detection techniques are calculating the R-R intervals with variations of the root mean square of successive differences. Because this approach is inflexible and expensive, a major hospital in Germany suggests the implementation of an artificial intelligence solution for atrial fibrillation detection. The aim of the paper is to study the feasibility of atrial fibrillation detection with artificial intelligence in the clinical setting of the hospital

Tracheal Sound Acquisition Using Laser Doppler Vibrometer

Breathing is vital for human life. Respiratory diseases cause more than one million deaths annually. The sound of breathing activity can be used to assess the state of the lungs and detect adverse problems involving respiratory failure. Traditionally, mechanical stethoscopes are used for respiratory auscultation to analyse the lung sounds. Firstly, measurements are carried out on five different standard stethoscopes to assess their reliability for detecting respiratory sounds in people. Secondly, three human subjects were used for tracheal sound experiments using five stethoscopes. Thirdly, a laser doppler vibrometer was used to detect tracheal sounds on three human subjects. Five stethoscopes used for measurements give different results especially above 150 Hz. Experimental results show that mechanical stethoscopes are not reliable tools for assessing lungs sounds in people

Employment of artificial intelligence mechanisms for e-Health systems in order to obtain vital signs improving the processes of online consultations and diagnosis

A large number of web-based e-Health applications have been developed through time, allowing doctors to access different types of functionalities, like knowing which medication the patient has consumed or performing online consultations. Internet systems for healthcare can be improved by using artificial intelligence mechanisms for the process of detecting diseases and obtaining biological data, allowing medical professionals to have important information that facilitates the diagnosis process and the choice of the correct treatment for each particular person. The proposed research work aims to present an innovative approach when compared to traditional platforms, by providing online vital signs in real time and allowing the visualization of all historical data of a patient. It aims to defend the concept of promoting online consultations, providing complementary functionalities to the traditional methods for performing medical diagnoses through the use of software engineering practices. This investigation led to the conclusion that, in the future, many medical processes will most likely be done online, where this practice is considered extremely helpful for the analysis and treatment of contagious diseases, or cases that require constant monitoring.info:eu-repo/semantics/acceptedVersio

Thermal image processing for real-time noncontact respiration rate monitoring

A real-time thermal imaging based, non-contact respiration rate monitoring method was developed. It measured the respiration related skin surface temperature changes under the tip of the nose. Facial tracking was required as head movements caused the face to appear in different locations in the recorded images over time. The algorithm detected the tip of the nose and then, a region just under it was selected. The pixel values in this region in successive images were processed to determine respiration rate. The segmentation method, used as part of the facial tracking, was evaluated on 55,000 thermal images recorded from 14 subjects with different extent of head movements. It separated the face from image background in all images. However, in 11.7% of the images, a section of the neck was also included, but this did not cause an error in determining respiration rate. The method was further evaluated on 15 adults, against two contact respiration rate monitoring methods that tracked thoracic and abdominal movements. The three methods gave close respiration rates in 12 subjects but in 3 subjects, where there were very large head movements, the respiration rates did not match

In the spotlight: Bioinstrumentation

Significant advances in the field of biomedical instrumentation have continued to contribute towards improvements in the quality of clinical diagnosis and treatment, which could have important implications for health and welfare. Up to the present, progress in several topics has been reviewed within the In the Spotlight column, including new and noteworthy measurement methods [1], ballistocardiography revisited [2], wearable monitoring [3], and home healthcare technologies [4]. The present review deals with three notable topics from recent publications in the field of bioinstrumentation. These are: (1) new conducting polymer electrodes for in vivo bio-electrical measurements, (2) mHealth technology using mobile communication devices such as mobile phones and personal digital assistants (PDAs), and (3) non-invasive in vivo measurement of blood constituents. These topics are of recent or renewed interest and, where appropriate, progress since the previous reviews [1], [2] will be highlighted. © 2008-2011 IEEE