Design Of Pulse Oximeters

A number of resources, every year, being spent to tackle early detection of cardiac abnormalities which is one of the leading causes of deaths all over the Globe. The challenges for healthcare systems includes early detection, portability and mobility of patients. This paper presents a categorical review of smartphone-based systems that can detect cardiac abnormalities by the analysis of Electrocardiogram (ECG) and Photoplethysmography (PPG) and the limitation and challenges of these system. The ECG based systems can monitor, record and forward signals for analysis and an alarm can be triggered in case of abnormality, however the limitation of smart phone’s processing capabilities, lack of storage and speed of network are major challenges. The systems based on PPG signals are non-invasive and provides mobility and portability. This study aims to critically review the existing systems, their limitation, challenges and possible improvements to serve as a reference for researchers and developers.

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Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis.

Final report of the 'Robust Heart Beat Detection in Multimodal Data' as part of the Masters in Operations Research. Project was done together with other group members, as can be found in the report itself.

Robust Detection Of Heart Beats In Multimodal Data

Recently, wearable technologies have evolved in the most unexpected field like a military force outside of healthcare, fitness, lifestyle and similar areas. The growing need for soldiers' coordination, training and health, the increase in asymmetric warfare, suspected geopolitical conflicts and soldiers' modernization programs, among others, are some of the factors that fuel the growth of the military wearables market. Wearable computation plays an important role in improving the capabilities of the soldier. Further, the 5G network promises a solution to the many network and performance challenges in order to adopt more sophisticated wearable technologies in defense automation. In this paper, we conduct a study to identify the role of wearable computing for the defence automation system. We present the taxonomy of wearable computing in defence automation system and explain the relationship of each attribute. In addition, we identify the raise issues and challenges in communication and cybersecurity when deploying the 5G network in defense automation. Furthermore, we propose the design of the wearable smartwatch architecture as a use case of healthcare transformation in defense automation in the 5G environment.

Wearable Computing for Defence Automation: Opportunities and Challenges in 5G Network

Autonomic nervous system (ANS) can maintain homeostasis through the coordination of different organs including heart. The change of blood glucose (BG) level can stimulate the ANS, which will lead to the variation of Electrocardiogram (ECG). Considering that the monitoring of different BG ranges is significant for diabetes care, in this paper, an ECG-based technique was proposed to achieve non-invasive monitoring with three BG ranges: low glucose level, moderate glucose level, and high glucose level. For this purpose, multiple experiments that included fasting tests and oral glucose tolerance tests were conducted, and the ECG signals from 21 adults were recorded continuously. Furthermore, an approach of fusing density-based spatial clustering of applications with noise and convolution neural networks (DBSCAN-CNN) was presented for ECG preprocessing of outliers and classification of BG ranges based ECG. Also, ECG's important information, which was related to different BG ranges, was graphically visualized. The result showed that the percentages of accurate classification were 87.94% in low glucose level, 69.36% in moderate glucose level, and 86.39% in high glucose level. Moreover, the visualization results revealed that the highlights of ECG for the different BG ranges were different. In addition, the sensitivity of prediabetes/diabetes screening based on ECG was up to 98.48%, and the specificity was 76.75%. Therefore, we conclude that the proposed approach for BG range monitoring and prediabetes/diabetes screening has potentials in practical applications.

Non-invasive Monitoring of Three Glucose Ranges Based On ECG By Using DBSCAN-CNN

Capacitive electrocardiogram (cECG), a tried and tested technique for more than 50 years, can replace the gold standard electrocardiogram (ECG) only in certain applications, where unobtrusiveness is aimed at the expense of reduced signal quality. This inferiority is due, among other reasons, to the fragility of the capacitive coupling between the body and the electrode, observed as motion artifacts in the presence of time-variant coupling capacitance. Attempts were made to remove these artifacts in earlier studies using adaptive noise cancellation by assuming statistical independence between motion and ECG signals. This article considers the mechanical vibrations of the human body itself as the source of time-variant coupling capacitances, which can cause motion artifacts. Ballistocardiography (BCG) measurements on cECG electrodes were recorded and analyzed to investigate how these mechanical vibrations influence the coupling impedance of the clothing between the body and the electrode. A novel sensor has been proposed and tested in a test bench for four different nominal pressure values (15, 20, 25, and 30 mmHg) with minute-increasing steps for the measurement of the coupling capacitance. Subsequently, simultaneous measurements of BCG and the coupling capacitance were conducted on three healthy subjects for two different electrode positions. A significant correlation between the coupling capacitance and measured ballistocardiograms was found both in time-domain shapes and time–frequency distributions in all measurements.

Physiological Motion Artifacts in Capacitive ECG: Ballistocardiographic Impedance Distortions

Photoplethysmography (PPG) is a transcutaneous optical signal acquisition method to monitor blood volume variations to assess cardiovascular health. Using a light source and a photodetector, PPG signals can be acquired and used in a vast application area. For the estimation of vital parameters like respiratory and heart rate, different properties of this pulsatile wave need to be further analyzed. This demands the utilization of different sensor techniques to be used like motion sensors, skin temperature monitoring and different wavelengths of light sources. This paper represents the proposed multipurpose PPG sensor design, called SmartPPG, which is able to measure multi-wavelength PPG from different skin penetration depths as well as skin temperature while providing motion tracking via an accelerometer. The manufactured prototype was tested for different PPG techniques to evaluate its applicability for arterial, venous and respiratory diagnosis.

Multifunctional Photoplethysmography Sensor Design for Respiratory and Cardiovascular Diagnosis

The capacitive electrocardiograph (cECG) has been tested for several measurement scenarios, including hospital beds, car seats and chairs since it was first proposed. The inferior signal quality of the cECG compared to the gold standard ECG guides the ongoing research in the direction of out-of-hospital applications, where unobtrusiveness is sought and high-level diagnostic signal quality is not essential. This study aims to expand the application range of cECG not in terms of the measurement scenario but in the profile of the subjects by including subjects with implanted cardiac pacemakers. Within this study, 20 patients with cardiac pacemakers were recruited during their clinical device follow-up and cECG measurements were conducted using a seat equipped with integrated cECG electrodes. The multichannel cECG recordings of active unipolar and bipolar pacemaker stimulation were analyzed offline and evaluated in terms of Fβ scores using a pacemaker spike detection algorithm. Fβ scores from 3652 pacing events, varying from 0.62 to 0.78, are presented with influencing parameters in the algorithm and the comparison of cECG channels. By tuning the parameters of the algorithm, different ranges of Fβ scores were found as 0.32 to 0.49 and 0.78 to 0.88 for bipolar and unipolar stimulations, respectively. For the first time, this study shows the feasibility of a cECG system allowing health monitoring in daily use on subjects wearing cardiac pacemakers.

Car Seats with Capacitive ECG Electrodes Can Detect Cardiac Pacemaker Spikes

Unobtrusive measurement technologies for vital signs, such as capacitively coupled electrocardiography (cECG), allow for health monitoring outside the clinical domain, for example in automotive applications. While cECG has the potential to deliver accurate information about the driver's heart, the signal quality is very volatile compared to standard ECG and can be corrupted easily by motion artifacts. In this work, we present a signal-level fusion algorithm based on a convolutional neural network (CNN) to locate individual heartbeats in three-channel cECG signals. To design and optimize the network's structure, data from the PhysioNet / CinC challenge “Robust Detection of Heart Beats in Multimodal Data” was used as an independent source. To train and test the algorithm, we used cECG data from six subjects and three different driving scenarios (highway, city, and proving ground) that is freely available as part of our UnoViS-database. Data consisted of 31 recordings with a total duration of 13.4 hours. Leave-one-subject-out cross validation was performed to assess the algorithm's performance. We achieved a sensitivity of 88.0% and a positive predictive value of 95.2% compared to the reference ECG, with a root-mean-square R-Peak localization error of 20.81 ms. The developed algorithm is available for download via the UnoViS-Website.

http://www.cinc.org/archives/2018/pdf/CinC2018-143.pdf

Signal-Level Fusion with Convolutional Neural Networks for Capacitively Coupled ECG in the Car

Capacitive ECG (cECG), a technique older than 50, is able to replace the gold standard ECG only in certain applications where unobtrusiveness and conformity are aimed at the expense of reduced signal quality. Triboelectric surface charges, motion artifacts, and resulting time-variant coupling capacitances are among the reasons for the signal deformations in cECG. In this paper, the mechanical vibrations of the human body are proposed as the source of time-variant coupling capacitances causing motion artifacts, which questions the applicability of adaptive filtering approaches to the problem of time-variant coupling capacitance. Ballistocardiogram (BCG) measurements on cECG electrodes are recorded and analyzed to investigate how these mechanical vibrations reflect on a differential biopotential measurement. Furthermore, using measured signals in a human experiment, numerical and test bench simulations were conducted to replicate how triboelectric surface charges might cause deformation on cECG signals.

Durmus Umutcan Uguz

Papers

Physiological Motion Artifacts in Capacitive ECG: Ballistocardiographic Impedance Distortions

Multifunctional Photoplethysmography Sensor Design for Respiratory and Cardiovascular Diagnosis

Car Seats with Capacitive ECG Electrodes Can Detect Cardiac Pacemaker Spikes

Signal-Level Fusion with Convolutional Neural Networks for Capacitively Coupled ECG in the Car

Ballistocardiographic Coupling of Triboelectric Charges into Capacitive ECG