Wearable optical fiber sensor based on a bend singlemode-multimode-singlemode fiber structure for respiration monitoring

Respiration rate (RR) is an important information related to human physiological health. A wearable optical fiber sensor for respiration monitoring based on a bend singlemode-multimodesinglemode (SMS) fiber structure, which is highly sensitive to bend, is firstly proposed and experimentally demonstrated. The sensor fastened by an elastic belt on the abdomen of a person will acquire the respiration signal when the person breaths, which will introduce front and back movement of the abdomen, and thus bend of SMS fiber structure. Short-time Fourier transform (STFT) method is employed for signal processing to extract characteristic information of both the time and frequency domain of the measured waveform, which provides accurate RR measurement. Six different SMS fiber sensors have been tested by six individuals and the experimental results demonstrated that the RR signals can be effectively monitored among different individuals, where an average Pearson Correlation Coefficient of 0.88 of the respiration signal has been achieved, which agrees very well with that of commercial belt respiration sensor. The proposed technique can provide a new wearable and portable solution for monitoring of respiratory with advantage of easy fabrication and robust to environment

A phonocardiographic-based fiber-optic sensor and adaptive filtering system for noninvasive continuous fetal heart rate monitoring

This paper focuses on the design, realization, and verification of a novel phonocardiographic-based fiber-optic sensor and adaptive signal processing system for noninvasive continuous fetal heart rate (fHR) monitoring. Our proposed system utilizes two Mach-Zehnder interferometeric sensors. Based on the analysis of real measurement data, we developed a simplified dynamic model for the generation and distribution of heart sounds throughout the human body. Building on this signal model, we then designed, implemented, and verified our adaptive signal processing system by implementing two stochastic gradient-based algorithms: the Least Mean Square Algorithm (LMS), and the Normalized Least Mean Square (NLMS) Algorithm. With this system we were able to extract the fHR information from high quality fetal phonocardiograms (fPCGs), filtered from abdominal maternal phonocardiograms (mPCGs) by performing fPCG signal peak detection. Common signal processing methods such as linear filtering, signal subtraction, and others could not be used for this purpose as fPCG and mPCG signals share overlapping frequency spectra. The performance of the adaptive system was evaluated by using both qualitative (gynecological studies) and quantitative measures such as: Signal-to-Noise Ratio-SNR, Root Mean Square Error-RMSE, Sensitivity-S+, and Positive Predictive Value-PPV.Web of Science174art. no. 89

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system

Laser doppler vibrometry for cardiovascular monitoring and photoacoustic imaging

Nowadays, techniques for health monitoring mainly require physical contact with patients, which is not always ideal. Non-contact health monitoring has become an important research topic in the last decades. The non-contact detection of a patient's health condition represents a beneficial tool in different biomedical fields. Examples can be found in intensive care, home health care, the nursing of the elderly, the monitoring of physical efforts, and in human-machine interactions. Cardiovascular diseases (CV) are one of the most spread causes of death in developed countries. Their monitoring techniques involve physical contact with patients. A non-contact technique for cardiovascular monitoring could overcome problems related to the contact with the patient such as skin lesions. It could also expand the availability of monitoring to those cases where contact is not possible or should be avoided to reduce the exposure of medical personnel to biochemical hazard conditions.Several research groups have investigated different techniques for non-contact monitoring of health; among them, the laser Doppler Vibrometry (LDVy) has one of the highest accuracies and signal to noise ratios for cardiorespiratory signals detection. Moreover, the simplicity of data processing, the long-distance measurement range, and the high bandwidth make the laser Doppler vibrometer (LDV) suitable for daily measurements. LDVy is an interferometric technique employed for the measurements of displacement or velocity signals in various fields. In particular, it is deployed in the biomedical field for the extraction of several cardiovascular parameters, such as the PR-time. Generally, the extraction of these parameters requires ideal measuring conditions (measuring spot and laser direction), which are not realistic for daily monitoring in non-laboratory conditions, and especially in tracking applications. The first scientific hypothesis of this work is that the PR-time detected with LDV has an acceptable uncertainty for a realistic variety of measurement spot positions and angles of the incident laser beam. Therefore, I investigated the uncertainty contribution to the detection of the PR-time from LDV signals resulting from the laser beam direction and from the measurement point position; these investigations were carried out with a multipoint laser Doppler vibrometer. The uncertainties were evaluated according to the Guide to the Expression of Uncertainty in Measurement. Successively, the ranges of PR-time values where it is possible to state with 95% certainty that a diagnosis is correct are identified. Normal values of PR-time are included in the range 120 ms -200 ms. For single value measurements with precise alignment the reliable range for the detection of the healthy condition is 146.4 ms -173.6 ms. The detection of CV diseases is reliable for measured values lower than 93.6 ms and greater than 226.4 ms. For mean value measurements with precise alignment the reliable range for the detection of the healthy condition is 126.6 ms -193.4 ms. The detection of CV diseases is reliable for measured values lower than 113.4 ms and greater than 206.6 ms. Therefore, for measured values included in the mentioned ranges, the detection of the PR-time and relative diagnosis with the LDVy in non-laboratory conditions is reliable. The method for the estimation of the uncertainty contribution proposed in this work can be applied to other cardiovascular parameters extracted with the LDVy. Recently, the LDVy was employed for the detection of tumors in tissue-mimic phantoms as a noncontact alternative to the ultrasound sensors employed in photoacoustic imaging (PAI). A non-contact method has considerable advantages for photoacustic imaging, too. Several works present the possibility to perform PAI measurements with LDVy. However, a successful detection of the signals generated by a tumor depends on the metrological characteristics of the LDV, on the properties of the tumor and of the tissue. The conditions under which a tumor is detectable with the laser Doppler vibrometer has not been investigated yet. The second scientific hypothesis of this work is that, under certain conditions, photoacoustic imaging measurements with LDVy are feasible. Therefore, I identified those conditions to determine the detection limits of LDVy for PAI measurements. These limits were deduced by considering the metrological characteristics of a commercial LDV, the dimensions and the position of the tumor in the tissue. I derived a model for the generation and propagation of PA signals and its detection with an LDV. The model was validated by performing experiments on silicone tissue-micking phantoms. The validated model with breast-tissue parameters reveals the limits of tumor detection with LDVy-based PAI. The results show that commercial LDVs can detect tumors with a minimal radius of ≈350 μm reliably if they are located at a maximal depth in tissue of ≈2 cm. Depending on the position of the detection point, the maximal depth can diminish and depending on the absorption characteristics of the tumor, the detection range increases.Heutzutage erfordern Techniken zur Gesundheitsüberwachung hauptsächlich den physischen Kontakt mit dem Patienten, was nicht immer ideal ist. Die berührungslose Gesundheitsüberwachung hat sich in den letzten Jahrzehnten zu einem wichtigen Forschungsthema entwickelt. Die berührungslose Erkennung des Gesundheitszustands eines Patienten stellt ein nützliches Instrument in verschiedenen biomedizinischen Bereichen dar. Beispiele finden sich in der Intensivpflege, der häuslichen Krankenpflege, der Altenpflege, der Überwachung körperlicher Anstrengungen und in der MenschMaschine-Interaktion. Herz-Kreislauf-Erkrankungen sind eine der am weitesten verbreiteten Todesursachen in den Industrieländern. Ihre Überwachungstechniken erfordern einen physischen Kontakt mit den Patienten. Eine berührungslose Technik für die Überwachung von Herz-KreislaufErkrankungen könnte Probleme im Zusammenhang mit dem Kontakt mit dem Patienten, wie z. B. Hautverletzungen, überwinden. Verschiedene Messgeräte wurden für die berührungslose Überwachung der Gesundheit untersucht; unter ihnen hat das Laser-Doppler-Vibrometrer (LDV) eine der höchsten Genauigkeiten und Signal-Rausch-Verhältnisse für die Erkennung kardiorespiratorischer Signale. Darüber hinaus ist das Laser-Doppler-Vibrometer (LDV) aufgrund der einfachen Datenverarbeitung, des großen Messbereichs und der hohen Bandbreite für tägliche Messungen geeignet. LDV ist ein interferometrisches Verfahren, das zur Messung von Weg- oder Geschwindigkeitssignalen in verschiedenen Bereichen eingesetzt wird. Insbesondere wird es im biomedizinischen Bereich für die Extraktion verschiedener kardiovaskulärer Parameter, wie z. B. der PR-Zeit, eingesetzt. Im Allgemeinen erfordert die Extraktion dieser Parameter ideale Messbedingungen (Messfleck und Laserrichtung), die für die tägliche Überwachung unter Nicht-Laborbedingungen und insbesondere für TrackingAnwendungen nicht realistisch sind. Die erste wissenschaftliche Hypothese dieser Arbeit ist, dass die mit dem LDV ermittelte PR-Zeit eine akzeptable Unsicherheit für eine realistische Vielzahl von Messpunktpositionen und Winkeln des einfallenden Laserstrahls aufweist. Daher wurde der Unsicherheitsbeitrag zur Ermittlung der PR-Zeit aus LDV-Signalen untersucht, der sich aus der Laserstrahlrichtung und der Messpunktposition ergibt; diese Untersuchungen wurden mit einem Mehrpunkt-Laser-Doppler-Vibrometer durchgeführt. Die Unsicherheiten wurden gemäß der Technische Regel ISO/IEC Guide 98-3:2008-09 Messunsicherheit – Teil 3: Leitfaden zur Angabe der Unsicherheit beim Messen bewertet. Nacheinander werden die Bereiche der PR-Zeit-Werte ermittelt, in denen mit 95%iger Sicherheit eine korrekte Diagnose gestellt werden kann. Die in dieser Arbeit vorgeschlagene Methode zur Schätzung des Unsicherheitsbeitrags kann auch auf andere kardiovaskuläre Parameter angewendet werden, die mit dem LDV extrahiert werden. Kürzlich wurde das LDV zur Erkennung von Tumoren in gewebeähnlichen Phantomen als berührungslose Alternative zu den Ultraschallsensoren eingesetzt, die bei der photoakustischen Bildgebung (PAI) verwendet werden. Eine berührungslose Methode hat auch für die photoakustische Bildgebung erhebliche Vorteile. In mehreren Arbeiten wird die Möglichkeit vorgestellt, PAIMessungen mit LDV durchzuführen. Die erfolgreiche Erkennung der von einem Tumor erzeugten Signale hängt jedoch von den messtechnischen Eigenschaften des LDV sowie von den Eigenschaften des Tumors und des Gewebes ab. Die Bedingungen, unter denen ein Tumor mit dem LDV detektierbar ist, wurden bisher nicht untersucht. Die zweite wissenschaftliche Hypothese dieser Arbeit ist, dass unter bestimmten Bedingungen photoakustische Bildgebungsmessungen mit dem LDV möglich sind. Daher wurden diese Bedingungen ermittelt, um die Nachweisgrenzen von LDV für PAI-Messungen zu bestimmen. Diese Grenzen wurden unter Berücksichtigung der messtechnischen Eigenschaften eines handelsüblichen LDV, der Abmessungen und der Position des Tumors im Gewebe abgeleitet. In dieser Arbeit wurde ein Modell für die Erzeugung und Ausbreitung von PA-Signalen und deren Nachweis mit einem LDV abgeleitet. Das Modell wurde durch Experimente an Silikongewebe-Phantomen validiert. Das validierte Modell mit Parametern des Brustgewebes zeigt die Grenzen der Tumorerkennung mit LDV-basierter PAI auf. Die Ergebnisse zeigen, dass kommerzielle LDV Tumore mit einem minimalen Radius von ≈350 μm zuverlässig erkennen können

Performance Comparison for Ballistocardiogram Peak Detection Methods

Citation: Suliman, A., Carlson, C., Ade, C. J., Warren, S., & Thompson, D. E. (2019). Performance Comparison for Ballistocardiogram Peak Detection Methods. IEEE Access, 7, 53945–53955. https://doi.org/10.1109/ACCESS.2019.2912650A number of research groups have proposed methods for ballistocardiogram (BCG) peak detection toward the identification of individual cardiac cycles. However, objective comparisons of these proposed methods are lacking. This paper, therefore, conducts a systematic and objective performance evaluation and comparison of several of these approaches. Five peak-detection methods (three replicated from the literature and two adapted from code provided by the methods' authors) are compared using data from 30 volunteers. A basic cross-correlation approach was also included as a sixth method. Two high-performing methods were identified: the method proposed by Sadek et al. and the method proposed by Brüser et al. The first achieved the highest average peak-detection rate of 94%, the lowest average false alarm rate of 0.0552 false alarms per second, and a relatively small mean absolute error between the real and detected peaks: 0.0175 seconds. The second method achieved the lowest mean absolute error of 0.0088 seconds between the real and detected peaks, an average peak-detection success rate of 89%, and 0.0766 false alarms per second. All metrics are averaged across participants

Low-cost wearable sensor based on a D-shaped plastic optical fiber for respiration monitoring

A low cost, wearable textile-based respiratory sensing system is proposed and experimentally demonstrated. A highly sensitive D-shaped plastic optical fiber (POF) sensor that responds to bending is integrated into an elastic band structure to form a respiratory sensing system. The curvature sensing experiments were conducted on the D-shaped POF sensor, which has a coefficient of determination (R2) of 0.9977. The system can be used to monitor not only the respiratory rate (RR) of the human body under different movement states (resting, walking and running), but also the RR of steady and unsteady respiratory signals due to different physiological states. In addition, using the proposed signal processing technique, the interference of motion noise can be removed and the influence of body movement on the sensor response can be eliminated. The advantages of the system are its low cost, compactness and simplicity in design. Thus, the application of the proposed respiratory sensing system provides a simple and inexpensive optical solution for wearable health

Estimating Thoracic Movement with High-Sampling Rate THz Technology

We use a high-sampling rate terahertz (THz) homodyne spectroscopy system to estimate thoracic movement from healthy subjects performing breathing at different frequencies. The THz system provides both the amplitude and phase of the THz wave. From the raw phase information, a motion signal is estimated. An electrocardiogram (ECG) signal is recorded with a polar chest strap to obtain ECG-derived respiration information. While the ECG showed sub-optimal performance for the purpose and only provided usable information for some subjects, the signal derived from the THz system showed good agreement with the measurement protocol. Over all the subjects, a root mean square estimation error of 1.40 BPM is obtained

MR fully compatible and safe FBG breathing sensor: A practical solution for respiratory triggering

This publication describes an original simple low-cost MR fully-compatible and safe fiber-optic breathing sensor (FOBS), which can be used for respiratory triggering and for monitoring the development of respiratory rate within the MR environment and can, thus, serve as prevention from the hyperventilation syndrome. The sensor is created by encapsulation of the Bragg grating into conventional nasal oxygen cannulas. The sensor is immune to minor patient movements, thus limiting movement artifacts to a minimum. Thanks to this fact it can be used for the retrospective/prospective respiratory gating. The sensor is immune to electromagnetic interference (EMI) and can thus be used in any magnetic field (1.5T, 3T, and 7T). The sensor prototype has been tested in both laboratory and real magnetic resonance (3T) environments relative to conventional pneumatic respiration references (PRR). The data measured were statistically evaluated using the objective Bland-Altman method (BAM) and the functionality of the proposed solution was confirmed. Respiratory Triggering functionality was confirmed by the radiologic doctors on the basis of analyzing images using the most used respiratory triggered T2 TSE 3D sequences and by objective method using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE).Web of Science712302512301