From the vibration analysis generated by biological and mechanical systems, it is possible to determine whether there has been a fundamental change in their time or frequency domain. This analysis can provide information about the internal state of the system and its dynamics without the need for invasive intervention. In addition, the measured data can be used together with advanced signal processing algorithms (neural networks or machine learning) to create a predictive maintenance and diagnostic system. According to the measured data diagnostics, it will be possible to identify a defective component (e.g. a damaged bearing, loose part or worn belt), replace it and prevent possible system failure. This work describes a modular measuring device based on closed acoustic tube. Acoustic tube parameters are measured and optimized for use in remote monitoring, which is part of a project dealing with measurement of vital signs in magnetic resonance.
This paper presents the comparison of fibre-optic Bragg Grating Sensor with the commercially available probes for heart sounds measurement based on microphones. The analysis of the sensitivity and specificity was carried out to assess the efficacy of the individual measuring probes. Since fibre-optic sensing uses light in optical fibre rather than electricity, it solves the limitations of electrical sensors such as transmission loss and susceptibility to electromagnetic interference (EMI). Experimental results have shown that Fibre-Optic Bragg Grating Sensor significantly outperforms the devices using the microphones. Moreover, the sensor embedded in polydimethylsiloxane polymer and is fixed on the thorax by means of elastic belt. The material is biocompatible and immune to electromagnetic interference, which is major advantage for the healthcare environment. The probe dimensions are small; therefore, it would be convenient for the patient and easily implemented into clinical practice. Nevertheless, the signal processing methods must be applied to separate the desired signal from the environmental noise.
KEYWORDS: Fetus, Independent component analysis, Principal component analysis, Signal to noise ratio, Signal processing, Heart, Sensors, Electrodes, Abdomen, Ultrasonography
Fetal Phonocardiography (fPCG) is still secondary tool but provides very important information about fetal well-being that cannot be given by another fetal monitoring method. Independent Component Analysis (ICA) and Principal Component Analysis (PCA) were chosen for testing on synthetic data that are able to extract fPCG from abdominal signals. Results show that ICA and PCA could be used in clinical practice for fetal Heart Rate (fHR) monitoring, because after extraction of components it is easy to determine fHR. Signal to Noise Ratio (SNR) proved that after the extraction there was a significant improvement in estimated signal to compare with input abdominal signals. We found that ICA method works better than PCA method on this data, even though it changes the amplitude of the output components.
This article deals with the implementation of fiber-optic Bragg Grating Sensors signal processing methods for the detection of respiration rate, pulse rate, and body temperature. The sensed signals are influenced by a variety of interferences (motion artifact, environmental noise, etc.). Clinically relevant information is only available at certain frequencies, while the utilized optical sensor is able to cover relatively broad spectrum range. For real-world medical applications, the desired signal needs to be separated from the noise, which can often be other clinical information. This article introduces a virtual instrument for the extraction of clinically relevant information, such as respiration and heart rate, and body temperature. Frequency-selective filters were implemented in the proposed application. The functionality of the application was tested on real data using the FBGUARD and LabVIEW evaluation unit. The results were verified with commercially available devices and also statistically processed. Experimental results have shown that Fiber-Optic Bragg Grating Sensor signal processing is a key aspect of a successful incorporation of these sensors into clinical practice.
This article is focused on the advanced signal processing methods for third-generation sensors requirements. These sensors are based on the influence of a non-electric quantities on a light beam. This generation of sensors, also known as fiber optic sensors, is based on the principles of optoelectronics and integrated optics. These sensors are used in a variety of real-world applications such as biomedical engineering, industry 4.0, transportation, etc. In real-world applications, the signals sensed by these sensors are distorted by a variety of interference due to its sensitivity. We often encounter the problem that the useful information and the interference overlap in the spectral domain, therefore we cannot use conventional frequency selective filters. This article focuses on the implementation of adaptive filtering, Principle Component Analysis and Independent Component Analysis to reduce the interference in various application areas. The methods were tested on real data. This paper offers the comparison of the tested methods in different application areas.
This paper deals with methods for processing signals from an optical interferometer to monitor vital signs (Respiration Rate and Heart Rate). Optical interferometer signals are contaminated by variety of technical and biological artifacts (motion artifacts, hospital/patient-generated noise, etc.). Tested optical sensors are very sensitive, it therefore crucial to reduce such unwanted signals. In this article, a complex application for processing the signals from optical interferometer based on virtual instrumentation was developed. The experiments were conducted on data sensed by optical interferometer using a National Instruments card NI USB-6216 BNC and application in the LabVIEW environment. Frequency selective filters were tested in the experiments. The results obtained by using optical interferometer were statistically compared with the ECG and PCG reference. According to the results, optical interferometers are able to measure both the Respiration and Heart Rate under the given conditions. Unfortunately, the measurement is very difficult to replicate in the hospital environment, which is the primary reason why these methods are not used in clinical practice.
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