This paper describes the design, construction, and testing of a multi-channel fetal electrocardiogram (fECG) signal generator based on LabVIEW. Special attention is paid to the fetal heart development in relation to the fetus' anatomy, physiology, and pathology. The non-invasive signal generator enables many parameters to be set, including fetal heart rate (FHR), maternal heart rate (MHR), gestational age (GA), fECG interferences (biological and technical artifacts), as well as other fECG signal characteristics. Furthermore, based on the change in the FHR and in the T wave-to-QRS complex ratio (T/QRS), the generator enables manifestations of hypoxic states (hypoxemia, hypoxia, and asphyxia) to be monitored while complying with clinical recommendations for classifications in cardiotocography (CTG) and fECG ST segment analysis (STAN). The generator can also produce synthetic signals with defined properties for 6 input leads (4 abdominal and 2 thoracic). Such signals are well suited to the testing of new and existing methods of fECG processing and are effective in suppressing maternal ECG while non-invasively monitoring abdominal fECG. They may also contribute to the development of a new diagnostic method, which may be referred to as non-invasive trans-abdominal CTG + STAN. The functional prototype is based on virtual instrumentation using the LabVIEW developmental environment and its associated data acquisition measurement cards (DAQmx). The generator also makes it possible to create synthetic signals and measure actual fetal and maternal ECGs by means of bioelectrodes.
The design, implementation, and verification of a signal simulator for the generation of patho-physiological records of foetal electrocardiograms (fECGs) during the prenatal period are briefly reported. The simulator enables users to model the patho-physiological changes that occur within the foetus' myocardium under hypoxic conditions (hypoxemia, hypoxia, asphyxia, etc.) during the 20th to 42nd week of pregnancy. The simulator deploys a dynamic fECG model including an actual fECG record taken from clinical practice, patho-physiological cardiotocography (CTG), and ST-analysis (STAN) records along with the ratio of T waves to the QRS complex; as well as clinical recommendations by FIGO (International Federation of Gynecology and Obstetrics) for classifying these records. By comparing synthesised and real patho-physiological CTG and STAN records, the functionality of the simulator, which effectively captured significant indicators of the foetus' condition during the prenatal period including fECG morphology, dynamic fECG characteristics, and others is evaluated and validated. The simulator enables users to test both current and emerging approaches in a very challenging area of gynaecology, namely the identification/classification of hypoxic conditions in the foetus during labour. Obstetricians can also use the simulator as a reference tool during the evaluation of suspect fECG abnormalities.
This pilot study focuses on the design, implementation, optimization and verification of a novel solution of smart measuring of water consumption and crisis detection leading to a smart water management platform. The system implemented consists of a modular IoT platform based on a PCB (Printed Circuit Board) design using the M2.COM standard, a LoraWAN modem and a LoraWAN gateway based on the Raspberry Pi platform. The prototype is modular, low-cost, low-power, low-complex and it fully reflects the requirements of strategic technological concepts of Smart City and Industry 4.0, i.e., data integration, interoperability, (I)IoT, etc. The study was produced in cooperation with M.I.S Protivanov and VODARENSKA AKCIOVA SPOLECNOST, a.s. (industry partners distributing drinking water in the Olomouc and South-Moravian regions) to depict the current situation in the Czech Republic, characterized by extreme weather fluctuations and increasingly frequent periods of drought. These drinking water distributors are also constantly placing new demands on these smart solutions. These requirements include, above all, reliability of data transmission, modularity and, last but not least, low cost. However, smart water management (water consumption, distribution, system identification, equipment maintenance, etc.) is becoming an important topic worldwide. The functionality of the system was first verified in laboratory conditions and, then, in real operation. The study also includes checking signal propagation in the municipal area of the village of Zdarna, where the radius of the proposed measuring system was tested. A laboratory test with simulation of water leakage is also part of this work. Subsequently, the system was tested in a residential unit by means of water leakage detection using the MNF method (minimum night flow); the detection success rate was 95%.
Here the authors explore, implement and verify the potential utility of hybrid intelligent adaptive systems for processing and analysis of multi‐channel non‐invasive abdominal foetal electrocardiogram (fECG) signals. This approach allows clinicians to enhance non‐invasive cardiotocography (CTG) with continuous ST waveform analysis (STAN) of fECG signals to improve intrapartum monitoring during labuor. The system uses a multi‐channel adaptive neuro‐fuzzy interference system with a new hybrid learning algorithm based on uniquely synthesised data, which comports well with real data acquired from clinical practice. The system allows the user to obtain a reference signal for objective verification. The functionality of the system has been evaluated not only by subjective criteria (an fECG morphology study by a gynaecologist), but also by objective criteria using quantitative performance metrics such as input and output signal‐to‐noise ratios, root mean square error, sensitivity S+, and positive predictive value among others. Experimental results indicate that hybrid neuro‐fuzzy systems have the potential to improve the diagnostic and monitoring qualities (sensitivity and specificity) of fECG signals while preserving their clinically important features by leveraging the combined utility of non‐invasive CTG and STAN.
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