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.

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.

The authors of this article deals with the implementation of a combination of techniques of the fuzzy system and artificial intelligence in the application area of non-linear noise and interference suppression. This structure used is called an Adaptive Neuro Fuzzy Inference System (ANFIS). This system finds practical use mainly in audio telephone (mobile) communication in a noisy environment (transport, production halls, sports matches, etc). Experimental methods based on the two-input adaptive noise cancellation concept was clearly outlined. Within the experiments carried out, the authors created, based on the ANFIS structure, a comprehensive system for adaptive suppression of unwanted background interference that occurs in audio communication and degrades the audio signal. The system designed has been tested on real voice signals. This article presents the investigation and comparison amongst three distinct approaches to noise cancellation in speech; they are LMS (least mean squares) and RLS (recursive least squares) adaptive filtering and ANFIS. A careful review of literatures indicated the importance of non-linear adaptive algorithms over linear ones in noise cancellation. It was concluded that the ANFIS approach had the overall best performance as it efficiently cancelled noise even in highly noise-degraded speech. Results were drawn from the successful experimentation, subjective-based tests were used to analyse their comparative performance while objective tests were used to validate them. Implementation of algorithms was experimentally carried out in Matlab to justify the claims and determine their relative performances.

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