Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Spontaneous blood pressure (BP) and R‐R variability are used frequently as ‘windows’ into cardiovascular control mechanisms. However, the origin of these rhythmic fluctuations is not completely understood. In this study, with ganglion blockade, we evaluated the role of autonomic neural activity versus other ‘non‐neural’ factors in the origin of BP and R‐R variability in humans. Beat‐to‐beat BP, R‐R interval and respiratory excursions were recorded in ten healthy subjects (aged 30 ± 6 years) before and after ganglion blockade with trimethaphan. The spectral power of these variables was calculated in the very low (0.0078‐0.05 Hz), low (0.05‐0.15 Hz) and high (0.15‐0.35 Hz) frequency ranges. The relationship between systolic BP and R‐R variability was examined by cross‐spectral analysis. After blockade, R‐R variability was virtually abolished at all frequencies; however, respiration and high frequency BP variability remained unchanged. Very low and low frequency BP variability was reduced substantially by 84 and 69 %, respectively, but still persisted. Transfer function gain between systolic BP and R‐R interval variability decreased by 92 and 88 % at low and high frequencies, respectively, while the phase changed from negative to positive values at the high frequencies. These data suggest that under supine resting conditions with spontaneous breathing: (1) R‐R variability at all measured frequencies is predominantly controlled by autonomic neural activity; (2) BP variability at high frequencies (> 0.15 Hz) is mediated largely, if not exclusively, by mechanical effects of respiration on intrathoracic pressure and/or cardiac filling; (3) BP variability at very low and low frequencies (< 0.15 Hz) is probably mediated by both sympathetic nerve activity and intrinsic vasomotor rhythmicity; and (4) the dynamic relationship between BP and R‐R variability as quantified by transfer function analysis is determined predominantly by autonomic neural activity rather than other, non‐neural factors.

Analysis of biomedical signals is a very challenging task involving implementation of various advanced signal processing methods. This area is rapidly developing. This paper is a Part III paper, where the most popular and efficient digital signal processing methods are presented. This paper covers the following bioelectrical signals and their processing methods: electromyography (EMG), electroneurography (ENG), electrogastrography (EGG), electrooculography (EOG), electroretinography (ERG), and electrohysterography (EHG).

Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today’s clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.

As it was mentioned in the previous part of this work (Part I)—the advanced signal processing methods are one of the quickest and the most dynamically developing scientific areas of biomedical engineering with their increasing usage in current clinical practice. In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared. It also describes both classical and advanced approaches for noise contamination removal such as among the others digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation, and wavelet transform.

This article provides the outcomes of complex testing of a novel low-cost and safe breathing sensor using fiber-optic technology under real magnetic resonance (MR) examination at 3 T. The sensor consists of the Bragg grating embedded into an oxygen breathing mask [i.e., fiber-optic breathing mask (FOBM)], thus minimizing movement artifacts and preventing MR interference. The obtained triggered MR images were evaluated using both objective and subjective image analysis methods, such as signal-to-noise ratio, contrast-noise ratio, and the analysis performed by radiologists. The system was compared with Siemens Prisma Respiratory Reference based on the navigator technique and pneumatic respiratory belt. Image quality assessment provided by radiologist experts shows that the FOBM approach provides reliable and accurate results since the overall image quality reached an average score of 3.3 ± 0.4, 3.7 ± 0.5, and 3.8 ± 0.2 for FOBM, Siemens respiratory reference, and respiratory belts, respectively.