Photoplethysmography upon cold stress-impact of measurement site and acquisition mode.

Photoplethysmography (PPG) allows various statements about the physiological state. It supports multiple recording setups, i.e., application to various body sites and different acquisition modes, rendering the technique a versatile tool for various situations. Owing to anatomical, physiological and metrological factors, PPG signals differ with the actual setup. Research on such differences can deepen the understanding of prevailing physiological mechanisms and path the way towards improved or novel methods for PPG analysis. The presented work systematically investigates the impact of the cold pressor test (CPT), i.e., a painful stimulus, on the morphology of PPG signals considering different recording setups. Our investigation compares contact PPG recorded at the finger, contact PPG recorded at the earlobe and imaging PPG (iPPG), i.e., non-contact PPG, recorded at the face. The study bases on own experimental data from 39 healthy volunteers. We derived for each recording setup four common morphological PPG features from three intervals around CPT. For the same intervals, we derived blood pressure and heart rate as reference. To assess differences between the intervals, we used repeated measures ANOVA together with paired t-tests for each feature and we calculated Hedges' g to quantify effect sizes. Our analyses show a distinct impact of CPT. As expected, blood pressure shows a highly significant and persistent increase. Independently of the recording setup, all PPG features show significant changes upon CPT as well. However, there are marked differences between recording setups. Effect sizes generally differ with the finger PPG showing the strongest response. Moreover, one feature (pulse width at half amplitude) shows an inverse behavior in finger PPG and head PPG (earlobe PPG and iPPG). In addition, iPPG features behave partially different from contact PPG features as they tend to return to baseline values while contact PPG features remain altered. Our findings underline the importance of recording setup and physiological as well as metrological differences that relate to the setups. The actual setup must be considered in order to properly interpret features and use PPG. The existence of differences between recording setups and a deepened knowledge on such differences might open up novel diagnostic methods in the future.

Beat-to-Beat Blood Pressure Estimation by Photoplethysmography and Its Interpretation.

Blood pressure (BP) is among the most important vital signals. Estimation of absolute BP solely using photoplethysmography (PPG) has gained immense attention over the last years. Available works differ in terms of used features as well as classifiers and bear large differences in their results. This work aims to provide a machine learning method for absolute BP estimation, its interpretation using computational methods and its critical appraisal in face of the current literature. We used data from three different sources including 273 subjects and 259,986 single beats. We extracted multiple features from PPG signals and its derivatives. BP was estimated by xgboost regression. For interpretation we used Shapley additive values (SHAP). Absolute systolic BP estimation using a strict separation of subjects yielded a mean absolute error of 9.456mmHg and correlation of 0.730. The results markedly improve if data separation is changed (MAE: 6.366mmHg, r: 0.874). Interpretation by means of SHAP revealed four features from PPG, its derivation and its decomposition to be most relevant. The presented approach depicts a general way to interpret multivariate prediction algorithms and reveals certain features to be valuable for absolute BP estimation. Our work underlines the considerable impact of data selection and of training/testing separation, which must be considered in detail when algorithms are to be compared. In order to make our work traceable, we have made all methods available to the public.

Detection of a Stroke Volume Decrease by Machine-Learning Algorithms Based on Thoracic Bioimpedance in Experimental Hypovolaemia.

Compensated shock and hypovolaemia are frequent conditions that remain clinically undetected and can quickly cause deterioration of perioperative and critically ill patients. Automated, accurate and non-invasive detection methods are needed to avoid such critical situations. In this experimental study, we aimed to create a prediction model for stroke volume index (SVI) decrease based on electrical cardiometry (EC) measurements. Transthoracic echo served as reference for SVI assessment (SVI-TTE). In 30 healthy male volunteers, central hypovolaemia was simulated using a lower body negative pressure (LBNP) chamber. A machine-learning algorithm based on variables of EC was designed. During LBNP, SVI-TTE declined consecutively, whereas the vital signs (arterial pressures and heart rate) remained within normal ranges. Compared to heart rate (AUC: 0.83 (95% CI: 0.73-0.87)) and systolic arterial pressure (AUC: 0.82 (95% CI: 0.74-0.85)), a model integrating EC variables (AUC: 0.91 (0.83-0.94)) showed a superior ability to predict a decrease in SVI-TTE ≥ 20% (p = 0.013 compared to heart rate, and p = 0.002 compared to systolic blood pressure). Simulated central hypovolaemia was related to a substantial decline in SVI-TTE but only minor changes in vital signs. A model of EC variables based on machine-learning algorithms showed high predictive power to detect a relevant decrease in SVI and may provide an automated, non-invasive method to indicate hypovolaemia and compensated shock.

Surrogate based continuous noninvasive blood pressure measurement.

Arterial blood pressure is one of the most often measured vital parameters in clinical practice. State-of-the-art noninvasive ABP measurement technologies have noticeable limitations and are mainly based on uncomfortable techniques of complete or partial arterial occlusion by cuffs. Most commonplace devices provide only intermittent measurements, and continuous systems are bulky and difficult to apply correctly for nonprofessionals. Continuous cuffless ABP measurements are still an unmet clinical need and a topic of ongoing research, with only few commercially available devices. This paper discusses surrogate-based noninvasive blood pressure measurement techniques. It covers measurement methods of continuously and noninvasively inferring BP from surrogate signals without applying external pressures, except for reference or initialization purposes. The BP is estimated by processing signal features, so called surrogates, which are modulated by variations of BP. Discussed techniques include well-known approaches such as pulse transit time and pulse arrival time techniques, pulse wave analysis or combinations thereof. Despite a long research history, these methods have not found widespread use in clinical and ambulatory practice, in part due to technical limitations and the lack of a standardized regulatory framework. This work summarizes findings from an invited workshop of experts in the fields covering clinical expertise, engineering aspects, commercialization and standardization issues. The goal is to provide an application driven outlook, starting with clinical needs, and extending to technical actuality. It provides an outline of recommended research directions and includes a detailed overview of clinical use case scenarios for these technologies, opportunities, and limitations.

Adaptive Gaussian Mixture Model Driven Level Set Segmentation for Remote Pulse Rate Detection.

This paper presents an approach for pulse rate extraction from videos. The core of the presented approach is a novel method to segment and track a suitable region of interest (ROI). The proposed method combines level sets with subject-individual Gaussian Mixture Models to yield a time varying ROI. The ROI builds up from multiple homogeneous skin areas under constraints regarding the area and contour length of the ROI. Together with state of the art signal processing methods our approach yields an Mean Average Error (MAE) of 2.3 bpm, 1.4 bpm and 2.7 bpm on own data, the PURE database and the UBFC-rPPG database, respectively. Therewith, our method performs equal or better compared to widely used approaches (e.g. the KLT tracker instead of the proposed image processing yields an MAE of 2.6 bpm, 2.6 bpm and 4.4 bpm). Such results and the 2nd place with a MAE of 7.92 bpm in the 1st Challenge on Remote Physiological Signal Sensing prove the applicability of the proposed method. The taken approach, however, bears further potential for optimization in the context of photoplethysmography imaging and should be transferable to other segmentation tasks as well.

Association of remote imaging photoplethysmography and cutaneous perfusion in volunteers.

Remote imaging photoplethysmography (iPPG) senses the cardiac pulse in outer skin layers and is responsive to mean arterial pressure and pulse pressure in critically ill patients. Whether iPPG is sufficiently sensitive to monitor cutaneous perfusion is not known. This study aimed at determining the response of iPPG to changes in cutaneous perfusion measured by  Laser speckle imaging (LSI). Thirty-seven volunteers were engaged in a cognitive test known to evoke autonomic nervous activity and a Heat test. Simultaneous measurements of iPPG and LSI were taken at baseline and during cutaneous perfusion challenges. A perfusion index (PI) was calculated to assess iPPG signal strength. The response of iPPG to the challenges and its relation to LSI were determined. PI of iPPG significantly increased in response to autonomic nervous stimuli and to the Heat test by 5.8% (p = 0.005) and 11.1% (p < 0.001), respectively. PI was associated with LSI measures of cutaneous perfusion throughout experiments (p < 0.001). iPPG responses to study task correlated with those of LSI (r = 0.62, p < 0.001) and were comparable among subjects. iPPG is sensitive to autonomic nervous activity in volunteers and is closely associated with cutaneous perfusion.

Pulse decomposition analysis in photoplethysmography imaging.

OBJECTIVE: Photoplethysmography imaging (PPGI) has gained immense attention over the last few years but only a few works have addressed morphological analysis so far. Pulse wave decomposition (PWD), i.e. the decomposition of a pulse wave by a varying number of kernels, allows for such analyses. This work investigates the applicability of PWD algorithms in the context of PPGI. APPROACH: We used simulated and experimental data to compare various PWD algorithms from the literature regarding their robustness against noise and motion artifacts while preserving morphological information as well as regarding their ability to reveal physiological changes by PPGI. MAIN RESULTS: Our experiments prove that algorithms that combine Gamma and Gaussian distributions outperform other choices. Further, algorithms with two kernels exhibit the highest robustness against noise and motion artifacts (improvement in [Formula: see text] of 14.09 %) while preserving the morphology similarly to algorithms using more kernels. Lastly, we showed that PWD can reveal physiological changes upon distal stimuli by PPGI. SIGNIFICANCE: This work proves the feasibility of pulse decomposition analysis in PPGI, particularly for algorithms with a low number of kernels, and opens up novel applications for PPGI. Not only for PPGI but for future research on PWD in general, our findings have importance as they elucidate differences between PWD algorithms and emphasize the importance of using initial values. To support such future research, we have released the algorithms and simulated data to the public.

Remote health diagnosis and monitoring in the time of COVID-19.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Predicting sepsis with a recurrent neural network using the MIMIC III database.

OBJECTIVE: Predicting sepsis onset with a recurrent neural network and performance comparison with InSight - a previously proposed algorithm for the prediction of sepsis onset. METHODOLOGY: A retrospective analysis of adult patients admitted to the intensive care unit (from the MIMIC III database) who did not fall under the definition of sepsis at the time of admission. The area under the receiver operating characteristic (AUROC) measures the performance of the prediction task. We examine the sequence length given to the machine learning algorithms for different points in time before sepsis onset concerning the prediction performance. Additionally, the impact of sepsis onset's definition is investigated. We evaluate the model with a relatively large and thus more representative patient population compared to related works in the field. RESULTS: For a prediction 3 h prior to sepsis onset, our network achieves an AUROC of 0.81 (95% CI: 0.78-0.84). The InSight algorithm achieves an AUROC of 0.72 (95% CI: 0.69-0.75). For a fixed sensitivity of 90% our network reaches a specificity of 47.0% (95% CI: 43.1%-50.8%) compared to 31.1% (95% CI: 24.8%-37.5%) for InSight. In addition, we compare the performance for 6 and 12 h prediction time for both approaches. CONCLUSION: Our findings demonstrate that a recurrent neural network is superior to InSight considering the prediction performance. Most probably, the improvement results from the network's ability of revealing time dependencies. We show that the length of the look back has a significant impact on the performance of the classifier. We also demonstrate that for the correct detection of sepsis onset for a retrospective analysis, further research is necessary.

Pulse decomposition analysis in camera-based photoplethysmography.

Imaging photoplethysmography (iPPG) is an interesting alternative to laser speckle contrast imaging for the analysis of spatio-temporal patterns in the cutaneous microcirculation. Recent years have witnessed the development of sophisticated techniques for the non-invasive extraction of vascular-related features. These techniques, referred to as pulse decomposition algorithms (PDA), most often involve the analysis of photoplethysmographic waves. This study validated the use of a multi-Gaussian (PDA) for the automatic mapping of iPPG pulse waveforms acquired with a standard camera. We show that iPPG-based PDA can reveal differences in skin perfusion in response to cold stimuli. The study thus proves the potential for morphological analyses of the iPPG pulse waveform.

Remote Photoplethysmographic Assessment of the Peripheral Circulation in Critical Care Patients Recovering From Cardiac Surgery.

PURPOSE: Camera-based photoplethysmography (cbPPG) remotely detects the volume pulse of cardiac ejection in the peripheral circulation. The cbPPG signal is sourced from the cutaneous microcirculation, yields a 2-dimensional intensity map, and is therefore an interesting monitoring technique. In this study, we investigated whether cbPPG is in general sufficiently sensitive to discern hemodynamic conditions. METHODS: cbPPG recordings of 70 patients recovering from cardiac surgery were analyzed. Photoplethysmograms were processed offline and the optical pulse power (OPP) of cardiac ejection was calculated. Hemodynamic data, image intensity, and patient movements were recorded synchronously. The effects of hemodynamic parameters and measurement conditions on the patient's individual OPP variability and their actual OPP values were calculated in mixed-effects regression models. RESULTS: Mean arterial pressure (MAP), pulse pressure (PP), heart rate (HR), and central venous pressure (CVP) significantly explained the individual OPP variability. PP had the highest explanatory power (19.9%). Averaged OPP significantly increased with PP and MAP (P < 0.001, respectively) and decreased with higher HR (P = 0.024). CVP had a 2-directional, nonsignificant effect on averaged OPP. Image intensity and patient movements did significantly affect OPP. After adjustment for hemodynamic covariables and measurement conditions, the effect of PP and HR remained unchanged, whereas that of MAP vanished. CONCLUSION: cbPPG is sensitive to hemodynamic parameters in critical care patients. It is a potential application for monitoring the peripheral circulation. Its value in a clinical setting has to be determined.

Nocturnal ventricular repolarization lability predicts cardiovascular mortality in the Sleep Heart Health Study.

The objective of the present study was to quantify repolarization lability and its association with sex, sleep stage, and cardiovascular mortality. We analyzed polysomnographic recordings of 2,263 participants enrolled in the Sleep Heart Health Study (SHHS-2). Beat-to-beat QT interval variability (QTV) was quantified for consecutive epochs of 5 min according to the dominant sleep stage [wakefulness, nonrapid eye movement stage 2 (NREM2), nonrapid eye movement stage 3 (NREM3), and rapid eye movement (REM)]. To explore the effect of sleep stage and apnea-hypopnea index (AHI) on QT interval parameters, we used a general linear mixed model and mixed ANOVA. The Cox proportional hazards model was used for cardiovascular disease (CVD) death prediction. Sex-related differences in T wave amplitude ( P < 0.001) resulted in artificial QTV differences. Hence, we corrected QTV parameters by T wave amplitude for further analysis. Sleep stages showed a significant effect ( P < 0.001) on QTV. QTV was decreased in deep sleep compared with wakefulness, was higher in REM than in NREM, and showed a distinct relation to AHI in all sleep stages. The T wave amplitude-corrected QTV index (cQTVi) in REM sleep was predictive of CVD death (hazard ratio: 2.067, 95% confidence interval: 1.105-3.867, P < 0.05) in a proportional hazards model. We demonstrated a significant impact of sleep stages on ventricular repolarization variability. Sex differences in QTV are due to differences in T wave amplitude, which should be corrected for. Independent characteristics of QTV measures to sleep stages and AHI showed different behaviors of heart rate variability and QTV expressed as cQTVi. cQTVi during REM sleep predicts CVD death. NEW & NOTEWORTHY We demonstrate here, for the first time, a significant impact of sleep stages on ventricular repolarization variability, quantified as QT interval variability (QTV). We showed that QTV is increased in rapid eye movement sleep, reflective of high sympathetic drive, and predicts death from cardiovascular disease. Sex-related differences in QTV are shown to be owing to differences in T wave amplitude, which should be corrected for.

Robust Methods for Automated Selection of Cardiac Signals After Blind Source Separation.

OBJECTIVE: Novel minimum-contact vital signs monitoring techniques like textile or capacitive electrocardiogram (ECG) provide new opportunities for health monitoring. These techniques are sensitive to artifacts and require handling of unstable signal quality. Spatio-temporal blind source separation (BSS) is capable of processing suchlike multichannel signals. However, BSS's permutation indeterminacy requires the selection of the cardiac signal (i.e., the component resembling the electric cardiac activity) after its separation from artifacts. This study evaluates different concepts for solving permutation indeterminacy. METHODS: Novel automated component selection routines based on heartbeat detections are compared with standard concepts, as using higher order moments or frequency-domain features, for solving permutation indeterminacy in spatio-temporal BSS. BSS was applied to a textile and a capacitive ECG dataset of healthy subjects performing a motion protocol, and to the MIT-BIH Arrhythmia Database. The performance of the subsequent component selection was evaluated by means of the heartbeat detection accuracy (ACC) using an automatically selected single component. RESULTS: The proposed heartbeat-detection-based selection routines significantly outperformed the standard selectors based on Skewness, Kurtosis, and frequency-domain features, especially for datasets containing motion artifacts. For arrhythmia data, beat analysis by sparse coding outperformed simple periodicity tests of the detected heartbeats. CONCLUSION: Component selection routines based on heartbeat detections are capable of reliably selecting cardiac signals after spatio-temporal BSS in case of severe motion artifacts and arrhythmia. SIGNIFICANCE: The availability of robust cardiac component selectors for solving permutation indeterminacy facilitates the usage of spatio-temporal BSS to extract cardiac signals in artifact-sensitive minimum-contact vital signs monitoring techniques.

Cardiovascular assessment by imaging photoplethysmography - a review.

Over the last few years, the contactless acquisition of cardiovascular parameters using cameras has gained immense attention. The technique provides an optical means to acquire cardiovascular information in a very convenient way. This review provides an overview on the technique's background and current realizations. Besides giving detailed information on the most widespread application of the technique, namely the contactless acquisition of heart rate, we outline further concepts and we critically discuss the current state.

Camera-based photoplethysmography in an intraoperative setting.

BACKGROUND: Camera-based photoplethysmography (cbPPG) is a measurement technique which enables remote vital sign monitoring by using cameras. To obtain valid plethysmograms, proper regions of interest (ROIs) have to be selected in the video data. Most automated selection methods rely on specific spatial or temporal features limiting a broader application. In this work, we present a new method which overcomes those drawbacks and, therefore, allows cbPPG to be applied in an intraoperative environment. METHODS: We recorded 41 patients during surgery using an RGB and a near-infrared (NIR) camera. A Bayesian skin classifier was employed to detect suitable regions, and a level set segmentation approach to define and track ROIs based on spatial homogeneity. RESULTS: The results show stable and homogeneously illuminated ROIs. We further evaluated their quality with regards to extracted cbPPG signals. The green channel provided the best results where heart rates could be correctly estimated in 95.6% of cases. The NIR channel yielded the highest contribution in compensating false estimations. CONCLUSIONS: The proposed method proved that cbPPG is applicable in intraoperative environments. It can be easily transferred to other settings regardless of which body site is considered.

Therapy Decision Support Based on Recommender System Methods.

We present a system for data-driven therapy decision support based on techniques from the field of recommender systems. Two methods for therapy recommendation, namely, Collaborative Recommender and Demographic-based Recommender, are proposed. Both algorithms aim to predict the individual response to different therapy options using diverse patient data and recommend the therapy which is assumed to provide the best outcome for a specific patient and time, that is, consultation. The proposed methods are evaluated using a clinical database incorporating patients suffering from the autoimmune skin disease psoriasis. The Collaborative Recommender proves to generate both better outcome predictions and recommendation quality. However, due to sparsity in the data, this approach cannot provide recommendations for the entire database. In contrast, the Demographic-based Recommender performs worse on average but covers more consultations. Consequently, both methods profit from a combination into an overall recommender system.

The value of polarization in camera-based photoplethysmography.

Camera-based photoplethysmography (cbPPG) is a novel measuring technique that permits the remote acquisition of cardiovascular signals using video cameras. Research still lacks in fundamental studies to reach a deeper technical and physiological understanding. This work analyzes the employment of polarization filtration to (i) assess the gain for the signal quality and (ii) draw conclusions about the cbPPG signal's origin. We evaluated various forehead regions of 18 recordings with different color and filter settings. Our results prove that for an optimal illumination, the perpendicular filter setting provides a significant benefit. The outcome supports the theory that signals arise from blood volume changes. For lateral illumination, ballistocardiographic effects dominate the signal as polarization's impact vanishes.

Non-invasive Fetal ECG Signal Quality Assessment for Multichannel Heart Rate Estimation.

OBJECTIVE: The noninvasive fetal ECG (NI-FECG) from abdominal recordings offers novel prospects for prenatal monitoring. However, NI-FECG signals are corrupted by various nonstationary noise sources, making the processing of abdominal recordings a challenging task. In this paper, we present an online approach that dynamically assess the quality of NI-FECG to improve fetal heart rate (FHR) estimation. METHODS: Using a naive Bayes classifier, state-of-the-art and novel signal quality indices (SQIs), and an existing adaptive Kalman filter, FHR estimation was improved. For the purpose of training and validating the proposed methods, a large annotated private clinical dataset was used. RESULTS: The suggested classification scheme demonstrated an accuracy of Krippendorff's alpha in determining the overall quality of NI-FECG signals. The proposed Kalman filter outperformed alternative methods for FHR estimation achieving accuracy. CONCLUSION: The proposed algorithm was able to reliably reflect changes of signal quality and can be used in improving FHR estimation. SIGNIFICANCE: NI-ECG signal quality estimation and multichannel information fusion are largely unexplored topics. Based on previous works, multichannel FHR estimation is a field that could strongly benefit from such methods. The developed SQI algorithms as well as resulting classifier were made available under a GNU GPL open-source license and contributed to the FECGSYN toolbox.

Assessment of blind source separation techniques for video-based cardiac pulse extraction.

Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function.

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.