Hidden Markov model in nonnegative matrix factorization for fetal heart rate estimation using physiological priors.

Objective.Fetal heart rate (fHR) analysis remains the most common technique for detecting fetal distress when monitoring the fetal well-being during labor. If cardiotocography (CTG) is nowadays the non-invasive clinical reference technique for fHR measurement, it suffers from several drawbacks, hence an increasing interest towards alternative technologies, especially around abdominal ECG (aECG).Approach.An original solution, using a single abdominal lead, was recently proposed to address both the feasibility in clinical routine and the challenging detection of temporal events when facing interfered signals from real life conditions. Based on a specification of the non-negative matrix factorization (NMF) algorithm, it exploits the semi-periodicity of fetal electrocardiogram (fECG) for fHR estimation. However, this method assumes temporal independence and therefore does not consider the continuity property of fHR values. It is thus proposed to add to the NMF framework a hidden Markov model (HMM) to include physiological information about fHR temporal evolution. Under a statistical setting, constraints have been added by accommodating regularization terms through Bayesian priors.Main results.The proposed method is evaluated on 23 real aECG signals from a new clinical database, according to CTG reference, and compared with the original NMF-only algorithm. The new proposed method improves performance, with an agreement with CTG increasing from 71% to 80%.Significance.This highlights the interest of a better modelization of the fHR characteristics for a more robust estimation.

Residual convolutional autoencoder combined with a non-negative matrix factorization to estimate fetal heart rate.

The fetal heart rate (fHR) plays an important role in the determination of the good health of the fetus. Beside the traditional Doppler ultrasound technique, non-invasive fetal electrocardiography (fECG) has become an interesting alternative. However, extracting clean fECG from abdominal ECG (aECG) recordings is a challenging task due to the presence of the maternal ECG component and various noise sources. In this context, we propose a deep residual convolutional autoencoder network trained on synthetic aECG simulations followed by a transfer learning phase on real aECG recordings to extract the cleanest fECG. Afterwards, we propose to use a non-negative matrix factorization based approach on the obtained fECG to estimate the fHR. Our method is evaluated on three publicly available databases demonstrating that it can provide significant performance improvement against comparative methodologies. Clinical relevance- The presented method has the advantage of estimating the fetal heart rate from a single-channel abdominal electrocardiogram without prior knowledge on the noise sources nor the maternal R-peak locations.

Abdominal cardiovascular sound recording and analysis using cardio-microphones.

In view of using abdominal microphones for fetal heart rate (FHR) monitoring, the analysis of the obtained abdominal phonocardiogram (PCG) signals is complex due to many interferential noises including blood flow sounds. In order to improve the understanding of abdominal phonocardiography, a preliminary study was conducted in one healthy volunteer and designed to characterize the PCG signals all over the abdomen. Acquisitions of PCG signals in different abdominal areas were realized, synchronously with one thoracic PCG signal and one electrocardiogram signal. The analysis was carried out based on the temporal behavior, amplitude and mean pattern of each signal. The synchronized rhythmic signature of each signal confirms that the PCG signals obtained on the abdominal area are resulting from heart function. However, the abdominal PCG patterns are totally different from the thoracic PCG one, suggesting the recording of vascular blood flow sounds on the abdomen instead of cardiac valve sounds. Moreover, the abdominal signal magnitude depends on the sensor position and therefore to the size of the underlying vessel. The sounds characterization of abdominal PCG signals could help improving the processing of such signals in the purpose of FHR monitoring.

Automated Quality Assessment for Accelerometer-Based Heart Sounds Recorded with a Novel Subcutaneous Medical Implant.

In the context of monitoring patients with heart failure conditions, the automated assessment of heart sound quality is of major importance to insure the relevance of the medical analysis of the heart sound data. We propose in this study a technique of quality classification based on the selection of a small set of representative features. The first features are chosen to characterize whether the periodicity, complexity or statistical nature of the heart sound recordings. After segmentation process, the latter features are probing the detectability of the heart sounds in cardiac cycles. Our method is applied on a novel subcutaneous medical implant that combines ECG and accelerometric-based heart sound measurements. The actual prototype is in pre-clinical phase and has been implanted on 4 pigs, which anatomy and activity constitute a challenging environment for obtaining clean heart sounds. As reference quality labeling, we have performed a three-class manual annotation of each recording, qualified as "good", "unsure" and "bad". Our method allows to retrieve good quality heart sounds with a sensitivity and an accuracy of 82% ± 2% and 88% ± 6% respectively. Clinical Relevance- By accurately recovering high quality heart sound sequences, our method will enable to monitor reliable physiological indicators of heart failure complications such as decompensation.

Fetal ECG denoising using dynamic time warping template subtraction.

The analysis of the fetal electrocardiogram (ECG) requires to remove the mother ECG (mECG) from the abdominal ECG signals. Template subtraction is a method that consists in modeling and removing the mECG's mean period i.e. the signal waveform defined as the Euclidean mean of all periods. This mean period is then subtracted to all periods to extract the fetal ECG (fECG). Such a method is not accurate because each mECG's period is not correctly aligned with the mean period. We propose to take account of the diffeomorphism of each period to improve the precision of the model and remove the mECG more efficiently. The soft-dynamic time warping (DTW) algorithm is used to compute the mean mECG period and the alignment between the mean period and all periods. Our approach is compared to a classic template subtraction on synthetic and real databases. Results show that considering the dynamic time warping allows a better removal of the mECG. Clinical relevance - The template subtraction is modified in this paper to consider the time warping for each mother ECG's period in order to improve the fetal ECG extraction from the abdominal ECG.

A new multimodal device for atrial fibrillation detection: ECG quality analysis in healthy volunteers.

In the context of increase in cardiovascular diseases in the aging population, including a high prevalence of atrial fibrillation (AF), the development of medical devices to ensure patient follow-up is of major interest. The purpose of this study is to assess the ECG signal quality of a one-lead in a new miniaturized device on healthy volunteers submitted to several conditions reflecting daily life activity. Our results show that the P wave identification is not enough reliable to consider the detection of its potential disappearance in case of AF. However, we show that the ECG signals can be used to robustly detect the RR intervals. To conclude, for rhythm disturbance detection, an automatic and specific analysis of RR variability has now to be integrated in this new multimodal device. Clinical relevance - This study confirms the potential interest of implantable medical device in the management of cardiac arrhythmias notably in the context of the individual follow up of aging patients.

Accelerometry-derived respiratory index estimating apnea-hypopnea index for sleep apnea screening.

BACKGROUND AND OBJECTIVE: Sleep Apnea Syndrome (SAS) is a multimorbid chronic disease with individual and societal deleterious consequences. Polysomnography (PSG) is the multi-parametric reference diagnostic tool that allows a manual quantification of the apnea-hypopnea index (AHI) to assess SAS severity. The burden of SAS is affecting nearly one billion people worldwide explaining that SAS remains largely under-diagnosed and undertreated. The development of an easy to use and automatic solution for early detection and screening of SAS is highly desirable. METHODS: We proposed an Accelerometry-Derived Respiratory index (ADR) solution based on a dual accelerometry system for airflow estimation included in a machine learning process. It calculated the AHI thanks to a RUSBoosted Tree model and used physiological and explanatory specifically developed features. The performances of this method were evaluated against a configuration using gold-standard PSG signals on a database of 28 subjects. RESULTS: The AHI estimation accuracy, specificity and sensitivity of the ADR index were 89%, 100% and 80% respectively. The added value of the specifically developed features was also demonstrated. CONCLUSION: Overnight physiological monitoring with the proposed ADR solution using a machine learning approach provided a clinically relevant estimate of AHI for SAS screening. The physiological component of the solution has a real interest for improving performance and facilitating physician's adhesion to an automatic AHI estimation.

Inductive Plethysmography for aortic Pulse Wave Velocity.

Aortic stiffening is a process that is linked to cardiovascular risk factor increase. Then, aortic stiffness evaluation is considered as a good index of the evolution of pathophysiological situations, including myocardial infarction, heart failure, atherosclerosis or stroke. Today, pulse wave velocity (PWV) measurement is considered as the gold standard for evaluation of arterial stiffness. However, most of the current measurement techniques of PWV consider the time for the blood pressure pulse to propagate through a combined length of arteries and give access to arterial PWV. Therefore, none of the available techniques focuses only on the aorta. In the present context of smart clothes development, Inductive Plethsymography (IP) can be an interesting alternative for aortic PWV measure, since it has recently been shown that combination of thoracic and abdominal IP recordings can give cardiac information. We therefore investigate the potential of IP for aortic PWV measurement. In this preliminary study, a comparative analysis of PWV estimated from IP and PWV evaluated from the arm has been carried out on 11 healthy volunteers. Results show a significant linear correlation between both measures (r = 0.86, p<0.001), promising for future investigations on pathological populations.

Is there an Optimal Localization of Cardio-microphone Sensors for Phonocardiogram Analysis?

Heart auscultation is one of the most useful medical diagnostic tools for getting valuable information of heart valves and heart hemodynamics functions. However, the information acquired by a traditional stethoscope can be inaccurate and insufficient. Phonocardiogram (PCG) was developed to improve accuracy through visual inspection and analysis. Digitally processed, PCG can then be analyzed by automated heart sound analysis systems. But there is no standardization for PCG data acquisition unlike electrocardiogram (ECG). This study aims at analyzing the influence of cardiomicrophone localization on the chest for the study of cardiac sounds S1 and S2. For that purpose, simultaneous acquisitions of 12 PCG signals with one ECG signal were realized and a comparative analysis of delays between R waves of ECG and detected S1 and S2 sounds was conducted. Results show that there are significant differences between R-S1 (or R-S2) intervals obtained from different areas of sensor placement on the chest. For future works on PCG, studies dealing with the analysis of heart sounds or proposing new heart sounds detection algorithms may pay attention to the location and attachment of PCG sensors.

A novel bimodal stethoscope for gastric collection of heart sounds: preliminary results.

Heart sound analysis is commonly used by physicians during auscultations to evaluate cardiac activity as a first line. These sounds originate from heartbeats and the resulting blood flow, and can provide important information about heart function and hemodynamics. The monitoring of heart sounds in patients suffering from chronic cardiac pathologies can be useful to detect or prevent cardiac events. For this purpose, a bimodal implanted gastric stethoscope was developed allowing home monitoring of electrophysiological and mechanical parameters.An in-vivo experiment in pigs was carried out to validate the feasibility of heart sound detection from an accelerometer embedded in a stethoscope prototype implanted in the submucosal layer of the gastric wall. Data recorded over several weeks validate the tolerance and the sensitivity of the device. These promising preliminary results confirm the interest of considering the stomach as a strategic implantation site for heart sound monitoring.

Fetal heart rate estimation from a single phonocardiogram signal using non-negative matrix factorization.

Fetal heart rate (FHR) is an important characteristic in fetal well-being follow-up. It is classically estimated using the cardiotocogram (CTG) non-invasive reference technique. However, this latter presents some significant drawbacks. An alternative non-invasive solution based on the fetal phonocardiogram (fetal PCG) can be used. But most of proposed methods based on the PCG signal need to detect and to label the fetal cardiac S1 and S2 sounds, which may be a difficult task in certain conditions. Therefore, in this paper, we propose a new methodology for FHR estimation from fetal PCG with one single cardio-microphone and without the distinction constraint of heart sounds. The method is based on the non-negative matrix factorization (NMF) applied on the spectrogram of fetal PCG considered as a source-filter model. The proposed method provides satisfactory results on a preliminary dataset of abdominal PCG signals. When compared to the reference CTG, correlation on FHR estimations between PCG and CTG is around 90%.

Adaptive Accelerometry Derived Respiration: Comparison with Respiratory Inductance Plethysmography during Sleep.

Polysomnography (PSG) is a multi-parametric test used in the study of sleep and as a diagnostic tool in sleep medicine. PSG is the gold standard that manually quantifies the apnea-hypopnea index (AHI) to assess the severity of sleep apnea syndrome (SAS). This work presents a novel method based on a dual tri-axis accelerometer system (Adaptive Accelerometry Derived Respiration, ADR) which was patched on the subject's chest that adaptively reconstructed thoracic and abdominal respiratory efforts. Performance evaluation was performed on a 60s-epoch basis using signal and physiological indicators: the evaluation consisted in the comparison of airflow estimations from ADR and RIP to the nasal airflow, considered as reference. Results showed that 74% of the 60s-epoch ADR airflow estimation present a correlation coefficient with nasal airflow ≥ 70% compared to 64% for RIP. Relative errors for one-minute respiration rate and tidal volume estimation appeared to be relatively low which reflected the good feasibility of the adaptive ADR method for respiration monitoring during sleep.

A simple mathematical model of spontaneous swallow effects on breathing based on new experimental data.

The coordination of respiration and swallowing involves an interaction between two central pattern generators, and this can be disturbed in some pathological situations. To better understand this interaction, we aim in this study to characterize the effect of a spontaneous swallow on the breathing pattern. This is first realized using Respiratory Inductive Plethysmography on 11 healthy subjects. Real signals highlight several mechanisms for swallowing: ending of inspiration, prolongation of expiration or active expiration. These behaviours have been integrated in an existing model of the respiratory system simulating the activity of the respiratory centers, the respiratory muscles, and rib cage internal mechanics. The resulting model of interaction between breathing and swallowing is compatible with the observed effects and is driven for swallowing by a limited number of parameters.

Non-invasive cardiac output monitoring in pharmacology: A plethysmographie solution in rats.

Cardiovascular monitoring is of great importance in pharmacology but there is a lack of convenient non-invasive alternatives. Hence, we aim to evaluate the relevance of inductive plethysmography (IP) in preclinical cardiac studies. An IP system was specifically designed for rat. Its evaluation carried out using a mechanical test bench has shown appropriate instrumental performances for cardiac monitoring in rats. Measurements were also performed during a volume overload hemodynamic challenge in vivo in rats. The cardiac output variation has similar kinetic and amplitude when compared to results of previous studies. This suggests that our system is suitable for cardiac output monitoring in rat.

Implementation of a model of bodily fluids regulation.

The classic model of blood pressure regulation by Guyton et al. (Annu Rev Physiol 34:13-46, 1972a; Ann Biomed Eng 1:254-281, 1972b) set a new standard for quantitative exploration of physiological function and led to important new insights, some of which still remain the focus of debate, such as whether the kidney plays the primary role in the genesis of hypertension (Montani et al. in Exp Physiol 24:41-54, 2009a; Exp Physiol 94:382-388, 2009b; Osborn et al. in Exp Physiol 94:389-396, 2009a; Exp Physiol 94:388-389, 2009b). Key to the success of this model was the fact that the authors made the computer code (in FORTRAN) freely available and eventually provided a convivial user interface for exploration of model behavior on early microcomputers (Montani et al. in Int J Bio-med Comput 24:41-54, 1989). Ikeda et al. (Ann Biomed Eng 7:135-166, 1979) developed an offshoot of the Guyton model targeting especially the regulation of body fluids and acid-base balance; their model provides extended renal and respiratory functions and would be a good basis for further extensions. In the interest of providing a simple, useable version of Ikeda et al.'s model and to facilitate further such extensions, we present a practical implementation of the model of Ikeda et al. (Ann Biomed Eng 7:135-166, 1979), using the ODE solver Berkeley Madonna.

Modeling quasi-periodic signals by a non-parametric model: application on fetal ECG extraction.

Quasi-periodic signals can be modeled by their second order statistics as Gaussian process. This work presents a non-parametric method to model such signals. ECG, as a quasi-periodic signal, can also be modeled by such method which can help to extract the fetal ECG from the maternal ECG signal, using a single source abdominal channel. The prior information on the signal shape, and on the maternal and fetal RR interval, helps to better estimate the parameters while applying the Bayesian principles. The values of the parameters of the method, among which the R-peak instants, are accurately estimated using the Metropolis-Hastings algorithm. This estimation provides very precise values for the R-peaks, so that they can be located even between the existing time samples.

Empirical mode decomposition of respiratory inductive plethysmographic signals for stroke volume variations monitoring: respiratory protocol and comparison with impedance cardiography.

We investigate Respiratory Inductive Plethysmography (RIP) to estimate cardiac activity from thoracic volume variations and study cardio-respiratory interactions. The objective of the present study is to evaluate the ability of RIP to monitor stroke volume (SV) variations, with reference to impedance cardiography (IMP). Five healthy volunteers in seated and supine positions were asked to blow into a manometer in order to induce significant SV decreases. Time-scale analysis was applied on calibrated RIP signals to extract cardiac volume signals. Averaged SV values, in quasi-stationary states at rest and during the respiratory maneuvers, were then estimated from these cardiac signals and from IMP signals simultaneously acquired. SV variations between rest and maneuvers were finally evaluated for both techniques. We show that SV values as well as SV variations are correlated between RIP and IMP estimations, suggesting that RIP could be used for SV variations monitoring.

Integration of detailed modules in a core model of body fluid homeostasis and blood pressure regulation.

This paper presents a contribution to the definition of the interfaces required to perform heterogeneous model integration in the context of integrative physiology. A formalization of the model integration problem is proposed and a coupling method is presented. The extension of the classic Guyton model, a multi-organ, integrated systems model of blood pressure regulation, is used as an example of the application of the proposed method. To this end, the Guyton model has been restructured, extensive sensitivity analyses have been performed, and appropriate transformations have been applied to replace a subset of its constituting modules by integrating a pulsatile heart and an updated representation of the renin-angiotensin system. Simulation results of the extended integrated model are presented and the impacts of their integration within the original model are evaluated.

A simple dynamic model of respiratory pump.

To study the interaction of forces that produce chest wall motion, we propose a model based on the lever system of Hillman and Finucane (J Appl Physiol 63(3):951-961, 1987) and introduce some dynamic properties of the respiratory system. The passive elements (rib cage and abdomen) are considered as elastic compartments linked to the open air via a resistive tube, an image of airways. The respiratory muscles (active) force is applied to both compartments. Parameters of the model are identified in using experimental data of airflow signal measured by pneumotachography and rib cage and abdomen signals measured by respiratory inductive plethysmography on eleven healthy volunteers in five conditions: at rest and with four level of added loads. A breath by breath analysis showed, whatever the individual and the condition are, that there are several breaths on which the airflow simulated by our model is well fitted to the airflow measured by pneumotachography as estimated by a determination coefficient R(2) > or = 0.70. This very simple model may well represent the behaviour of the chest wall and thus may be useful to interpret the relative motion of rib cage and abdomen during quiet breathing.