Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination.

Cardiotocography (CTG) is the most common technique for electronic fetal monitoring and consists of the simultaneous recording of fetal heart rate (FHR) and uterine contractions. In analogy with the adult case, spectral analysis of the FHR signal can be used to assess the functionality of the autonomic nervous system. To do so, several methods can be employed, each of which has its strengths and limitations. This paper aims at performing a methodological investigation on FHR spectral analysis adopting 4 different spectrum estimators and a novel PRSA-based spectral method. The performances have been evaluated in terms of the ability of the various methods to detect changes in the FHR in two common pregnancy complications: intrauterine growth restriction (IUGR) and gestational diabetes. A balanced dataset containing 2178 recordings distributed between the 32nd and 38th week of gestation was used. The results show that the spectral method derived from the PRSA better differentiates high-risk pregnancies vs. controls compared to the others. Specifically, it more robustly detects an increase in power percentage within the movement frequency band and a decrease in high frequency between pregnancies at high risk in comparison to those at low risk.

Fetal states identification in cardiotocographic tracings through discrete emissions multivariate hidden Markov models.

BACKGROUND AND OBJECTIVES: Computerized Cardiotocography (cCTG) allows to analyze the Fetal Heart Rate (FHR) objectively and thoroughly, providing valuable insights on fetal condition. A challenging but crucial task in this context is the automatic identification of fetal activity and quiet periods within the tracings. Different neural mechanisms are involved in the regulation of the fetal heart, depending on the behavioral states. Thereby, their correct identification has the potential to increase the interpretability and diagnostic capabilities of FHR quantitative analysis. Moreover, the most common pathologies in pregnancy have been associated with variations in the alternation between quiet and activity states. METHODS: We address the problem of fetal states clustering by means of an unsupervised approach, resorting to the use of a multivariate Hidden Markov Models (HMM) with discrete emissions. A fixed length sliding window is shifted on the CTG traces and a small set of features is extracted at each slide. After an encoding procedure, these features become the emissions of a multivariate HMM in which quiet and activity are the hidden states. After an unsupervised training procedure, the model is used to automatically segment signals. RESULTS: The achieved results indicate that our developed model exhibits a high degree of reliability in identifying quiet and activity states within FHR signals. A set of 35 CTG signals belonging to different pregnancies were independently annotated by an expert gynecologist and segmented using the proposed HMM. To avoid any bias, the physician was blinded to the results provided by the algorithm. The overall agreement between the HMM's predictions and the clinician's interpretations was 90%. CONCLUSIONS: The proposed method reliably identified fetal behavioral states, the alternance of which is an important factor in the fetal development. One key strength of our approach lies in the ease of interpreting the obtained results. By utilizing a small set of parameters that are already used in cCTG and possess clear intrinsic meanings, our method provides a high level of explainability. Another significant advantage of our approach is its fully unsupervised learning process. The states identified by our model using the Baum-Welch algorithm are associated with the "Active" and "Quiet" states only after the clustering process, removing the reliance on expert annotations. By autonomously identifying the clusters based solely on the intrinsic characteristics of the signal, our method achieves a more objective evaluation that overcomes the limitations of subjective interpretations. Indeed, we believe it could be integrated in cCTG systems to obtain a more complete signal analysis.

A Novel Large Structured Cardiotocographic Database.

In this work we present the creation of a large, structured database of CardioTocoGraphic (CTG) recordings, starting from a raw dataset containing tracings collected between 2013 and 2021 by the medical team of the University Hospital Federico II of Naples. The aim of the work is to provide a big, structured database of real clinical cardiotocographic data, useful for subsequent processing and analysis through state-of-the-art methods, in particular Deep Learning Methods. This organized dataset could lead to an increase of the diagnostic accuracy of CTG analysis in the discrimination of healthy and unhealthy fetuses.

A deep learning mixed-data type approach for the classification of FHR signals.

The Cardiotocography (CTG) is a widely diffused monitoring practice, used in Ob-Gyn Clinic to assess the fetal well-being through the analysis of the Fetal Heart Rate (FHR) and the Uterine contraction signals. Due to the complex dynamics regulating the Fetal Heart Rate, a reliable visual interpretation of the signal is almost impossible and results in significant subjective inter and intra-observer variability. Also, the introduction of few parameters obtained from computer analysis did not solve the problem of a robust antenatal diagnosis. Hence, during the last decade, computer aided diagnosis systems, based on artificial intelligence (AI) machine learning techniques have been developed to assist medical decisions. The present work proposes a hybrid approach based on a neural architecture that receives heterogeneous data in input (a set of quantitative parameters and images) for classifying healthy and pathological fetuses. The quantitative regressors, which are known to represent different aspects of the correct development of the fetus, and thus are related to the fetal healthy status, are combined with features implicitly extracted from various representations of the FHR signal (images), in order to improve the classification performance. This is achieved by setting a neural model with two connected branches, consisting respectively of a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN). The neural architecture was trained on a huge and balanced set of clinical data (14.000 CTG tracings, 7000 healthy and 7000 pathological) recorded during ambulatory non stress tests at the University Hospital Federico II, Napoli, Italy. After hyperparameters tuning and training, the neural network proposed has reached an overall accuracy of 80.1%, which is a promising result, as it has been obtained on a huge dataset.

Placental dysfunction in uncomplicated and complicated intrauterine growth restriction by preeclampsia and neonatal outcome.

OBJECTIVES: To investigate the use of computerized cardiotocography (cCTG) parameters in Intrauterine Growth Restriction (IUGR) pregnancies for the prediction of 1) complication with preeclampsia; 2) placental histological abnormalities, and 3) neonatal outcomes. . STUDY DESIGN: A single-center observational retrospective case-control study was performed by reviewing medical records, cCTG databases and pathological reports of women with singleton pregnancy and IUGR uncomplicated (controls) and complicated by preeclampsia (cases). Primary endpoint was the association between cCTG parameters and preeclampsia in IUGR. Secondary endpoints were the association between cCTG parameters and 1) placental abnormalities, and 2) neonatal outcomes. The one-way ANOVA test was used to compare cCTG parameters in cases and controls. t-test was applied to compare neonatal outcomes and placental abnormalities in both groups. The Spearman Test value Correlation coefficients between the cCTG parameters and neonatal outcome in the two groups. A p value < .05 was considered significant for all analyses. RESULTS: Among all cCTG parameters, a significant association with preeclampsia in IUGR was found for Fetal Heart Rate (FHR, p = .008), Delta (p = .018), Short Term Variability (STV, p = .021), Long Term Variability (LTV, p = .028), Acceleration Phase Rectified Slope (APRS, p = .018) and Deceleration Phase Rectified Slope (DPRS, p = .038). Of all placental histologic abnormalities, only vascular alterations at least moderate were significantly associated with increased FHR (p = .02). About neonatal outcomes, all cCTG parameters were significantly associated with birth weight, Apgar index at 1 and 5 min, pH and pCO2. FHR, LTI, Delta, Approximate Entropy (ApEn) and LF were significantly associated with pO2; LTI, Interval Index (II) and ApEn with base excess. Among controls, Delta, ApEn, Low Frequency (LF) and High Frequency (HF) were significantly associated with pCO2, while among cases, STV and Delta were significantly associated with pH; STV, LTI, Delta, ApEn, LF and HF with pCO2; STV, LTI, Delta and ApEn with pO2; HF with base excess; FHR and LF with lactates. CONCLUSIONS: cCTG parameters may be useful to detect complication with preeclampsia in IUGR pregnancies. Regarding placental status, cCTG parameters may detect overall circulation alterations, but not specific histological abnormalities. Lastly, all cCTG parameters may predict neonatal outcomes, helping to tailor the patients' management.

An integrated approach based on advanced CTG parameters and Doppler measurements for late growth restriction management.

BACKGROUND: The clinical diagnosis of late Fetal Growth Restriction (FGR) involves the integration of Doppler ultrasound data and Fetal Heart Rate (FHR) monitoring through computer assisted computerized cardiotocography (cCTG). The aim of the study was to evaluate the diagnostic power of combined Doppler and cCTG parameters by contrasting late FGR -and healthy controls. METHODS: The study was conducted from January 2018 to May 2020. Only pregnant women who had the last Doppler measurement obtained within 1 week before delivery and cCTG performed within 24 h before delivery were included in the study. Two hundred forty-nine pregnant women fulfilling the inclusion criteria were enrolled in the study; 95 were confirmed as late FGR and 154 were included in the control group. RESULTS: Among the extracted cCTG parameters, Delta Index, Short Term Variability (STV), Long Term Variability (LTV), Acceleration and Deceleration Phase Rectified Slope (APRS, DPRS) values were lower in the late FGR participants compared to the control group. In the FGR cohort, Delta, STV, APRS, and DPRS were found different when stratifying by MCA_PI (MCA_PI <5th centile or > 5th centile). STV and DPRS were the only parameters to be found different when stratifying by (UA_PI >95th centile or UA_PI <95th centile). Additionally, we measured the predictive power of cCTG parameters toward the identification of associated Doppler measures using figures of merit extracted from ROC curves. The AUC of ROC curves were accurate for STV (0,70), Delta (0,68), APRS (0,65) and DPRS (0,71) when UA_PI values were > 95th centile while, the accuracy attributable to the prediction of MCA_PI was 0.76, 0.77, 0.73, and 0.76 for STV, Delta, APRS, and DPRS, respectively. An association of UA_PI>95th centile and MCA_PI<5th centile with higher risk for NICU admission, was observed, while CPR < 5th centile resulted not associated with any perinatal outcome. Values of STV, Delta, APRS, DPRS were significantly lower for FGR neonates admitted to NICU, compared with the uncomplicated FGR cohort. CONCLUSIONS: The results of this study show the contribution of advanced cCTG parameters and fetal Doppler to the identification of late FGR and the association of those parameters with the risk for NICU admission. TRIAL REGISTRATION: Retrospectively registered.

A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction.

Late intrauterine growth restriction (IUGR) is a fetal pathological condition characterized by chronic hypoxia secondary to placental insufficiency, resulting in an abnormal rate of fetal growth. This pathology has been associated with increased fetal and neonatal morbidity and mortality. In standard clinical practice, late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This study presents a radial basis function support vector machine (RBF-SVM) classification based on quantitative features extracted from fetal heart rate (FHR) signals acquired using routine cardiotocography (CTG) in a population of 160 healthy and 102 late IUGR fetuses. First, the individual performance of each time, frequency, and nonlinear feature was tested. To improve the unsatisfactory results of univariate analysis we firstly adopted a Recursive Feature Elimination approach to select the best subset of FHR-based parameters contributing to the discrimination of healthy vs. late IUGR fetuses. A fine tuning of the RBF-SVM model parameters resulted in a satisfactory classification performance in the training set (accuracy 0.93, sensitivity 0.93, specificity 0.84). Comparable results were obtained when applying the model on a totally independent testing set. This investigation supports the use of a multivariate approach for the in utero identification of late IUGR condition based on quantitative FHR features encompassing different domains. The proposed model allows describing the relationships among features beyond the traditional linear approaches, thus improving the classification performance. This framework has the potential to be proposed as a screening tool for the identification of late IUGR fetuses.

Combining Millimeter-Wave Imaging, Ultrasound and Elastography in a New Multimodal Approach for Breast Cancer Detection: Initial Experimental Results.

In this paper, for the first time, a triple-mode scan using electromagnetic waves, in the shape of millimeter waves, and ultrasound waves, to obtain B-mode and quasistatic elastography images of a phantom of human breast tissues is shown. A homogeneous phantom composed of nontoxic, low-cost and easy-to-handle materials (i.e. water, oil, gelatin and dishwashing liquid) was produced, with an inclusion made of water and agar. These are intended to mimic, in terms of dielectric properties, healthy adipose tissues and neoplastic tissues, respectively. A millimeter-wave imaging prototype was used to scan the phantom, by implementing a linear synthetic array of 24 antennas with a central working frequency of 30 GHz. The phantom was then scanned using an ultrasound research system and a linear-array probe at 7 MHz, acquiring both B-mode and quasi-static elastography images. The millimeter-wave system showed an excellent ability to detect the target placed at about 1.4 cm depth. Also in the ultrasound case the inclusion was correctly detected as a hypoechoic, stiff mass. This first experimental findings show that millimeter-wave, ultrasound and elasticity imaging can be used jointly to detect tumor-like targets into phantoms mimicking healthy breast tissues. Thus, they provide promising preliminary results to further study the application of this multimodal approach in all those critical cases in which such complementary imaging techniques could be exploited for an enhanced tumor detection, based on tissues dielectric, acoustic and elastic properties.

A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features.

Among dementia-like diseases, Alzheimer disease (AD) and vascular dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve the diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study, we investigated, first, whether different kinds of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD and, second, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and diffusion tensor imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a "mixed VD-AD dementia" (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a 3-year clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD, reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature data set (e.g., DTI + rs-fMRI metrics) rather than a unimodal feature data set. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach has a high discriminant power to classify AD and VD profiles. Moreover, the same approach also showed potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians' diagnostic evaluations.

Dataset on linear and non-linear indices for discriminating healthy and IUGR fetuses.

The presented collection of data comprises of a set of 12 linear and nonlinear indices computed at different time scales and extracted from Fetal Heart Rate (FHR) traces acquired through Hewlett Packard CTG fetal monitors (series 1351A), connected to a PC. The sampling frequency of the recorded FHR signal is equal 2 Hz. The recorded populations consist of two groups of fetuses: 60 healthy and 60 Intra Uterine Growth Restricted (IUGR) fetuses. IUGR condition is a fetal condition defined as the abnormal rate of fetal growth. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. The pathology is a documented cause of fetal and neonatal morbidity and mortality. The described database was employed in a set of machine learning approaches for the early detection of the IUGR condition: "Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring" [1]. The added value of the proposed indices is their interpretability and close connection to physiological and pathological aspect of FHR regulation. Additional information on data acquisition, feature extraction and potential relevance in clinical practice are discussed in [1].

Impact of nuchal cord on antenatal and intrapartum foetal heart rate surveillance and perinatal outcome.

Analysing antepartum and intrapartum computerised cardiotocographic (cCTG) parameters in physiological term pregnancies with nuchal (NC) or body cord (BC), in order to correlate them with labour events and neonatal outcome. We enrolled 808 pregnant women, composed of 264 with 'one NC', 121 with 'multiple NCs', 39 with BC and 384 with 'no NC', were monitored from the 37th week of gestation before labour, while 49 pregnant women with 'one or more NCs' and 47 with 'no NCs' were analysed during labour. No differences in maternal characteristics, foetal pH at birth and 5-min Apgar score were observed. The birth weight was significantly lower in the 'multiple NCs' group, while 1-minute Apgar score was lower in the BC group than the other groups, respectively. No relevant differences in cCTG parameters were observed, except for LTI, Delta and number of variable decelerations in antepartum period and only variable deceleration in intrapartum period.Impact statementWhat is already known on this subject? Ultrasound cannot predict which foetuses with NCs are likely to have problem during labour. The question arose if single or multiple NC could affects FHR monitoring prior and during labour.What do the results of this study add? Computerised cardiotocography (cCTG) is a standardised method developed to reduce inter- and intra-observer variability and the poor reproducibility of visual analysis. Few studies have investigated the influence of NCs on FHR variability and, to our knowledge, no one has evaluated its linear and nonlinear characteristics in antepartum and intrapartum period using a computerised analysis system. No differences in maternal characteristics, foetal pH at birth and 5-min Apgar score were observed. Birth weight was significantly lower in the 'multiple NCs' group, while 1-min Apgar score was lower in the BC group than the other groups, respectively. Foetuses with 'one or more NCs' evidenced a larger number of prolonged second stage and meconium-stained liquor cases, while the operative vaginal delivery and emergency caesarean section rates were unchanged. No relevant differences in cCTG parameters were observed, except for LTI, Delta and number of variable decelerations in antepartum period and only variable deceleration in intrapartum period.What are the implications of these findings for clinical practice and/or further research? cCTG monitoring results confirmed their usefulness for assessing the state of good oxygenation for all foetuses investigated.

Intrapartum cardiotocography with and without computer analysis: a systematic review and meta-analysis of randomized controlled trials.

Objective: To evaluate whether intrapartum cardiotocography with computer analysis decreases the incidence of newborn metabolic acidosis or obstetric intervention when compared with visual analysis through a systematic review with meta-analysis of randomized controlled trials.Methods: The research was conducted using Medline, Embase, Web of Science, Scopus, ClinicalTrial.gov, Ovid and Cochrane Library as electronic databases from the inception of each database to May 2018. Selection criteria included randomized trial evaluating women with cephalic presentation at term or late preterm term during labor who were randomized to electronic fetal heart rate monitoring with either computer analysis (i.e. intervention group) or standard visual analysis (i.e. control group). Trials evaluating antenatal fetal heart rate monitoring in women not in labor were excluded. The primary outcome was incidence of newborn metabolic acidosis, defined as pH less than 7.05 and base deficit greater than 12 mmol/L. Secondary outcomes were mode of delivery, admission to neonatal intensive care unit, hypoxic-ischemic encephalopathy, and perinatal death. The summary measures were reported as relative risk (RR) with 95% confidence interval (CI).Results: Three randomized controlled trials (RCTs), including 54,492 participants, which met inclusion criteria for this meta-analysis, were analyzed. All the included trials enrolled women with cephalic presentation at term or late preterm. Women were randomized in the active first stage of labor and all of them received continuous cardiotocography (CTG) from randomization until delivery. Women who received continuous CTG during labor with computerized analysis had similar risk of newborn metabolic acidosis. No between group differences were found in the secondary outcomes.Conclusions: Compared with visual analysis, use of computer analysis of fetal monitoring signals during labor did not significantly reduce the rate of metabolic acidosis or obstetric intervention.

A Comparison of Coherence-Based Beamforming Techniques in High-Frame-Rate Ultrasound Imaging With Multi-Line Transmission.

One of the current challenges in ultrasound imaging is achieving higher frame rates, particularly in cardiac applications, where tracking the heart motion and other rapid events can provide potential valuable diagnostic information. The main drawback of ultrasound high-frame-rate strategies is that usually they partly sacrifice image quality in order to speed up the acquisition time. In particular, multi-line transmission (MLT), which consists in transmitting multiple ultrasound beams simultaneously in different directions, has been proven able to improve frame rates in echocardiography, unfortunately generating artifacts due to inter-beam crosstalk interferences. This work investigates the possibility to effectively suppress crosstalk artifacts in MLT while improving image quality by applying beamforming techniques based on backscattered signals spatial coherence. Several coherence-based algorithms (i.e., short-lag filtered-delay multiply and sum beamforming, coherence and generalized coherence factor, phase and sign coherence, and nonlinear beamforming with p th root compression) are implemented and compared, and their performance trends are evaluated when varying their design parameters. Indeed, experimental results of phantom and in vivo cardiac acquisitions demonstrate that this class of algorithms can provide significant benefits compared with delay and sum, well-suppressing artifacts (up to 48.5-dB lower crosstalk), and increasing image resolution (by up to 46.3%) and contrast (by up to 30 dB in terms of contrast ratio and 12.6% for generalized contrast-to-noise ratio) at the same time.

Integrating machine learning techniques and physiology based heart rate features for antepartum fetal monitoring.

BACKGROUND AND OBJECTIVES: Intrauterine Growth Restriction (IUGR) is a fetal condition defined as the abnormal rate of fetal growth. The pathology is a documented cause of fetal and neonatal morbidity and mortality. In clinical practice, diagnosis is confirmed at birth and may only be suspected during pregnancy. Therefore, designing an accurate model for the early and prompt identification of pathology in the antepartum period is crucial in view of pregnancy management. METHODS: We tested the performance of 15 machine learning techniques in discriminating healthy versus IUGR fetuses. The various models were trained with a set of 12 physiology based heart rate features extracted from a single antepartum CardioTocographic (CTG) recording. The reason for the utilization of time, frequency, and nonlinear indices is based on their standalone documented ability to describe several physiological and pathological fetal conditions. RESULTS: We validated our approach on a database of 60 healthy and 60 IUGR fetuses. The machine learning methodology achieving the best performance was Random Forests. Specifically, we obtained a mean classification accuracy of 0.911 [0.860, 0.961 (0.95 confidence interval)] averaged over 10 test sets (10 Fold Cross Validation). Similar results were provided by Classification Trees, Logistic Regression, and Support Vector Machines. A features ranking procedure highlighted that nonlinear indices showed the highest capability to discriminate between the considered fetal conditions. Nevertheless, is the combination of features investigating CTG signal in different domains, that contributes to an increase in classification accuracy. CONCLUSIONS: We provided validation of an accurate artificially intelligence framework for the diagnosis of IUGR condition in the antepartum period. The employed physiology based heart rate features constitute an interpretable link between the machine learning results and the quantitative estimators of fetal wellbeing.

Fetal heart rate monitoring and neonatal outcome in a population of early- and late-onset intrauterine growth restriction.

AIM: The early-onset intrauterine growth restriction (IUGR) is associated with severe placental insufficiency and Doppler abnormalities. The late-onset IUGR is associated with mild placental insufficiency and normal Doppler velocimetry. The computerized cardiotocographic (cCTG) monitoring is used to evaluate the fetal well-being in pregnancies complicated by IUGR. Our aim was to investigate the cardiotocographic characteristics of IUGR fetuses and to identify every cCTG difference between Healthy and IUGR fetuses. METHODS: Four hundred thirty pregnant women were enrolled starting from the 28th week of gestation until the time of delivery: 200 healthy and 230 IUGR fetuses. Fetal heart rate (FHR) baseline (FHR), short-term variability (STV), long-term irregularity (LTI), delta, interval index (II), approximate entropy (ApEn), high frequency (HF), low frequency (LF), movement frequency (MF), LF/(HF + MF) ratio (LF/(HF + MF)) and number of decelerations were examined. Newborn baby data were also collected. RESULTS: The parameters of short- and medium-term variability discriminate between IUGR and healthy fetuses before 36 weeks including FHR, STV, LTI and delta discriminate between each subgroup of IUGR were compared to each one of the other two (P < 0.05). CONCLUSION: cCTG is a useful tool for the evaluation of chronic hypoxemia, which causes a delay in the maturation of all components of the autonomic and central nervous system. However, cCTG requires integration with fetal ultrasound and Doppler vessels evaluation to improve the ability to predict the neonatal outcome.

A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography.

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.

An Efficient Algorithm for the Extraction of Fetal ECG from Standard and Non-Standard Multi Abdominal Maternal Leads.

The importance of fetal surveillance during pregnancy is worldwide accepted since its peculiar ability to anticipate fetal distress under a variety of conditions. The novel frontier in the field of remote fetal monitoring relies on a continuous and everyday-monitoring of fetal wellbeing. As a consequence, fECG monitoring systems have seen a net increase in popularity in the recent years. In this paper, we propose a novel algorithm for the detection of fECG and we validated its performances by testing it on an open source collection of 75 annotated fECG traces. Our results show the reliability of the proposed methodology in extracting fECG and deriving an estimate of fHR.

Specific Patterns of White Matter Alterations Help Distinguishing Alzheimer's and Vascular Dementia.

Alzheimer disease (AD) and vascular dementia (VaD) together represent the majority of dementia cases. Since their neuropsychological profiles often overlap and white matter lesions are observed in elderly subjects including AD, differentiating between VaD and AD can be difficult. Characterization of these different forms of dementia would benefit by identification of quantitative imaging biomarkers specifically sensitive to AD or VaD. Parameters of microstructural abnormalities derived from diffusion tensor imaging (DTI) have been reported to be helpful in differentiating between dementias, but only few studies have used them to compare AD and VaD with a voxelwise approach. Therefore, in this study a whole brain statistical analysis was performed on DTI data of 93 subjects (31 AD, 27 VaD, and 35 healthy controls-HC) to identify specific white matter patterns of alteration in patients affected by VaD and AD with respect to HC. Parahippocampal tracts were found to be mainly affected in AD, while VaD showed more spread white matter damages associated with thalamic radiations involvement. The genu of the corpus callosum was predominantly affected in VaD, while the splenium was predominantly affected in AD revealing the existence of specific patterns of alteration useful in distinguishing between VaD and AD. Therefore, DTI parameters of these regions could be informative to understand the pathogenesis and support the etiological diagnosis of dementia. Further studies on larger cohorts of subjects, characterized for brain amyloidosis, will allow to confirm and to integrate the present findings and, furthermore, to elucidate the mechanisms of mixed dementia. These steps will be essential to translate these advances to clinical practice.

Experimental evaluation of ultrasound higher-order harmonic imaging with Filtered-Delay Multiply And Sum (F-DMAS) non-linear beamforming.

Tissue Harmonic Imaging (THI) mode is currently one of the preferred choices by the clinicians for its ability to provide enhanced ultrasound images, thanks to the use of the second harmonic component of backscattered echoes. This paper aims at investigating whether the combination of THI with Filtered-Delay Multiply And Sum (F-DMAS) beamforming can provide further improvements in image quality. F-DMAS is a new non-linear beamformer, which, similarly to THI, is based on the use of the second harmonics of beamformed signals and is known to increase image contrast resolution and noise rejection. Thus, we have first compared the images obtained by using F-DMAS and the standard Delay And Sum (DAS) beamformers when only the second harmonics of the received signals was selected. Moreover, possible improvements brought about by other harmonic components generated by the combined use of the fundamental plus second harmonics and F-DMAS beamforming have been explored. Experimental results demonstrate that, as compared to standard harmonic imaging with DAS, THI and F-DMAS can be joined to improve the -20 dB lateral resolution up to 1 mm, the contrast ratio up to 12 dB on a cyst-phantom and up to 9 dB on in vivo images.