Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.

Proof of concept: Screening for REM sleep behaviour disorder with a minimal set of sensors.

OBJECTIVE: Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson's disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors. METHODS: Polysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels. RESULTS: The EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10. CONCLUSIONS: This study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques. SIGNIFICANCE: This study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.

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.

Detection of REM sleep behaviour disorder by automated polysomnography analysis.

OBJECTIVE: Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. METHODS: Analysis was assessed using a limited polysomnography montage from 53 participants with RBD and 53 age-matched healthy controls. Sleep stage classification was achieved using a Random Forest (RF) classifier and 156 features extracted from electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) channels. For RBD detection, a RF classifier was trained combining established techniques to quantify muscle atonia with additional features that incorporate sleep architecture and the EMG fractal exponent. RESULTS: Automated multi-state sleep staging achieved a 0.62 Cohen's Kappa score. RBD detection accuracy improved from 86% to 96% (compared to individual established metrics) when using manually annotated sleep staging. Accuracy remained high (92%) when using automated sleep staging. CONCLUSIONS: This study outperforms established metrics and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection. This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation. SIGNIFICANCE: This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology.

SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging.

Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time. In this paper, we tackle the task as a sequence-to-sequence classification problem that receives a sequence of multiple epochs as input and classifies all of their labels at once. For this purpose, we propose a hierarchical recurrent neural network named SeqSleepNet (source code is available at http://github.com/pquochuy/SeqSleepNet). At the epoch processing level, the network consists of a filterbank layer tailored to learn frequency-domain filters for preprocessing and an attention-based recurrent layer designed for short-term sequential modeling. At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modeling of sequential epochs. The classification is then carried out on the output vectors at every time step of the top recurrent layer to produce the sequence of output labels. Despite being hierarchical, we present a strategy to train the network in an end-to-end fashion. We show that the proposed network outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen's kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data.

As the collection of mobile health data becomes pervasive, missing data can make large portions of datasets inaccessible for analysis. Missing data has shown particularly problematic for remotely diagnosing and monitoring Parkinson's disease (PD) using smartphones. This contribution presents multi-source ensemble learning, a methodology which combines dataset deconstruction with ensemble learning and enables participants with incomplete data (i.e., where not all sensor data is available) to be included in the training of machine learning models and achieves a 100% participant retention rate. We demonstrate the proposed method on a cohort of 1513 participants, 91.2% of which contributed incomplete data in tapping, gait, voice, and/or memory tests. The use of multi-source ensemble learning, alongside convolutional neural networks (CNNs) capitalizing on the amount of available data, increases PD classification accuracy from 73.1% to 82.0% as compared to traditional techniques. The increase in accuracy is found to be partly caused by the use of multi-channel CNNs and partly caused by developing models using the large cohort of participants. Furthermore, through bootstrap sampling we reveal that feature selection is better performed on a large cohort of participants with incomplete data than on a small number of participants with complete data. The proposed method is applicable to a wide range of wearable/remote monitoring datasets that suffer from missing data and contributes to improving the ability to remotely monitor PD via revealing novel methods of accounting for symptom heterogeneity.

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification.

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs' labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

Multichannel Sleep Stage Classification and Transfer Learning using Convolutional Neural Networks.

Current sleep medicine relies on the supervised analysis of polysomnographic measurements, comprising amongst others electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals. Convolutional neural networks (CNN) provide an interesting framework to automated classification of sleep based on these raw waveforms. In this study, we compare existing CNN approaches to four databases of pathological and physiological subjects. The best performing model resulted in Cohen's Kappa of $\kappa = 0 .75$ on healthy subjects and $\kappa = 0 .64$ on patients suffering from a variety of sleep disorders. Further, we show the advantages of additional sensor data (i.e., EOG and EMG). Deep learning approaches require a lot of data which is scarce for less prevalent diseases. For this, we propose a transfer learning procedure by pretraining a model on large public data and fine-tune this on each subject from a smaller dataset. This procedure is demonstrated using a private REM Behaviour Disorder database, improving sleep classification by 24.4%.

DNN Filter Bank Improves 1-Max Pooling CNN for Single-Channel EEG Automatic Sleep Stage Classification.

We present in this paper an efficient convolutional neural network (CNN) running on time-frequency image features for automatic sleep stage classification. Opposing to deep architectures which have been used for the task, the proposed CNN is much simpler However, the CNN's convolutional layer is able to support convolutional kernels with different sizes, and therefore, capable of learning features at multiple temporal resolutions. In addition, the 1-max pooling strategy is employed at the pooling layer to better capture the shift-invariance property of EEG signals. We further propose a method to discriminatively learn a frequency-domain filter bank with a deep neural network (DNN) to preprocess the time-frequency image features. Our experiments show that the proposed 1-max pooling CNN performs comparably with the very deep CNNs in the literature on the Sleep- EDF dataset. Preprocessing the time-frequency image features with the learned filter bank before presenting them to the CNN leads to significant improvements on the classification accuracy, setting the state- of-the-art performance on the dataset.

Automatic Sleep Stage Classification Using Single-Channel EEG: Learning Sequential Features with Attention-Based Recurrent Neural Networks.

We propose in this work a feature learning approach using deep bidirectional recurrent neural networks (RNNs) with attention mechanism for single-channel automatic sleep stage classification. We firstly decompose an EEG epoch into multiple small frames and subsequently transform them into a sequence of frame-wise feature vectors. Given the training sequences, the attention-based RNN is trained in a sequence-to-label fashion for sleep stage classification. Due to discriminative training, the network is expected to encode information of an input sequence into a high-level feature vector after the attention layer. We, therefore, treat the trained network as a feature extractor and extract these feature vectors for classification which is accomplished by a linear SVM classifier. We also propose a discriminative method to learn a filter bank with a DNN for preprocessing purpose. Filtering the frame-wise feature vectors with the learned filter bank beforehand leads to further improvement on the classification performance. The proposed approach demonstrates good performance on the Sleep-EDF dataset.

Automating the Detection of REM Sleep Behaviour Disorder.

This study aims to develop automated diagnostic tools to aid in the identification of rapid-eye-movement (REM) sleep behaviour disorder (RBD). Those diagnosed with RBD enact their dreams and therefore present an abnormal characteristic of movement during REM sleep. Several methods have been proposed for RBD detection that use electromyogram (EMG) recordings and manually annotated sleep stages to objectively quantify abnormal REM movement. In this work we further develop these proven techniques with additional features that incorporate the relationship of muscle movement between sleep stages and general sleep architecture. Performance is evaluated using polysomnography (PSG) recordings from 43 aged-matched healthy controls and subjects diagnosed with RBD obtained from multiple institutions and publicly available resources. Using a random forest classifier with established and additional features, the performance of RBD detection was shown to improve upon established metrics (achieving 88% accuracy, 91% sensitivity, and 86% specificity).

The use of non-invasive fetal electrocardiography in diagnosing second-degree fetal atrioventricular block.

BACKGROUND: Complete atrioventricular block in fetuses is known to be mostly associated with autoimmune disease and can be irreversible if no steroids treatment is provided. Conventional methods used in clinical practice for diagnosing fetal arrhythmia are limited since they do not reflect the primary electrophysiological conduction processes that take place in the myocardium. The non-invasive fetal electrocardiogram has the potential to better support fetal arrhythmias diagnosis through the continuous analysis of the beat to beat variation of the fetal heart rate and morphological analysis of the PQRST complex. CASE PRESENTATION: We present two retrospective case reports on which atrioventricular block diagnosis could have been supported by the non-invasive fetal electrocardiogram. The two cases comprised a 22-year-old pregnant woman with the gestational age of 31 weeks and a 25-year-old pregnant woman with the gestational age of 41 weeks. Both women were admitted to the Department of Maternal and Fetal Medicine at the Kyiv and Kharkiv municipal perinatal clinics. Patients were observed using standard fetal monitoring methods as well as the non-invasive fetal electrocardiogram. The non-invasive fetal electrocardiographic recordings were analyzed retrospectively, where it is possible to identify the presence of the atrioventricular block. CONCLUSIONS: This study demonstrates, for the first time, the feasibility of the non-invasive fetal electrocardiogram as a supplementary method to diagnose of the fetal atrioventricular block. Combined with current fetal monitoring techniques, non-invasive fetal electrocardiography could support clinical decisions.

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.

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.

An open-source framework for stress-testing non-invasive foetal ECG extraction algorithms.

Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed. This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e.g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio). The overall median FQRS detection accuracies (i.e. considering all non-stationary events) for the best performing methods in each group were 99.9% for BSS, 97.9% for AM and 96.0% for TS. Both FQRS detections and morphological parameters were shown to heavily depend on the extraction techniques and signal-to-noise ratio. Particularly, it is shown that their evaluation in the source domain, obtained after using a BSS technique, should be avoided. Data, extraction algorithms and evaluation routines were released as part of the fecgsyn toolbox on Physionet under an GNU GPL open-source license. This contribution provides a standard framework for benchmarking and regulatory testing of NI-FECG extraction algorithms.

A practical guide to non-invasive foetal electrocardiogram extraction and analysis.

Non-Invasive foetal electrocardiography (NI-FECG) represents an alternative foetal monitoring technique to traditional Doppler ultrasound approaches, that is non-invasive and has the potential to provide additional clinical information. However, despite the significant advances in the field of adult ECG signal processing over the past decades, the analysis of NI-FECG remains challenging and largely unexplored. This is mainly due to the relatively low signal-to-noise ratio of the FECG compared to the maternal ECG, which overlaps in both time and frequency. This article is intended to be used by researchers as a practical guide to NI-FECG signal processing, in the context of the above issues. It reviews recent advances in NI-FECG research including: publicly available databases, NI-FECG extraction techniques for foetal heart rate evaluation and morphological analysis, NI-FECG simulators and the methodology and statistics for assessing the performance of the extraction algorithms. Reference to the most recent work is given, recent findings are highlighted in the form of intermediate summaries, references to open source code and publicly available databases are provided and promising directions for future research are motivated. In particular we emphasise the need and specifications for building a new open reference database of NI-FECG signals, and the need for new algorithms to be benchmarked on the same database, employing the same evaluation statistics. Finally we motivate the need for research in NI-FECG to address morphological analysis, since this represent one of the most promising avenues for this foetal monitoring modality.

Multimodal heart beat detection using signal quality indices.

The electrocardiogram (ECG) is a well studied signal from which many clinically relevant parameters can be derived, such as heart rate. A key component in the estimation of these parameters is the accurate detection of the R peak in the QRS complex. While corruption of the ECG by movement artefact or sensor failure can result in poor delineation of the R peak, use of synchronously measured signals could allow for resolution of the R peak even scenarios with poor quality ECG recordings. Robust estimation of R peak locations from multimodal signals facilitates real time monitoring and is likely to reduce false alarms due to inaccurate derived parameters.We propose a method which fuses R peaks detected on the ECG using an energy detector with those detected on the arterial blood pressure (ABP) waveform using the length transform. A signal quality index (SQI) for the two signals is then derived. The ECG SQI is based upon the agreement between two distinct peak detectors. The ABP SQI estimates the blood pressure at various phases in the cardiac cycle and only accepts the signal as good quality if the values are physiologically plausible. Detections from these two signals were merged by selecting the R peak detections from the signal with a higher SQI. The approach presented in this paper was evaluated on datasets provided for the Physionet/Computing in Cardiology Challenge 2014. The algorithm achieved a sensitivity of 95.1% and positive predictive value of 89.3% on an external evaluation set, and achieved a score of 91.5%.The method here demonstrated excellent performance across a variety of signal morphologies collected during clinical practice. Fusion of R peaks from other signals has the potential to provide informed estimates of the R peak location in situations where the ECG is noisy or completely absent. Source code for the algorithm is made available freely online.

An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings.

Accurate foetal electrocardiogram (FECG) morphology extraction from non-invasive sensors remains an open problem. This is partly due to the paucity of available public databases. Even when gold standard information (i.e derived from the scalp electrode) is present, the collection of FECG can be problematic, particularly during stressful or clinically important events.In order to address this problem we have introduced an FECG simulator based on earlier work on foetal and adult ECG modelling. The open source foetal ECG synthetic simulator, fecgsyn, is able to generate maternal-foetal ECG mixtures with realistic amplitudes, morphology, beat-to-beat variability, heart rate changes and noise. Positional (rotation and translation-related) movements in the foetal and maternal heart due to respiration, foetal activity and uterine contractions were also added to the simulator.The simulator was used to generate some of the signals that were part of the 2013 PhysioNet Computing in Cardiology Challenge dataset and has been posted on Physionet.org (together with scripts to generate realistic scenarios) under an open source license. The toolbox enables further research in the field and provides part of a standard for industry and regulatory testing of rare pathological scenarios.

Robust fetal ECG extraction and detection from abdominal leads.

The fetal ECG derived from abdominal leads provides an alternative to standard means of fetal monitoring. Furthermore, it permits long-term and ambulant recordings, which expands the range diagnostic possibilities for evaluating the fetal health state. However, due to the temporal and spectral overlap of maternal and fetal signals, the usage of abdominal leads imposes the need for elaborated signal processing routines.In this work a modular combination of processing techniques is presented. Its core consists of two maternal ECG estimation techniques, namely the extended Kalman smoother (EKS) and template adaption (TA) in combination with an innovative detection algorithm. Our detection method employs principles of evolutionary computing to detect fetal peaks by considering the periodicity and morphological characteristics of the fetal signal. In a postprocessing phase, single channel detections are combined by means of kernel density estimation and heart rate correction.The described methodology was presented during the Computing in Cardiology Challenge 2013. The entry was the winner of the closed-source events with average scores for events 4/5 with 15.1/3.32 (TA) and 69.5/4.58 (EKS) on training set-A and 20.4/4.57 (TA) and 219/7.69 (EKS) on test set-B, respectively. Using our own clinical data (24 subjects each 20 min recordings) and statistical measures beyond the Challenge's scoring system, we further validated the proposed method. For our clinical data we obtained an average detection rate of 82.8% (TA) and 83.4% (EKS). The achieved results show that the proposed methods are able produce reliable fetal heart rate estimates from a restricted number of abdominal leads.