Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Two dimensional electrophysiological characterization of human pluripotent stem cell-derived cardiomyocyte system.

Stem cell-derived cardiomyocytes provide a promising tool for human developmental biology, regenerative therapies, disease modeling, and drug discovery. As human pluripotent stem cell-derived cardiomyocytes remain functionally fetal-type, close monitoring of electrophysiological maturation is critical for their further application to biology and translation. However, to date, electrophysiological analyses of stem cell-derived cardiomyocytes has largely been limited by biologically undefined factors including 3D nature of embryoid body, sera from animals, and the feeder cells isolated from mouse. Large variability in the aforementioned systems leads to uncontrollable and irreproducible results, making conclusive studies difficult. In this report, a chemically-defined differentiation regimen and a monolayer cell culture technique was combined with multielectrode arrays for accurate, real-time, and flexible measurement of electrophysiological parameters in translation-ready human cardiomyocytes. Consistent with their natural counterpart, amplitude and dV/dtmax of field potential progressively increased during the course of maturation. Monolayer culture allowed for the identification of pacemaking cells using the multielectrode array platform and thereby the estimation of conduction velocity, which gradually increased during the differentiation of cardiomyocytes. Thus, the electrophysiological maturation of the human pluripotent stem cell-derived cardiomyocytes in our system recapitulates in vivo development. This system provides a versatile biological tool to analyze human heart development, disease mechanisms, and the efficacy/toxicity of chemicals

Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability of being undetected. In this work, we present an attention based deep learning framework for detection of paroxysmal AF episodes from a sequence of windows. Time-frequency representation of 30 seconds recording windows, over a 10 minute data segment, are fed sequentially into a deep convolutional neural network for image-based feature extraction, which are then presented to a bidirectional recurrent neural network with an attention layer for AF detection. To demonstrate the effectiveness of the proposed framework for transient AF detection, we use a database of 24 hour Holter Electrocardiogram (ECG) recordings acquired from 2850 patients at the University of Virginia heart station. The algorithm achieves an AUC of 0.94 on the testing set, which exceeds the performance of baseline models. We also demonstrate the cross-domain generalizablity of the approach by adapting the learned model parameters from one recording modality (ECG) to another (photoplethysmogram) with improved AF detection performance. The proposed high accuracy, low false alarm algorithm for detecting paroxysmal AF has potential applications in long-term monitoring using wearable sensors.Comment: Accepted to the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK, 201

A Comparison of Patient History- and EKG-based Cardiac Risk Scores.

Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history - age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient's raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Pre-Hospital Management of Acute Myocardial Infarction Using Tele-Electrocardiography System

A comprehensive survey revealed that many patients of Acute Myocardial Infarction reach to the hospital so late to deliver an effective treatment. This leads to poor treatment outcome which increases the mortality rates. Multiple reasons such as lack of diagnostic facilities in the rural health care centers and absence of cardiologists in Emergency Rooms inhibit the timely treatment. In this study, we aimed to develop an effective system to pre-hospital management of Acute Myocardial Infarction. The effectiveness of early thrombolysis in Myocardial Infarction is well established, particularly with regard to its positive effect on reducing the mortality rates. Our proposed system by using tele-electrocardiography technology enables cardiologist to analyze the patient's signal far away from a medical center. This system transmits the collected ECG signal as well some vital signs to the medical center by using internet network. In order to review and analyze the recorded data, an ECG Viewer software is added to this system. The automatic measurement algorithm and some additional tools embedded in the ECG Viewer provide a convenient platform for ECG signal analysis by the cardiologist in the medical center. The designed system has been tested as a prototype in Tehran Emergency Service Center. The evaluation of the proposed system shows acceptable results in recording, transmission, and accurate analysis of ECG signals to early diagnosis of Acute Myocardial Infarction.Comment: 2nd Conference on Novel Approaches of Biomedical Engineering in Cardiovascular Disease

Simultaneous 12-Lead Electrocardiogram Synthesis using a Single-Lead ECG Signal: Application to Handheld ECG Devices

Recent introduction of wearable single-lead ECG devices of diverse configurations has caught the intrigue of the medical community. While these devices provide a highly affordable support tool for the caregivers for continuous monitoring and to detect acute conditions, such as arrhythmia, their utility for cardiac diagnostics remains limited. This is because clinical diagnosis of many cardiac pathologies is rooted in gleaning patterns from synchronous 12-lead ECG. If synchronous 12-lead signals of clinical quality can be synthesized from these single-lead devices, it can transform cardiac care by substantially reducing the costs and enhancing access to cardiac diagnostics. However, prior attempts to synthesize synchronous 12-lead ECG have not been successful. Vectorcardiography (VCG) analysis suggests that cardiac axis synthesized from earlier attempts deviates significantly from that estimated from 12-lead and/or Frank lead measurements. This work is perhaps the first successful attempt to synthesize clinically equivalent synchronous 12-lead ECG from single-lead ECG. Our method employs a random forest machine learning model that uses a subject's historical 12-lead recordings to estimate the morphology including the actual timing of various ECG events (relative to the measured single-lead ECG) for all 11 missing leads of the subject. Our method was validated on two benchmark datasets as well as paper ECG and AliveCor-Kardia data obtained from the Heart, Artery, and Vein Center of Fresno, California. Results suggest that this approach can synthesize synchronous ECG with accuracies (R2) exceeding 90%. Accurate synthesis of 12-lead ECG from a single-lead device can ultimately enable its wider application and improved point-of-care (POC) diagnostics

Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing

In recent years, the use of bio-sensing signals such as electroencephalogram (EEG), electrocardiogram (ECG), etc. have garnered interest towards applications in affective computing. The parallel trend of deep-learning has led to a huge leap in performance towards solving various vision-based research problems such as object detection. Yet, these advances in deep-learning have not adequately translated into bio-sensing research. This work applies novel deep-learning-based methods to various bio-sensing and video data of four publicly available multi-modal emotion datasets. For each dataset, we first individually evaluate the emotion-classification performance obtained by each modality. We then evaluate the performance obtained by fusing the features from these modalities. We show that our algorithms outperform the results reported by other studies for emotion/valence/arousal/liking classification on DEAP and MAHNOB-HCI datasets and set up benchmarks for the newer AMIGOS and DREAMER datasets. We also evaluate the performance of our algorithms by combining the datasets and by using transfer learning to show that the proposed method overcomes the inconsistencies between the datasets. Hence, we do a thorough analysis of multi-modal affective data from more than 120 subjects and 2,800 trials. Finally, utilizing a convolution-deconvolution network, we propose a new technique towards identifying salient brain regions corresponding to various affective states.Comment: Accepted for publication in IEEE Transactions on Affective Computing. This version on the arXiv is the updated version of the same manuscrip

Non-invasive acquisition of fetal ECG from the maternal xyphoid process: a feasibility study in pregnant sheep and a call for open data sets

Objective: The utility of fetal heart rate (FHR) monitoring can only be achieved with an acquisition sampling rate that preserves the underlying physiological information on the millisecond time scale (1000 Hz rather than 4 Hz). For such acquisition, fetal ECG (fECG) is required, rather than the ultrasound to derive FHR. We tested one recently developed algorithm, SAVER, and two widely applied algorithms to extract fECG from a single channel maternal ECG signal recorded over the xyphoid process rather than the routine abdominal signal. Approach: At 126dG, ECG was attached to near-term ewe and fetal shoulders, manubrium and xyphoid processes (n=12). FECG served as the ground-truth to which the fetal ECG signal extracted from the simultaneously-acquired maternal ECG was compared. All fetuses were in good health during surgery (pH 7.29+/-0.03, pO2 33.2+/-8.4, pCO2 56.0+/-7.8, O2Sat 78.3+/-7.6, lactate 2.8+/-0.6, BE -0.3+/-2.4). Main result: In all animals, single lead fECG extraction algorithm could not extract fECG from the maternal ECG signal over the xyphoid process with the F1 less than 50%. Significance: The applied fECG extraction algorithms might be unsuitable for the maternal ECG signal over the xyphoid process, or the latter does not contain strong enough fECG signal, although the lead is near the mother's abdomen. Fetal sheep model is widely used to mimic various fetal conditions, yet ECG recordings in a public data set form are not available to test the predictive ability of fECG and FHR. We are making this data set openly available to other researchers to foster non-invasive fECG acquisition in this animal model