Evangelos Karvounis

Fetal heart rate extraction from composite maternal ECG using complex continuous wavelet transform

Fetal heart rate extraction from the abdominal ECG is of great importance due to the information that carries in assessing appropriately the fetus well-being during pregnancy. In this work a novel automated method is presented for the detection of the QRS complexes of the fetus cardiac activity using multichannel maternal ECG recordings. No accessory preprocessing step for noise filtering is required. The method is based on the complex continuous wavelet transform and modulus maxima theory. The proposed method was validated using real signals, recorded at different weeks of gestation, covering most of the pregnancy period. The system performs well, since almost all fetal beats are detected (accuracy: 99.5%).

A Method for Fetal Heart Rate Extraction Based on Time-Frequency Analysis

A three-stage method for fetal heart rate extraction, from abdominal ECG recordings, is proposed. In the first stage the maternal R-peaks and fiducial points (QRS onset and offset) are detected, using time-frequency analysis, and the maternal QRS complexes are eliminated. The second stage locates the positions of the candidate fetal R-peaks, using complex wavelets and pattern matching theory techniques. In the third stage, the fetal R-peaks that overlap with the maternal QRS complexes are found. The method is validated using a dataset of 4 long duration recordings and the obtained results indicate high detection ability of the method (96% accuracy)

A cardiovascular simulator tailored for training and clinical uses

OBJECTIVEnIn the present work a cardiovascular simulator designed both for clinical and training use is presented.nnnMETHODnThe core of the simulator is a lumped parameter model of the cardiovascular system provided with several modules for the representation of baroreflex control, blood transfusion, ventricular assist device (VAD) therapy and drug infusion. For the training use, a Pre-Set Disease module permits to select one or more cardiovascular diseases with a different level of severity. For the clinical use a Self-Tuning module was implemented. In this case, the user can insert patients specific data and the simulator will automatically tune its parameters to the desired hemodynamic condition. The simulator can be also interfaced with external systems such as the Specialist Decision Support System (SDSS) devoted to address the choice of the appropriate level of VAD support based on the clinical characteristics of each patient.nnnRESULTSnThe Pre-Set Disease module permits to reproduce a wide range of pre-set cardiovascular diseases involving heart, systemic and pulmonary circulation. In addition, the user can test different therapies as drug infusion, VAD therapy and volume transfusion. The Self-Tuning module was tested on six different hemodynamic conditions, including a VAD patient condition. In all cases the simulator permitted to reproduce the desired hemodynamic condition with an error<10%.nnnCONCLUSIONSnThe cardiovascular simulator could be of value in clinical arena. Clinicians and students can utilize the Pre-Set Diseases module for training and to get an overall knowledge of the pathophysiology of common cardiovascular diseases. The Self-Tuning module is prospected as a useful tool to visualize patients status, test different therapies and get more information about specific hemodynamic conditions. In this sense, the simulator, in conjunction with SDSS, constitutes a support to clinical decision - making.

A Non-invasive Methodology for Fetal Monitoring during Pregnancy

OBJECTIVESnThis paper describes a methodology for the monitoring of the fetal cardiac health status during pregnancy, through the effective and non-invasive monitoring of the abdominal ECG signals (abdECG) of the mother.nnnMETHODSnFor this purpose, a three-stage methodology has been developed. In the first stage, the fetal heart rate (fHR) is extracted from the abdECG signals, using nonlinear analysis. Also, the eliminated ECG (eECG) is calculated, which is the abdECG after the maternal QRSs elimination. In the second stage, a blind source separation technique is applied to the eECG signals and the fetal ECG (fECG) is obtained. Finally, monitoring of the fetus is implemented using features extracted from the fHR and fECG, such as the T/QRS ratio and the characterization of the fetal ST waveforms.nnnRESULTSnThe methodology is evaluated using a dataset of simulated multichannel abdECG signals: 94.79% accuracy for fHR extraction, 92.49% accuracy in T/QRS ratio calculation and 79.87% in ST waveform classification.nnnCONCLUSIONSnThe novel non-invasive proposed methodology is advantageous since it offers automated identification of fHR and fECG and automated ST waveform analysis, exhibiting a high diagnostic accuracy.

ART-ML: A new markup language for modelling and representation of biological processes in cardiovascular diseases

BACKGROUNDnWith an ever increasing number of biological models available on the internet, a standardized modelling framework is required to allow information to be accessed and visualized.nnnOBJECTIVEnIn this paper we propose a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of geometry, blood flow, plaque progression and stent modelling, exported by any cardiovascular disease modelling software. ART-ML has been developed and tested using ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries.nnnMETHODSnThe images and their content are fused to develop morphological models of the arteries in 3D representations. All the above described procedures integrate disparate data formats, protocols and tools. ART-ML proposes a representation way, expanding ARTool, for interpretability of the individual resources, creating a standard unified model for the description of data and, consequently, a format for their exchange and representation that is machine independent. More specifically, ARTool platform incorporates efficient algorithms which are able to perform blood flow simulations and atherosclerotic plaque evolution modelling. Integration of data layers between different modules within ARTool are based upon the interchange of information included in the ART-ML model repository. ART-ML provides a markup representation that enables the representation and management of embedded models within the cardiovascular disease modelling platform, the storage and interchange of well-defined information.nnnRESULTSnThe corresponding ART-ML model incorporates all relevant information regarding geometry, blood flow, plaque progression and stent modelling procedures. All created models are stored in a model repository database which is accessible to the research community using efficient web interfaces, enabling the interoperability of any cardiovascular disease modelling software models.nnnCONCLUSIONSnART-ML can be used as a reference ML model in multiscale simulations of plaque formation and progression, incorporating all scales of the biological processes.

ART-ML - a novel XML format for the biological procedures modeling and the representation of blood flow simulation

The paper proposes a novel Extensible Markup Language (XML) based format called ART-ML that aims at supporting the interoperability and the reuse of models of blood flow, mass transport and plaque formation, exported by ARTool. ARTool is a platform for the automatic processing of various image modalities of coronary and carotid arteries. The images and their content are fused to develop morphological models of the arteries in easy to handle 3D representations. The platform incorporates efficient algorithms which are able to perform blood flow simulation. In addition atherosclerotic plaque development is estimated taking into account morphological, flow and genetic factors. ART-ML provides a XML format that enables the representation and management of embedded models within the ARTool platform and the storage and interchange of well-defined information. This approach influences in the model creation, model exchange, model reuse and result evaluation.

Fetal heart rate detection in multivariate abdominal ECG recordings using non-linear analysis

A novel three-stage methodology for the detection of fetal heart rate (fHR) from multivariate abdominal electrocardiogram (ECG) recordings is introduced. In the first stage, the maternal R-peaks and fiducial points (maternal QRS onset and offset) are detected. Maternal fiducial points are used to eliminate the maternal QRS complexes from the abdominal ECG recordings. In the second stage, two denoising procedures are applied to enhance the fetal QRS complexes. The phase space characteristics are employed to identify fetal heart beats not overlapping with the maternal QRSs which are eliminated in the first stage. The extraction of the fetal heart rate is accomplished in the third stage, using a histogram based technique in order to identify the location of the fetal heart beats which overlap with the maternal QRSs. The methodology is evaluated on simulated and real multichannel ECG signals. In both cases, the obtained results indicate high performance; in the simulated ECGs the accuracy ranges from 74.21–100%, depending on the employed SNR, while in the real recordings the average accuracy is 94.08%.