A cardiovascular simulator tailored for training and clinical uses.

OBJECTIVE: In the present work a cardiovascular simulator designed both for clinical and training use is presented. METHOD: The 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 patient's 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. RESULTS: The 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%. CONCLUSIONS: The 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 patient's 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 decision support system for the treatment of patients with ventricular assist device support.

BACKGROUND: Heart failure (HF) is affecting millions of people every year and it is characterized by impaired ventricular performance, exercise intolerance and shortened life expectancy. Despite significant advancements in drug therapy, mortality of the disease remains excessively high, as heart transplant remains the gold standard treatment for end-stage HF when no contraindications subsist. Traditionally, implanted Ventricular Assist Devices (VADs) have been employed in order to provide circulatory support to patients who cannot survive the waiting time to transplantation, reducing the workload imposed on the heart. In many cases that process could recover its contractility performance. OBJECTIVES: The SensorART platform focuses on the management and remote treatment of patients suffering from HF. It provides an interoperable, extendable and VAD-independent solution, which incorporates various hardware and software components in a holistic approach, in order to improve the quality of the patients' treatment and the workflow of the specialists. This paper focuses on the description and analysis of Specialist's Decision Support System (SDSS), an innovative component of the SensorART platform. METHODS: The SDSS is a Web-based tool that assists specialists on designing the therapy plan for their patients before and after VAD implantation, analyzing patients' data, extracting new knowledge, and making informative decisions. RESULTS: SDSS offers support to medical and VAD experts through the different phases of VAD therapy, incorporating several tools covering all related fields; Statistics, Association Rules, Monitoring, Treatment, Weaning, Speed and Suction Detection. CONCLUSIONS: SDSS and its modules have been tested in a number of patients and the results are encouraging.

A non-invasive methodology for fetal monitoring during pregnancy.

OBJECTIVES: This 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. METHODS: For 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. RESULTS: The 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. CONCLUSIONS: The 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.

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%.

Automatic seizure detection based on time-frequency analysis and artificial neural networks.

The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during long-term electroencephalograph (EEG) recordings is highly recommended. As EEG signals are nonstationary, the conventional methods of frequency analysis are not successful for diagnostic purposes. This paper presents a method of analysis of EEG signals, which is based on time-frequency analysis. Initially, selected segments of the EEG signals are analyzed using time-frequency methods and several features are extracted for each segment, representing the energy distribution in the time-frequency plane. Then, those features are used as an input in an artificial neural network (ANN), which provides the final classification of the EEG segments concerning the existence of seizures or not. We used a publicly available dataset in order to evaluate our method and the evaluation results are very promising indicating overall accuracy from 97.72% to 100%.

An arrhythmia classification system based on the RR-interval signal.

OBJECTIVE: This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. METHODOLOGY: A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of beats: normal, premature ventricular contractions, ventricular flutter/fibrillation and 2 degrees heart block. The beat classification is used as input of a knowledge-based deterministic automaton to achieve arrhythmic episode detection and classification. Six rhythm types are classified: ventricular bigeminy, ventricular trigeminy, ventricular couplet, ventricular tachycardia, ventricular flutter/fibrillation and 2 degrees heart block. RESULTS: The method is evaluated by using the MIT-BIH arrhythmia database. The achieved scores indicate high performance: 98% accuracy for arrhythmic beat classification and 94% accuracy for arrhythmic episode detection and classification. CONCLUSION: The proposed method is advantageous because it uses only the RR-interval signal for arrhythmia beat and episode classification and the results compare well with more complex methods.