Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

Background: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. Objective: To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. Method: Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. Result: We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. Conclusion: Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

Cardiac Electrophysiology study is the origin and treatment of arrhythmia, which is an abnormality in the rate, regularity or sequence of cardiac activation. There has been considerable recent development in this field, where computational methods such as Imaging and Machine Learning for Cardiac Electrophysiology, provide the framework for cardiac re-modeling. In this research, we review various recent strategies currently available for the meeting the goal of structurally and functionally integrated models of cardiac function that combine data intensive cellular systems models with compute-intensive anatomically detailed multiscale simulations.

Arterial stiffness has been proven to be one of the most promising diagnostic and prognostic features of many cardiovascular diseases (CVD). Arterial compliance (AC) and pulse wave velocity (PWV) allows a good quantitative assessment of arterial stiffness. To estimate pulmonary Arterial Compliance and Pulse Wave Velocity in Cerebral-Cardiovascular Patients using 64-Row Multi-Detector Computed Tomography Cardiac Images. Here in this work, we have used an in house developed three-dimensional reconstruction algorithm that extracts high-resolution cardiac Computer Tomography images in Digital Imaging and Communications in Medicine (DICOM) format from nine subjects. The subjects consist four patients with stroke (SR) turned to atrial fibrillation (AF) and five patients with both SR and AF. The calculation showed strok volume of Left and Right Ventricles, which are near equal to 62.56±12.61 ml and 65.95±13.13 ml, respectively, with a good positive correlation (R = 0.717, p < 0.0001) while pulse wave velocity and arterial compliance are calculated to be 0.21±0.0665 m/s and 0.09 ± 0.0547 ml/mmHg for patients with SR turned AF, 0.15±0.0502 m/s and 0.10 ± 0.0438 for the SR and AF patients. We concluded that the derived methodology for calculation of PWV and AC from 3D cardiac CT images could be used as an index in the evaluation of arterial stiffness.

Most of the epidemiological studies have shown a good relationship between blood pressure and manifestation of cardiovascular diseases (CVD). CVD has become a very common health condition that exhibits significant changes in distensibilility, compliance, and elastic modulus of the arterial vascular system. The main causes of this disease are the spreading of fats and cholesterols which initiate the damaging of endothelium, decreasing wall buffering capacity, and hardening of the arteries eventually leading to severe narrowing. We compared data from invasive and non-invasive blood pressure wave form. Using the Windkessel model, to estimate the arterial compliance as vascular index, we used data after the aortic valve closed to the end-diastolic point of blood pressure waveforms, utilized Bland-Altman difference plot, T-test, box whisker plot and scatter diagram. We analyzed physiological real data and investigated the correlation between the non-invasive and invasive blood pressure wave form. Non-invasive compressive pulse waveforms can be used to assess the total compliance of the arterial blood pressure waveforms of the invasive aorta that indicates the buffering of muscle and adipose tissue.

Rooming-in is the arrangement that allows mothers and newborn babies in the same room during the postpartum hospital stay. This practice gives an opportunity for the parent to have more affectionate touch with their infants. However, the influence of intimate contact on neonate’s physiological changes has not been studied. Our study is purposed to develop an evaluation system of neonate physiological changes during parental contact. The system recorded the electrocardiogram (ECG) in real-time and analyzed the heart rate variability (HRV).Thirty healthy neonates are recruited in this study. The paired t-test is performed for statistical analysis. The HRV parameter of baby with and without parental hold was compared. The HF power and HF/LF ratio are significant (P<0.05). Though, there is no considerable difference in LF power. In conclusions, this research has developed a system and evaluated neonate physiological index. The system can make available reference to parent with a long-term monitoring and recording of their neonate physiological information.