Surface chest motion can be easily monitored with a wide variety of sensors such as pressure belts, fiber Bragg gratings and inertial sensors, etc. The current applications of these sensors are mainly restricted to respiratory motion monitoring/analysis due to the technical challenges involved in separation of the cardiac motion from the dominant respiratory motion. The contribution of heart to the surface chest motion is relatively very small as compared to the respiratory motion. Further, the heart motion spectrally overlaps with the respiratory harmonics and their separation becomes even more challenging. In this paper, we approach this source separation problem with independent component analysis (ICA) framework. ICA with reference (ICA-R) yields only desired component with improved separation, but the method is highly sensitive to the reference generation. Several reference generation approaches are developed to solve the problem. Experimental validation of these proposed approaches is performed with chest displacement data and ECG obtained from healthy subjects under normal breathing and post-exercise conditions. The extracted component morphologically matches well with the collected ECG. Results show that the proposed methods perform better than conventional band pass filtering.
Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.
Chest surface motion is of significant importance as it contains information of respiratory and cardiac systems together with the complex coupling between these two systems. Chest surface motion is not only critical in radiotherapy, but also useful in personalized systems for continuous cardiorespiratory monitoring. In this dataset, a multimodal setup is employed to simultaneously acquire cardiorespiratory signals. These signals include high-density trunk surface motion (from 16 distinct locations) with VICON motion capture system, nasal breathing from a thermal sensor, respiratory effort from a strain belt and electrocardiogram in lead-II configuration. This dataset contains 72 trials recorded from 11 participants with a cumulative duration of approximately 215 min under various conditions such as normal breathing, breath-hold, irregular breathing and post-exercise recovery. The presented dataset is not only useful for evaluating prediction algorithms for radiotherapy applications, but can also be employed for the development of techniques to evaluate the cardio-mechanics and hemodynamic parameters of chest surface motion.
Siemocardiography is a non-invasive technique for cardiomechanical assessment by analyzing the local vibrations on chest surface which can be readily acquired from cost-effective accelerometers. The peaks in siesmocardiogram (SCG) signal correspond to underlying mechanical events in heart cycle and have numerious potential clinical and health-awareness applications. However, utilization of SCG signal requires annotation of these peaks that is challenging due to variations in inter-subject morphology and noise prone characteristics of SCG signal. In this paper, we propose an approach to automatically annotate the desired peaks in SCG signal that are required for systolic time intervals (STI). The approach is based on formulating sliding template for the oncoming beat which is less noisier and hence desired peak detection is easier. The information of peak detected in the sliding template is then used to narrow-down the search of desired peak in actual signal.
Performance of robotics based hand-held surgical devices in real-time is mainly dependent on accurate filtering of physiological tremor. The presence of phase delay in sensors (hardware) and filtering (software) processes affects the cancellation accuracy. This paper focuses on developing an estimation algorithm to improve the estimation accuracy in the presence of phase delay for real-time implementations. Moving window based online training approach for least squares-support vector machines (LSSVM) is employed in this paper for tremor estimation. A study is conducted with tremor data recorded from the subjects to analyze the suitability of proposed approach for both single-step and multi-step prediction.
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