Ghufran Shafiq

Surface Chest Motion Decomposition for Cardiovascular Monitoring

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.

Automatic Identification of Systolic Time Intervals in Seismocardiogram.

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.

Online LS-SVM based multi-step prediction of physiological tremor for surgical robotics

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.

Performance Comparison of Spatial Filter with Multiple BMFLCs for BCI Applications

The subjects can learn to modulate their EEG pattern to achieve multiple targets in brain-computer interface systems. The modulation can occur in both α and β bands of the EEG signal. To successfully identify these modulated EEG patterns, multiple band-limited multiple Fourier linear combiner (BMFLCs) are employed to estimate the amplitude variations in EEG. To achieve better signal to noise ratio, spatial filter is paired with BMFLC for classification with linear discriminant analysis. Various existing spatial filters are paired with BMFLC and the performance is compared to identify the best spatial filters for classification of four targets in BCI Competition 2003 data set II(a). Results show that the Tikhonov regularized common spatial filter (TRCSP) paired with BMFLC provides better accuracy in comparison with other spatial filters.

BMFLC with neural network and DE for better event classification

The event-related desynchronization(ERD) is a well known phenomenon that is commonly used for classification in brain-computer interface(BCI) applications. The classification accuracy of ERD based BCI can be improved by selection of subject-specific reactive band rather than complete μ-band. After obtaining time-frequency(TF) mapping of EEG signal with a Fourier based adaptive method, differential evolution(DE) is used for the identification of the reactive band. Compared to classical band-power based method, the proposed method based on subject-specific reactive band yields better accuracy with BCI competition dataset IV.

Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications

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.

Predictive local receptive fields based respiratory motion tracking for motion-adaptive radiotherapy

Extracranial robotic radiotherapy employs external markers and a correlation model to trace the tumor motion caused by the respiration. The real-time tracking of tumor motion however requires a prediction model to compensate the latencies induced by the software (image data acquisition and processing) and hardware (mechanical and kinematic) limitations of the treatment system. A new prediction algorithm based on local receptive fields extreme learning machines (pLRF-ELM) is proposed for respiratory motion prediction. All the existing respiratory motion prediction methods model the non-stationary respiratory motion traces directly to predict the future values. Unlike these existing methods, the pLRF-ELM performs prediction by modeling the higher-level features obtained by mapping the raw respiratory motion into the random feature space of ELM instead of directly modeling the raw respiratory motion. The developed method is evaluated using the dataset acquired from 31 patients for two horizons in-line with the latencies of treatment systems like CyberKnife. Results showed that pLRF-ELM is superior to that of existing prediction methods. Results further highlight that the abstracted higher-level features are suitable to approximate the nonlinear and non-stationary characteristics of respiratory motion for accurate prediction.

Corrigendum: Multimodal chest surface motion data for respiratory and cardiovascular monitoring applications

This corrects the article DOI: 10.1038/sdata.2017.52.

Robust control of DC motor drives using higher-order integral terminal sliding mode

The tracking performance of the industrial servo systems are highly affected by the inherent unstructured uncertainties (external disturbances, and/or unmodeled dynamics), which degrades the reliability of the drive. This paper presents a higher-order terminal sliding mode controller to achieve high-accuracy motion-tracking control of DC motor drives. Fractional integral terminal sliding mode (ITSM) manifold is selected to eliminate the reaching time to the sliding hyperplane, which provides fast tracking error convergence in finite-time. Further, super-twisting control is employed to reduce the chattering while compensating the unwanted unstructured uncertainties compared with the traditional sliding mode control. Experimental results on a DC motor-based industrial mechatronic drives unit (IMDU) with belt-drive load are presented to show the effectiveness of the proposed controller.

Automatic annotation of peaks in seismocardiogram for systolic time intervals

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.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.