Kouhyar Tavakolian

Ballistocardiography and Seismocardiography: A Review of Recent Advances

In the past decade, there has been a resurgence in the field of unobtrusive cardiomechanical assessment, through advancing methods for measuring and interpreting ballistocardiogram (BCG) and seismocardiogram (SCG) signals. Novel instrumentation solutions have enabled BCG and SCG measurement outside of clinical settings, in the home, in the field, and even in microgravity. Customized signal processing algorithms have led to reduced measurement noise, clinically relevant feature extraction, and signal modeling. Finally, human subjects physiology studies have been conducted using these novel instruments and signal processing tools with promising results. This paper reviews the recent advances in these areas of modern BCG and SCG research.

Mechanically Flexible Wireless Multisensor Platform for Human Physical Activity and Vitals Monitoring

Practical usability of the majority of current wearable body sensor systems for multiple parameter physiological signal acquisition is limited by the multiple physical connections between sensors and the data-acquisition modules. In order to improve the user comfort and enable the use of these types of systems on active mobile subjects, we propose a wireless body sensor system that incorporates multiple sensors on a single node. This multisensor node includes signal acquisition, processing, and wireless data transmission fitted on multiple layers of a thin flexible substrate with a very small footprint. Considerations for design include size, form factor, reliable body attachment, good signal coupling, low power consumption, and user convenience. The prototype device measures 55 15 mm and is 3 mm thick. The unit is attached to the patients chest, and is capable of performing simultaneous measurements of parameters, such as body motion, activity intensity, tilt, respiration, cardiac vibration, cardiac potential (ECG), heart rate, and body surface temperature. In this paper, we discuss the architecture of this system, including the multisensor hardware, the firmware, a mobile-phone receiver unit, and assembly of the first proof-of-concept prototype. Preliminary performance results on key elements of the system, such as power consumption, wireless range, algorithm efficiency, ECG signal quality for heart-rate calculations, as well as synchronous ECG and body activity signals are also presented.

Different classification techniques considering brain computer interface applications.

In this work the application of different machine learning techniques for classification of mental tasks from electroencephalograph (EEG) signals is investigated. The main application for this research is the improvement of brain computer interface (BCI) systems. For this purpose, Bayesian graphical network, neural network, Bayesian quadratic, Fisher linear and hidden Markov model classifiers are applied to two known EEG datasets in the BCI field. The Bayesian network classifier is used for the first time in this work for classification of EEG signals. The Bayesian network appeared to have a significant accuracy and more consistent classification compared to the other four methods. In addition to classical correct classification accuracy criteria, the mutual information is also used to compare the classification results with other BCI groups.

Seismocardiography: Past, present and future

This paper presents an overview of seismocardiography (SCG) as a noninvasive cardiology method. The paper represents a brief historical background to the SCG, an assessment of the technology at present, and an evaluation of the challenges we must address. These challenges include the development and clarification of definitions, standards, and annotations.

Improvement of ballistocardiogram processing by inclusion of respiration information

In this paper a novel methodology for processing of a ballistocardiogram (BCG) is proposed in which the respiration signal is utilized to improve the averaging of the BCG signal and ultimately the annotation and interpretation of the signal. Previous research works filtered out the respiration signal while the novelty of the current research is that, rather than removing the respiration effect from the signal, we utilize the respiration information to improve the averaging and thus analysis and interpretation of the BCG signal in diagnosis of cardiac malfunctions. This methodology is based on our investigation that BCG cycles corresponding to the inspiration and expiration phases of the respiration cycle are different in morphology. BCG cycles corresponding to the expiration phase of respiration have been proved to be more closely related to each other when compared to cycles corresponding to inspiration, and therefore expiration cycles are better candidates to be selected for the calculation of the averaged BCG signal. The new BCG average calculated based on this methodology is then considered as the representative and a template of the BCG signal for further processing. This template can be considered as the output of a clinical BCG instrument with higher reliability and accuracy compared to the previous processing methods.

BSeg++: A modified Blind Segmentation Method for Ballistocardiogram Cycle Extraction

This paper presents a method to extract cardiac cycles and H-I-J components of Ballistocardiogram (BCG). The new improved algorithm BSeg++ permits on the segmentation of BCG signal and extraction of its basic complexes H-I-J without Electrocardiogram (ECG) synchronization. The BSeg++ is based on two previously developed methods described in [1, 2, 3] for extracting BCG cycles without using a reference ECG signal. Those methods suffered from extract redundant BCG cycles because of motion artifacts or BCG fluctuations. In this study, we modified the blind segmentation algorithm and solved its problems. We also added another feature to detect H-I-J complexes of BCG. Also, this new algorithm can be used to extract cardiac cycles and R-S-T components of ECG. The data analysis has been performed on the subjects tested at Simon Fraser University. Initial tests of BCG and ECG from twenty subjects indicate that the method extracted BCG (ECG) cycles and its components with a negligible error in the presence of motion artifacts, BCG fluctuations, latency and non-linear disturbance.

Precordial vibrations provide noninvasive detection of early-stage hemorrhage.

ABSTRACT Graded lower-body negative pressure was used to create a hemodynamic response similar to hemorrhage. Echocardiogram measurements showed a maximal reduction of 32.4% in stroke volume. Analysis of systolic time intervals, such as pre-ejection period and left ventricular ejection time (LVET), derived from a seismocardiogram (SCG), were demonstrated to be more sensitive in detection of early-stage hemorrhage compared with pulse pressure, heart rate, and the amplitude features extracted from SCG. In particular, the LVET and pre-ejection period/LVET features, extracted from SCG, were significantly different between, and correlated with, the different stages of lower-body negative pressure (r = 0.9 and 0.88, P < 0.05), for 32 subjects. These results suggest a portable, cost-effective solution for identification of mild or moderate hemorrhage using accelerometers.

Automatic Annotation of Seismocardiogram With High-Frequency Precordial Accelerations

Seismocardiogram (SCG) is the low-frequency vibrations signal recorded from the chest using accelerometers. Peaks on dorsoventral and sternal SCG correspond to specific cardiac events. Prior research work has shown the potential of extracting such peaks for various types of monitoring and diagnosis applications. However, annotation of these peaks is not a trivial task and complicated in some subjects. In this paper, an automated method is proposed to annotate these peaks. The high-frequency accelerations obtained from the same accelerometer, used to record SCG with, were used to facilitate the annotation of the SCG. Algorithms were developed for detection of isovolumic moment (IM) and aortic valve closure (AC) points of SCG. Four different envelope calculation methods were used: cardiac sound characteristic waveform (CSCW), Shannon, absolute, and Hilbert. The algorithms were evaluated based on a dataset including 18 subjects undergoing lower body negative pressure and were further tested with another dataset, which included 67 subjects. These datasets had been previously manually annotated. The algorithm based on CSCW envelope calculation produced the highest detection accuracy for both IM and AC. The overall CSCW algorithm detection accuracy for the test dataset was 98.7% and 99.1% for the IM and AC points, respectively.

Development of a novel contactless mechanocardiograph device

A novel method of detecting mechanical movement of the heart, Mechanocardiography (MCG), with no connection to the subjects body is presented. This measurement is based on radar technology and it has been proven through this research work that the acquired signal is highly correlated to the phonocardiograph signal and acceleration-based ballistocardiograph signal (BCG) recorded directly from the sternum. The heart rate and respiration rate have been extracted from the acquired signal as two possible physiological monitoring applications of the radar-based MCG device.

Comparative analysis of three different modalities for characterization of the seismocardiogram

We introduce and compare three different modalities to study seismocardiogram (SCG) and its correlation with cardiac events. We used an accelerometer attached to the subject sternum to get a reference measure. Cardiac events were then approximately identified using echocardiography. As an alternative approximation, we used consecutive Cine-MRI images of the heart to capture cardiac movements and compared them with the experimental SCG. We also employed an anatomically accurate, finite element base electromechanical model with geometry built completely from DT-MRI to simulate a portion of the cardiac cycle as observed in the SCG signal. The preliminary results demonstrate the usability of these newly proposed methods to investigate the mechanism of SCG waves and also demonstrate the usability of echocardiograph in interpretation of these results in terms of correlating them to underlying cardiac cycle events.