Abdul Qadir Javaid

Harmonic Path (HAPA) algorithm for non-contact vital signs monitoring with IR-UWB radar

We introduce the Harmonic Path (HAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. A well known result is that a periodic movement, such as the lung wall or heart wall movement, induces a fundamental frequency and its harmonics. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods try to identify the fundamental component to estimate the HR and/or RR. However, often the fundamental is distorted or cancelled by interference, such as RR harmonics interference on the HR fundamental, leading to significant error for HR estimation. HAPA is the first reported algorithm to take advantage of the HR harmonics, where there is less interference, to achieve more reliable and robust estimation of the fundamental frequency. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference.

Quantifying and Reducing Motion Artifacts in Wearable Seismocardiogram Measurements During Walking to Assess Left Ventricular Health

Goal: Our objective is to provide a framework for extracting signals of interest from the wearable seismocardiogram (SCG) measured during walking at normal (subjects preferred pace) and moderately fast (1.34–1.45 m/s) speeds. Methods: We demonstrate, using empirical mode decomposition (EMD) and feature tracking algorithms, that the pre-ejection period (PEP) can be accurately estimated from a wearable patch that simultaneously measures electrocardiogram and sternal acceleration signals. We also provide a method to determine the minimum number of heartbeats required for an accurate estimate to be obtained for the PEP from the accelerometer signals during walking. Results: The EMD-based denoising approach provides a statistically significant increase in the signal-to-noise ratio of wearable SCG signals and also improves estimation of PEP during walking. Conclusion: The algorithms described in this paper can be used to provide hemodynamic assessment from wearable SCG during walking. Significance: A major limitation in the use of the SCG, a measure of local chest vibrations caused by cardiac ejection of blood in the vasculature, is that a user must remain completely still for high-quality measurements. The motion can create artifacts and practically render the signal unreadable. Addressing this limitation could allow, for the first time, SCG measurements to be obtained reliably during movement—aside from increasing the coverage throughout the day of cardiovascular monitoring, analyzing SCG signals during movement would quantify the cardiovascular systems response to stress (exercise), and thus provide a more holistic assessment of overall health.

Towards Sleep Apnea Screening with an Under-the-Mattress IR-UWB Radar Using Machine Learning

In this work, we apply machine learning to investigate the effectiveness of an Impulse Radio Ultra-Wide Band (IR-UWB) radar panel, in an under-the-mattress configuration, for detecting apnea events in subjects known to have obstructive sleep apnea (OSA). We consider a collection of features, some novel and some inspired by features that worked well for sleep apnea detection using other types of sensors (i.e., not IR-UWB). To extract the features, we collected a total of 25 hours of data from four subjects as they slept through the night. The data included digitized samples of the IR-UWB radar return signal and the scored polysomnograph (PSG), which is the gold standard and measures a large number of physiological parameters in a well-equipped sleep laboratory. Normal and apnea epochs were extracted from the IR-UWB data corresponding to normal and apnea epochs in the PSG data. Statistical features were derived from these extracted epochs and a Linear Discriminant classifier was trained. Using cross-validation, we found that the classifier had an accuracy of around 70% in detection of apnea and normal epochs. The novel aspect of this project involves processing and investigation of different methods for feature extraction on data obtained from real apnea subjects and suggests that the radar, when paired with other under-the-mattress sensors might provide an effective screening device in a convenient form factor.

Towards robust estimation of systolic time intervals using head-to-foot and dorso-ventral components of sternal acceleration signals

Continuous measurement of cardiac time intervals throughout normal activities of daily living is of interest for both chronic disease management and preventive wellness monitoring. Systolic time intervals in particular - i.e., pre-ejection period (PEP) and left ventricular ejection time (LVET) - have been shown to be relevant to assessing myocardial health and performance, but are challenging to measure with wearable sensors. In this paper, we present novel methods for estimating PEP and LVET from a single three-axis accelerometer placed at the sternum, based on the measurement of cardiogenic vibrations: seismocardiography (SCG) and ballistocardiography (BCG). Although such signals have been examined in the existing literature, the analysis and interpretation has focused mainly on the dorso-ventral components only in the context of systolic time interval estimation. In this paper, we find that features extracted from the head-to-foot accelerations yield better correlations to PEP measured from impedance cardiogram (ICG) than standard approaches based on dorso-ventral components. Additionally, we examine the effects of postural variations on the correlation between PEP estimated from accelerometer and ICG signals and also on correlation between LVET estimated from both sensors. We determine that such correlations are robust to postural changes. Based on these findings, we anticipate that wearable, accelerometer based vibration measurements from standing subjects can be used for robust systolic time interval estimation in a variety of ubiquitous cardiovascular health and fitness sensing applications.

Elucidating the Hemodynamic Origin of Ballistocardiographic Forces: Toward Improved Monitoring of Cardiovascular Health at Home

The ballistocardiogram (BCG), a signal describing the reaction forces of the body to cardiac ejection of blood, has recently gained interest in the research community as a potential tool for monitoring the mechanical aspects of cardiovascular health for patients at home and during normal activities of daily living. An important limitation in the field of BCG research is that while the BCG signal measures the forces of the body, the information desired (and understood) by clinicians and caregivers, regarding mechanical health of the cardiovascular system, is typically expressed as blood pressure or flow. This paper aims to explore, using system identification tools, the mathematical relationship between the BCG signal and the better-understood impedance cardiography (ICG) and arterial blood pressure (ABP) waveforms, with a series of human subject studies designed to asynchronously modulate cardiac output and blood pressure and with different magnitudes. With this approach, we demonstrate for 19 healthy subjects that the BCG waveform more closely maps to the ICG (flow) waveform as compared with the finger-cuff-based ABP (pressure) waveform, and that the BCG can provide a more accurate estimate of stroke volume (r=0.73, p <; 0.05) as compared with pulse pressure changes (r = 0.26). We also examined, as a feasibility study, for one subject, the ability to calibrate the BCG measurement tool with an ICG measurement on the first day, and then track changes in stroke volume on subsequent days. Accordingly, we conclude that the BCG is a signal more closely related to blood flow than pressures, and that a key health parameter for titrating care-stroke volume-can potentially be accurately measured with BCG signals at home using unobtrusive and inexpensive hardware, such as a modified weighing scale, as compared with the state-of-the-art ICG and ABP devices, which are expensive and obtrusive for use at home.

Quantifying and Reducing Posture-Dependent Distortion in Ballistocardiogram Measurements

Ballistocardiography is a noninvasive measurement of the mechanical movement of the body caused by cardiac ejection of blood. Recent studies have demonstrated that ballistocardiogram (BCG) signals can be measured using a modified home weighing scale and used to track changes in myocardial contractility and cardiac output. With this approach, the BCG can potentially be used both for preventive screening and for chronic disease management applications. However, for achieving high signal quality, subjects are required to stand still on the scale in an upright position for the measurement; the effects of intentional (for user comfort) or unintentional (due to user error) modifications in the position or posture of the subject during the measurement have not been investigated in the existing literature. In this study, we quantified the effects of different standing and seated postures on the measured BCG signals, and on the most salient BCG-derived features compared to reference standard measurements (e.g., impedance cardiography). We determined that the standing upright posture led to the least distorted signals as hypothesized, and that the correlation between BCG-derived timing interval features (R-J interval) and the preejection period, PEP (measured using ICG), decreased significantly with impaired posture or sitting position. We further implemented two novel approaches to improve the PEP estimates from other standing and sitting postures, using system identification and improved J-wave detection methods. These approaches can improve the usability of standing BCG measurements in unsupervised settings (i.e., the home), by improving the robustness to nonideal posture, as well as enabling high-quality seated BCG measurements.

Novel Wearable Seismocardiography and Machine Learning Algorithms Can Assess Clinical Status of Heart Failure Patients

Background: Remote monitoring of patients with heart failure (HF) using wearable devices can allow patient-specific adjustments to treatments and thereby potentially reduce hospitalizations. We aimed to assess HF state using wearable measurements of electrical and mechanical aspects of cardiac function in the context of exercise. Methods and Results: Patients with compensated (outpatient) and decompensated (hospitalized) HF were fitted with a wearable ECG and seismocardiogram sensing patch. Patients stood at rest for an initial recording, performed a 6-minute walk test, and then stood at rest for 5 minutes of recovery. The protocol was performed at the time of outpatient visit or at 2 time points (admission and discharge) during an HF hospitalization. To assess patient state, we devised a method based on comparing the similarity of the structure of seismocardiogram signals after exercise compared with rest using graph mining (graph similarity score). We found that graph similarity score can assess HF patient state and correlates to clinical improvement in 45 patients (13 decompensated, 32 compensated). A significant difference was found between the groups in the graph similarity score metric (44.4±4.9 [decompensated HF] versus 35.2±10.5 [compensated HF]; P<0.001). In the 6 decompensated patients with longitudinal data, we found a significant change in graph similarity score from admission (decompensated) to discharge (compensated; 44±4.1 [admitted] versus 35±3.9 [discharged]; P<0.05). Conclusions: Wearable technologies recording cardiac function and machine learning algorithms can assess compensated and decompensated HF states by analyzing cardiac response to submaximal exercise. These techniques can be tested in the future to track the clinical status of outpatients with HF and their response to pharmacological interventions.

Spectrum-Averaged Harmonic Path (SHAPA) Algorithm for Non-Contact Vital Sign Monitoring with Ultra-wideband (UWB) Radar

We introduce the Spectrum-averaged Harmonic Path (SHAPA) algorithm for estimation of heart rate (HR) and respiration rate (RR) with Impulse Radio Ultrawideband (IR-UWB) radar. Periodic movement of human torso caused by respiration and heart beat induces fundamental frequencies and their harmonics at the respiration and heart rates. IR-UWB enables capture of these spectral components and frequency domain processing enables a low cost implementation. Most existing methods of identifying the fundamental component either in frequency or time domain to estimate the HR and/or RR lead to significant error if the fundamental is distorted or cancelled by interference. The SHAPA algorithm (1) takes advantage of the HR harmonics, where there is less interference, and (2) exploits the information in previous spectra to achieve more reliable and robust estimation of the fundamental frequency in the spectrum under consideration. Example experimental results for HR estimation demonstrate how our algorithm eliminates errors caused by interference and produces 16% to 60% more valid estimates.

A novel physiological features-assisted architecture for rapidly distinguishing health problems from hardware Trojan attacks and errors in medical devices

Malicious Hardware Trojans (HTs) that are inserted during chip manufacturing can corrupt data which if undetected may cause serious harm in medical devices. This paper presents a novel physiological features-assisted architecture to detect and distinguish attacks by ultra-small HTs from actual health problems in health monitoring applications. Our threat scenario considers attacks that pass undetected using other HT detection methods such as ones that use side-channel analysis and digital systems test. The key to our detection approach is to embed multiple signature generation and testing techniques, some of which are based on physiology, deep in the hardware and close to the origin of data generation. Our experimental results show that our proposed techniques are able to distinguish unhealthy physiology from functionality altering HT attacks anywhere inside a state-of-the-art medical chip including the chips primary inputs with minimal performance and area overhead.

Balance-based time-frequency features for discrimination of young and elderly subjects using unsupervised methods

The maintenance of static standing balance is not only essential for performing everyday activities but is also an important risk factor for prediction of falls in the elderly adults. Human balance / posture control is controlled by complex integration of different senses. The changes in steadiness of balance can be characterized by center-of-pressure (COP) signals measured from a 3-D force plate while a subject stands on it in an upright erect posture for a short duration. Recent research has shown that time and frequency based features from these COP tracings are different for elderly adults as compared to young healthy individuals and thus, can provide a deep insight into the postural control system. However, most of the studies have focused on features from groups of healthy old and young subjects while others have tried to find useful information from the COP signals for different disease categories. In this work, we propose a collection of features, some novel and some inspired by features that worked well for previous COP analysis. The data for the work is obtained from Physionet and contains COP signals from healthy as well as subjects with illnesses in both the young and old categories. A statistical analysis is performed on the extracted features along with a t-Stochastic Neighborhood Embedding (t-SNE) based visualization. The results of the study indicate that time and frequency based features can help in distinguishing groups based on age.