Yee Siong Lee

Monitoring and Analysis of Respiratory Patterns Using Microwave Doppler Radar

Noncontact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations, such as physical sensor attachment or special clothing, which can be useful for certain healthcare applications. Furthermore, robustness of Doppler radar against environmental factors, such as light, ambient temperature, interference from other signals occupying the same bandwidth, fading effects, reduce environmental constraints and strengthens the possibility of employing Doppler radar in long-term respiration detection, and monitoring applications such as sleep studies. This paper presents an evaluation in the of use of microwave Doppler radar for capturing different dynamics of breathing patterns in addition to the respiration rate. Although finding the respiration rate is essential, identifying abnormal breathing patterns in real-time could be used to gain further insights into respiratory disorders and refine diagnostic procedures. Several known breathing disorders were professionally role played and captured in a real-time laboratory environment using a noncontact Doppler radar to evaluate the feasibility of this noncontact form of measurement in capturing breathing patterns under different conditions associated with certain breathing disorders. In addition to that, inhalation and exhalation flow patterns under different breathing scenarios were investigated to further support the feasibility of Doppler radar to accurately estimate the tidal volume. The results obtained for both experiments were compared with the gold standard measurement schemes, such as respiration belt and spirometry readings, yielding significant correlations with the Doppler radar-based information. In summary, Doppler radar is highlighted as an alternative approach not only for determining respiration rates, but also for identifying breathing patterns and tidal volumes as a preferred nonwearable alternative to the conventional contact sensing methods.

Noncontact Detection and Analysis of Respiratory Function Using Microwave Doppler Radar

Real-time respiratory measurement with Doppler Radar has an important advantage in the monitoring of certain conditions such as sleep apnoea, sudden infant death syndrome (SIDS), and many other general clinical uses requiring fast nonwearable and non-contact measurement of the respiratory function. In this paper, we demonstrate the feasibility of using Doppler Radar in measuring the basic respiratory frequencies (via fast Fourier transform) for four different types of breathing scenarios: normal breathing, rapid breathing, slow inhalation-fast exhalation, and fast inhalation-slow exhalation conducted in a laboratory environment. A high correlation factor was achieved between the Doppler Radar-based measurements and the conventional measurement device, a respiration strap. We also extended this work from basic signal acquisition to extracting detailed features of breathing function (I : E ratio). This facilitated additional insights into breathing activity and is likely to trigger a number of new applications in respiratory medicine.

Doppler radar in respiratory monitoring: Detection and analysis

This paper presents the preliminary results of our work in detecting respiration using Doppler Radar in the 2.7 GHz operating band. We demonstrate the capability of Doppler Radar in capturing breathing patterns under various breathing forms such as normal breathing, fast breathing, as well as different rate of inhale and exhale. From the captured signals, respiration rate was obtained using Fast Fourier Transform and validated. The proposed approach could potentially be used in number of applications involving breathing rate and breathing pattern analysis via non-contact methods.

Further applications of Doppler radar for non-contact respiratory assessment

This paper further investigates the use of Doppler radar for detecting and identifying certain human respiratory characteristics from observed frequency and phase modulations. Specifically, we show how breathing frequencies can be determined from the demodulated signal leading to identifying abnormalities of breathing patterns using signal derivatives, optimal filtering and standard statistical measures. Specifically, we report results on a robust method for distinguishing cessation of the normal breathing cycle. The proposed approach can have potential application in the management of sudden infant death syndrome(SIDS) and sleep apnea.

A Mobile Cloud Computing Framework Integrating Multilevel Encoding for Performance Monitoring in Telerehabilitation

Recent years have witnessed a surge in telerehabilitation and remote healthcare systems blessed by the emerging low-cost wearable devices to monitor biological and biokinematic aspects of human beings. Although such telerehabilitation systems utilise cloud computing features and provide automatic biofeedback and performance evaluation, there are demands for overall optimisation to enable these systems to operate with low battery consumption and low computational power and even with weak or no network connections. This paper proposes a novel multilevel data encoding scheme satisfying these requirements in mobile cloud computing applications, particularly in the field of telerehabilitation. We introduce architecture for telerehabilitation platform utilising the proposed encoding scheme integrated with various types of sensors. The platform is usable not only for patients to experience telerehabilitation services but also for therapists to acquire essential support from analysis oriented decision support system (AODSS) for more thorough analysis and making further decisions on treatment.

Separation of Doppler radar-based respiratory signatures

Abstract Respiration detection using microwave Doppler radar has attracted significant interest primarily due to its unobtrusive form of measurement. With less preparation in comparison with attaching physical sensors on the body or wearing special clothing, Doppler radar for respiration detection and monitoring is particularly useful for long-term monitoring applications such as sleep studies (i.e. sleep apnoea, SIDS). However, motion artefacts and interference from multiple sources limit the widespread use and the scope of potential applications of this technique. Utilising the recent advances in independent component analysis (ICA) and multiple antenna configuration schemes, this work investigates the feasibility of decomposing respiratory signatures into each subject from the Doppler-based measurements. Experimental results demonstrated that FastICA is capable of separating two distinct respiratory signatures from two subjects adjacent to each other even in the presence of apnoea. In each test scenario, the separated respiratory patterns correlate closely to the reference respiration strap readings. The effectiveness of FastICA in dealing with the mixed Doppler radar respiration signals confirms its applicability in healthcare applications, especially in long-term home-based monitoring as it usually involves at least two people in the same environment (i.e. two people sleeping next to each other). Further, the use of FastICA to separate involuntary movements such as the arm swing from the respiratory signatures of a single subject was explored in a multiple antenna environment. The separated respiratory signal indeed demonstrated a high correlation with the measurements made by a respiratory strap used currently in clinical settings.

Suppression of interference in continuous wave Doppler radar based respiratory measurements

Microwave Doppler radar has received considerable attention as a non-contact form of measuring human respiration; in particular for long term monitoring. One of the main challenges in converting this into a viable application is to suppress or separate the artefacts and other interfering signals from the desired respiration signal using a less complex and practically feasible design for regular and potentially real time use. Existing systems either require complex experimental setups or multiple Doppler radar modules to achieve this. In this paper, we propose an approach based on EMD-ICA and approximate entropy ideas to systematically separate received Doppler shifted signal into distinct components and reconstruct the desired respiration pattern pertaining to respective physiological activity. Indeed this allows suppression of the undesirable artefacts and interference from other competing signals. Practical experiments confirmed comparable performance of the proposed method to the measurements obtained through chest straps which are widely used clinically for monitoring respiration.

Non-contact measurement of respiratory function and deduction of tidal volume

This paper further the investigation of Doppler radar feasibility in measuring the flow in and out due to inhalation and exhalation under different conditions of breathing activities. Three different experiment conditions were designed to investigate the feasibility and consistency of Doppler radar which includes the combination of the states of normal breathing, deep breathing and apnoea state were demonstrated. The obtained Doppler radar signals were correlated and compared with the gold standard medical device, spirometer, yielding a good correlations between both devices. We also demonstrated the calibration of the Doppler radar signal can be performed in a simple manner in order to have a good agreements with the spirometer readings. The measurement of the flow in and out during the breathing activities can be measured accurately under different dynamics of breathing as long as the calibration is performed correctly.

Respiration rate and breathing patterns from Doppler radar measurements

This paper presents an evaluation of microwave Doppler radar used for capturing different types of breathing patterns in addition to the respiration rate. Finding the respiration rate is equally important as identifying abnormal breathing patterns which it could be used to gain a better insight into respiratory disorders. Various known breathing disorders were role played and captured using a non-contact microwave Doppler radar which further supports the feasibility of Doppler radar in obtaining an accurate detection of different types of breathing patterns. The results obtained for all the experiments were compared with a standard measurement apparatus, respiration strap, yielding a good correlations with the Doppler radar signals. In a nutshell, Doppler radar can be potentially used as an alternative approach, not only for finding the respiration rates, but also for identifying respiration patterns replacing the conventional contact methods.

Motion artefact separation in single channel Doppler radar respiration measurement

Direct conversion Doppler radar has the capability to remotely monitor human respiratory activity in a non-contact form. However, the motion or movement from the subject will degrade the acquired respiration signal. As the respiration pattern is one of the essential parameters in respiratory medicine intrinsically containing more information about the respiratory function, it is particularly important to suppress or to separate these motion artefacts in order to reconstruct the corresponding patterns. Experiment results show that EMD-ICA algorithm is capable of separating the mixed respiration signal by recovering the useful information of the breathing pattern as well as the motion signatures using only a single channel measurement when using the source separation algorithm. This reduces the complexity and the cost of the sensing system while removing the undesirable artefacts. A high correlation was also observed from the recovered respiration pattern in comparison to the standard respiration strap for both experiments setup (a seated and a supine position).