Wilhelm von Rosenberg

Enabling R-peak detection in wearable ECG: Combining matched filtering and Hilbert transform

Precise detection of R-peaks is a prerequisite in real-world ECG applications - this is particularly critical for wearable ECG where sensors are typically low resolution and embedded. Such recorded ECG data are typically contaminated by noise, motion artefacts, unbalanced skin-electrode impedance and other physiological signals. These affect the quality of R-peak detection and can consequently lead to failure in the evaluation of physiological functions or a misinterpretation of the state of the body, such as in monitoring stress. While numerous methods for R-peak detection are available for stationary and comparably noise-free ECG, robust DSP software for wearable devices is still emerging. To this end, a new approach which combines matched filtering and Hilbert transform is proposed. The RR-intervals and cross-correlation are used in conjunction to not only automatically locate the R-peaks but also to display the candidate ambiguous peaks via an interactive graphical user interface. The performance of the proposed approach is compared to the well-known Pan-Tompkins algorithm and is evaluated for two types of ECG databases: standard stationary data and low-SNR ECG data obtained from wearable ECG. The proposed method results in a distinctly higher positive predictivity and leads to more satisfying overall outcomes, especially for the critical call of low-SNR data.

Hearables: Multimodal physiological in-ear sensing

Future health systems require the means to assess and track the neural and physiological function of a user over long periods of time, and in the community. Human body responses are manifested through multiple, interacting modalities – the mechanical, electrical and chemical; yet, current physiological monitors (e.g. actigraphy, heart rate) largely lack in cross-modal ability, are inconvenient and/or stigmatizing. We address these challenges through an inconspicuous earpiece, which benefits from the relatively stable position of the ear canal with respect to vital organs. Equipped with miniature multimodal sensors, it robustly measures the brain, cardiac and respiratory functions. Comprehensive experiments validate each modality within the proposed earpiece, while its potential in wearable health monitoring is illustrated through case studies spanning these three functions. We further demonstrate how combining data from multiple sensors within such an integrated wearable device improves both the accuracy of measurements and the ability to deal with artifacts in real-world scenarios.

Resolving Ambiguities in the LF/HF Ratio: LF-HF Scatter Plots for the Categorization of Mental and Physical Stress from HRV

It is generally accepted that the activities of the autonomic nervous system (ANS), which consists of the sympathetic (SNS) and parasympathetic nervous systems (PNS), are reflected in the low- (LF) and high-frequency (HF) bands in heart rate variability (HRV)—while, not without some controversy, the ratio of the powers in those frequency bands, the so called LF-HF ratio (LF/HF), has been used to quantify the degree of sympathovagal balance. Indeed, recent studies demonstrate that, in general: (i) sympathovagal balance cannot be accurately measured via the ratio of the LF- and HF- power bands; and (ii) the correspondence between the LF/HF ratio and the psychological and physiological state of a person is not unique. Since the standard LF/HF ratio provides only a single degree of freedom for the analysis of this 2D phenomenon, we propose a joint treatment of the LF and HF powers in HRV within a two-dimensional representation framework, thus providing the required degrees of freedom. By virtue of the proposed 2D representation, the restrictive assumption of the linear dependence between the activity of the autonomic nervous system (ANS) and the LF-HF frequency band powers is demonstrated to become unnecessary. The proposed analysis framework also opens up completely new possibilities for a more comprehensive and rigorous examination of HRV in relation to physical and mental states of an individual, and makes possible the categorization of different stress states based on HRV. In addition, based on instantaneous amplitudes of Hilbert-transformed LF- and HF-bands, a novel approach to estimate the markers of stress in HRV is proposed and is shown to improve the robustness to artifacts and irregularities, critical issues in real-world recordings. The proposed approach for resolving the ambiguities in the standard LF/HF-ratio analyses is verified over a number of real-world stress-invoking scenarios.

Smart Helmet: Wearable Multichannel ECG and EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet.

Vital signs from inside a helmet: A multichannel face-lead study

It is essential to measure physiological parameters such as heart rate variability and respiratory rate of drivers to evaluate their performance. The results from this measurement can be used to assess the state of body and mind, for instance concentration and stress. However, current systems only work in controlled environments, or sensors obstruct and interfere with operations of the driver. In this study, a face-lead ECG is placed inside a helmet to enhance comfort and convenience in racing scenarios. Multiple electrodes were attached to facial locations, which exhibit good contact with a helmet, and bipolar configurations were examined between the left and right side of the subjects face. Standard and data-driven filtering algorithms were employed to improve the extraction of R peaks from the ECG data. The so-extracted R peaks were subsequently used to estimate heart activity and respiration effort, and the results were compared with standard recording protocols. It is shown that ECG recordings obtained from locations on the lower jaw match closely with conventional recording paradigms (limb-lead ECG), highlighting the potential of vital sign monitoring from within a racing helmet.

Smart helmet: Monitoring brain, cardiac and respiratory activity.

The timing of the assessment of the injuries following a road-traffic accident involving motorcyclists is absolutely crucial, particularly in the events with head trauma. Standard apparatus for monitoring cardiac activity is usually attached to the limbs or the torso, while the brain function is routinely measured with a separate unit connected to the head-mounted sensors. In stark contrast to these, we propose an integrated system which incorporates the two functionalities inside an ordinary motorcycle helmet. Multiple fabric electrodes were mounted inside the helmet at positions featuring good contact with the skin at different sections of the head. The experimental results demonstrate that the R-peaks (and therefore the heart rate) can be reliably extracted from potentials measured with electrodes on the mastoids and the lower jaw, while the electrodes on the forehead enable the observation of neural signals. We conclude that various vital sings and brain activity can be readily recorded from the inside of a helmet in a comfortable and inconspicuous way, requiring only a negligible setup effort.

Self-assessment of surgical ward crisis management using video replay augmented with stress biofeedback

BackgroundWe aimed to explore the feasibility and attitudes towards using video replay augmented with real time stress quantification for the self-assessment of clinical skills during simulated surgical ward crisis management.MethodsTwenty two clinicians participated in 3 different simulated ward based scenarios of deteriorating post-operative patients. Continuous ECG recordings were made for all participants to monitor stress levels using heart rate variability (HRV) indices. Video recordings of simulated scenarios augmented with real time stress biofeedback were replayed to participants. They were then asked to self-assess their performance using an objective assessment tool. Participants attitudes were explored using a post study questionnaire.ResultsUsing HRV stress indices, we demonstrated higher stress levels in novice participants. Self-assessment scores were significantly higher in more experienced participants. Overall, participants felt that video replay and augmented stress biofeedback were useful in self-assessment.ConclusionSelf-assessment using an objective self-assessment tool alongside video replay augmented with stress biofeedback is feasible in a simulated setting and well liked by participants.

Hearables: feasibility of recording cardiac rhythms from head and in-ear locations

Mobile technologies for the recording of vital signs and neural signals are envisaged to underpin the operation of future health services. For practical purposes, unobtrusive devices are favoured, such as those embedded in a helmet or incorporated onto an earplug. However, these locations have so far been underexplored, as the comparably narrow neck impedes the propagation of vital signals from the torso to the head surface. To establish the principles behind electrocardiogram (ECG) recordings from head and ear locations, we first introduce a realistic three-dimensional biophysics model for the propagation of cardiac electric potentials to the head surface, which demonstrates the feasibility of head-ECG recordings. Next, the proposed biophysics propagation model is verified over comprehensive real-world experiments based on head- and in-ear-ECG measurements. It is shown both that the proposed model is an excellent match for the recordings, and that the quality of head- and ear-ECG is sufficient for a reliable identification of the timing and shape of the characteristic P-, Q-, R-, S- and T-waves within the cardiac cycle. This opens up a range of new possibilities in the identification and management of heart conditions, such as myocardial infarction and atrial fibrillation, based on 24/7 continuous in-ear measurements. The study therefore paves the way for the incorporation of the cardiac modality into future ‘hearables’, unobtrusive devices for health monitoring.

Pain Prediction From ECG in Vascular Surgery

Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-D polynomial surface fit, of physiological metrics and numerical pain ratings from patients, in order to model the link between the modulation of cardiovascular responses and pain in varicose vein surgeries. Spectral and structural complexity features found in heart rate variability signals, recorded immediately prior to 17 varicose vein surgeries, are used as pain metrics. The so obtained pain prediction model is validated through a leave-one-out validation, and achieved a Kappa coefficient of 0.72 (substantial agreement) and an area below a receiver operating characteristic curve of 0.97 (almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility of the accurate classification of pain sensitivity, and introduces a mathematical model to aid clinicians in the objective administration of the safest and most cost-effective anaesthetic to individual patients.