Influence of photoplethysmogram signal quality on pulse arrival time during polysomnography

Intervals of low-quality photoplethysmogram (PPG) signals might lead to significant inaccuracies in estimation of pulse arrival time (PAT) during polysomnography (PSG) studies. While PSG is considered to be a “gold standard” test for diagnosing obstructive sleep apnea (OSA), it also enables tracking apnea-related nocturnal blood pressure fluctuations correlated with PAT. Since the electrocardiogram (ECG) is recorded synchronously with the PPG during PSG, it makes sense to use the ECG signal for PPG signal-quality assessment. (1) Objective: to develop a PPG signal-quality assessment algorithm for robust PAT estimation, and investigate the influence of signal quality on PAT during various sleep stages and events such as OSA. (2) Approach: the proposed algorithm uses R and T waves from the ECG to determine approximate locations of PPG pulse onsets. The MESA database of 2055 PSG recordings was used for this study. (3) Results: the proportions of high-quality PPG were significantly lower in apnea-related oxygen desaturation (matched-pairs rc = 0.88 and rc = 0.97, compared to OSA and hypopnea, respectively, when p < 0.001) and arousal (rc = 0.93 and rc = 0.98, when p < 0.001) than in apnea events. The significantly large effect size of interquartile ranges of PAT distributions was between low- and high-quality PPG (p < 0.001, rc = 0.98), and regular and irregular pulse waves (p < 0.001, rc = 0.74), whereas a lower quality of the PPG signal was found to be associated with a higher interquartile range of PAT across all subjects. Suggested PPG signal quality-based PAT evaluation reduced deviations (e.g., rc = 0.97, rc = 0.97, rc = 0.99 in hypopnea, oxygen desaturation, and arousal stages, respectively, when p < 0.001) and allowed obtaining statistically larger differences between different sleep stages and events. (4) Significance: the implemented algorithm has the potential to increase the robustness of PAT estimation in PSG studies related to nocturnal blood pressure monitoring

Induced pain affects auricular and body biosignals: From cold stressor to deep breathing

Pain affects every fifth adult worldwide and is a significant health problem. From a physiological perspective, pain is a protective reaction that restricts physical functions and causes responses in physiological systems. These responses are accessible for evaluation via recorded biosignals and can be favorably used as feedback in active pain therapy via auricular vagus nerve stimulation (aVNS). The aim of this study is to assess the significance of diverse parameters of biosignals with respect to their deflection from cold stressor to deep breathing and their suitability for use as biofeedback in aVNS stimulator. Seventy-eight volunteers participated in two cold pressors and one deep breathing test. Three targeted physiological parameters (RR interval of electrocardiogram, cardiac deflection magnitude ZAC of ear impedance signal, and cardiac deflection magnitude PPGAC of finger photoplethysmogram) and two reference parameters (systolic and diastolic blood pressures BPS and BPD) were derived and monitored. The results show that the cold water decreases the medians of targeted parameters (by 5.6, 9.3%, and 8.0% of RR, ZAC, and PPGAC, respectively) and increases the medians of reference parameters (by 7.1% and 6.1% of BPS and BPD, respectively), with opposite changes in deep breathing. Increasing pain level from relatively mild to moderate/strong with cold stressor varies the medians of targeted and reference parameters in the range from 0.5% to 6.0% (e.g., 2.9% for RR, ZAC and 6.0% for BPD). The physiological footprints of painful cold stressor and relaxing deep breathing were shown for auricular and non-auricular biosignals. The investigated targeted parameters can be used as biofeedback to close the loop in aVNS to personalize the pain therapy and increase its compliance

A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation

Abstract The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF Introduction The detection of brief episode paroxysmal atrial fibrillation (PAF) is an important problem to solve since atrial fibrillation (AF) is a progressive disorder. If not treated, PAF usually becomes more frequent and longer until it becomes permanent Automatic AF detection can be done in different waysone is based on identification of P-wave absence and another on the analysis of RR interval irregularity. Since P-waves are not apparent during AF such knowledge can be combined with RR irregularity information in order to improve the performance of AF detection Recently, there has been a growing interest in developing algorithms for detection of brief AF episodes. A sample entropy based method was proposed that is capable of detecting AF using only 12 consecutive RR intervals A novel detector architecture was recently proposed, where information on P wave presence/absence, heart rate irregularity, and atrial activity analysis was combined, using an artificial neural network as classifier In this study, the proposed method is based on atrial activity extraction using an echo state network (ESN) recently introduced as a unified solution to the problem of QRST cancellation in the presence of substantial variation in beat morphology and/or occasional ectopic beats Methods The main processing steps of the proposed AF detector are illustrated i

Iteracinis optimizavimu pagrįsto akies dugno kraujagyslių trasavimo metodo variantas

Akies dugne matomos smulkios žmogaus kraujagyslės. Iš jų pakitimų galima diagnozuoti įvairias sisteminių ligų komplikacijas. Dėl to daug dėmesio skiriama automatiniams kraujagyslių stebėjimo akies dugno vaizduose algoritmams. Daugelis esamų kraujagyslių trasavimo metodų pradeda trasavimą arba nuo daugelio automatiškai rastų taškų, arba nuo vartotojo interaktyviai pažymėtų taškų. Šiame darbe buvo siekiama plačiau išnagrinėti galimybę pradėti trasavimą nuo vieno automatiškai gautotaško, kurio priklausymo kraujagyslei tikimybė labai didelė. Naudojant straipsnyje aprašytą algoritmą trasavimas pradedamas randant pradinį tašką pagal skirtumo tarp skaitmeninio vaizdo raudono ir žalio RGB komponentų reikšmes. Kraujagyslės trasuojamos keliomis kryptimis nuo pradinio taško. Tolesni kraujagyslės taškai randami ieškant didžiausios tikslo funkcijos reikšmės. Tyrimas leido įsitikinti, kad pradedant kraujagyslių trasavimą nuo vieno taško, galima pasiekti kokybę, palyginamą su kitų kraujagyslių radimo metodų kokybe.Iterative Variant of the Optimisation-Based Eye Fundus Blood Vessel Tracking MethodMartynas Patašius, Vaidotas Marozas, Darius Jegelevičius, Arūnas Lukoševičius SummaryEye fundus is the place where human microvasculature can be observed directly. The vascular changes that can be detected there make it possible to diagnose complications of various systemic diseases. Thus a lot of attention is paid to blood vessel detection in eye fundus images. Most of the currently used blood vessels tracking methods start the tracking either from a set of automatically generated seed points, or from the manually marked seed points. In this study, we tried to explore the possibility to start the tracing from one point that belongs to the blood vessel with a high probability. Using the method described here blood vessel tracking starts from fi nding a seed point according to the difference of red and green RGB components. The next points of the blood vessel are found by searching for the maximal values of the goal function. The study has corroborated that it is possible to achieve a blood vessel detection quality comparable to the quality achieved by other blood vessel detection methods by starting blood vessel tracking from a single point according to the described method

Difference in pulse arrival time at forehead and at finger as a surrogate of pulse transit time

Pulse transit time (PTT) difference (PTTD) to the forehead and finger dynamics are compared to pulse arrival time towards the finger (PATF) dynamics during a tilt table test. Two frequency bands, where different physiological information is expected, are analyzed: low frequency (LF) influenced by both sympathetic and parasympathetic activity, and high frequency (HF) influenced by parasympathetic activity. As PATF, PTTD is influenced by PTT, but in contrast to PATF, PTTD is not influenced by the pre-ejection period (PEP). This is advantaging in certain applications such as arterial stiffness assessment or blood pressure estimation. Results showed higher correlation between PTTD and PATF during rest stages than during tilt stage, when the PEP dynamics have stronger effect in PATF dynamics. This suggests that PTTD variability can potentially be a surrogate of PTT variability that is not influenced by PEP, which is advantaging in the previously mentioned applications. However, further studies must be elaborated in order to evaluate the potential of PTTD in such specific applications

Optimal fiducial points for pulse rate variability analysis from forehead and finger PPG signals

Objective: The aim of this work is to evaluate and compare five fiducialpoints for the temporal location of each pulse wave from forehead and fingerphotoplethysmographic pulse waves signals (PPG) to perform pulse rate variability(PRV) analysis as a surrogate of heart rate variability (HRV) analysis. Approach: Forehead and finger PPG signals were recorded during tilt-table testsimultaneously to the ECG. Artifacts were detected and removed and, five fiducialpoints were computed: apex, middle-amplitude and foot points of the PPG signal,apex point of the first derivative signal and, the intersection point of the tangent tothe PPG waveform at the apex of the derivative PPG signal and the tangent to thefoot of the PPG pulse defined as intersecting tangents method. Pulse period (PP)time intervals series were obtained from both PPG signals and compared to the RRintervals obtained from the ECG. Heart and pulse rate variability signals (HRV andPRV) were estimated and, classical time and frequency domain indices were computed. Main Results: The middle-amplitude point of the PPG signal (nM), the apexpoint of the first derivative (n*A), and the tangents intersection point (nT) are themost suitable fiducial points for PRV analysis, which result in the lowest relativeerrors estimated between PRV and HRV indices, higher correlation coefficients and reliability indexes. Statistically significant differences according to the Wilcoxon testbetween PRV and HRV signals were found for the apex and foot fiducial points ofthe PPG, as well as the lowest agreement between RR and PP series according toBland-Altman analysis. Hence, they have been considered less accurate for variabilityanalysis. In addition, the relative errors are significantly lower fornMandn*Afeaturesby using Friedman statistics with Bonferroni multiple-comparison test and, we proposenMas the most accurate fiducial point. Based on our results, forehead PPG seems toprovide more reliable information for a PRV assessment than finger PPG. Significance: The accuracy of the pulse wave detections depends on the morphologyof the PPG. There is therefore a need to widely define the most accurate fiducial pointto perform a PRV analysis under non-stationary conditions based on different PPGsensor locations and signal acquisition techniques

Lifelogging Data Validation Model for Internet of Things enabled Personalized Healthcare

The rapid advance of the Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of IoT assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse life patterns in an IoT environment, lifelogging personal data contains much uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, it takes lifelogging physical activity as a target to explore the possibility of improving validity of lifelogging data in an IoT based healthcare environment. A rule based adaptive lifelogging physical activity validation model, LPAV-IoT, is proposed for eliminating irregular uncertainties and estimating data reliability in IoT healthcare environments. In LPAV-IoT, a methodology specifying four layers and three modules is presented for analyzing key factors impacting validity of lifelogging physical activity. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on an IoT enabled personalized healthcare platform MHA [38] connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of irregular uncertainty and adaptively indicating the reliability of lifelogging physical activity data on certain condition of an IoT personalized environment

Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application

VME-DWT : an efficient algorithm for detection and elimination of eye blink from short segments of single EEG channel

Objective: Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel. Method: The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel. Results: The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from −8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87). Significance: The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference

Visual Analytics for Health Monitoring and Risk Management in CARRE

With the rise of wearable sensor technologies, an increasing number of wearable health and medical sensors are available on the market, which enables not only people but also doctors to utilise them to monitor people’s health in such a consistent way that the sensors may become people’s lifetime companion. The consistent measurements from a variety of wearable sensors implies that a huge amount of data needs to be processed, which cannot be achieved by traditional processing methods. Visual analytics is designed to promote knowledge discovery and utilisation of big data via mature visual paradigms with well-designed user interactions and has become indispensable in big data analysis. In this paper we introduce the role of visual analytics for health monitoring and risk management in the European Commission funded project CARRE which aims to provide innovative means for the management of cardiorenal diseases with the assistance of wearable sensors. The visual analytics components of timeline and parallel coordinates for health monitoring and of node-link diagrams, chord diagrams and sankey diagrams for risk analysis are presented to achieve ubiquitous and lifelong health and risk monitoring to promote people’s health