Hilbert-Huang transform for analysis of heart rate variability in cardiac health

Prognostic Properties of Instantaneous Amplitudes Maxima of Earth Surface Tremor

A method is proposed for analyzing the tremor of the earth's surface, measured by GPS, in order to highlight prognostic effects. The method is applied to the analysis of daily time series of vertical displacements in Japan. The network of 1047 stations is divided into 15 clusters. The Huang Empirical Mode Decomposition (EMD) is applied to the time series of the principal components from the clusters, with subsequent calculation of instantaneous amplitudes using the Hilbert transform. To ensure the stability of estimates of the waveforms of the EMD decomposition, 1000 independent additive realizations of white noise of limited amplitude were averaged before the Hilbert transform. Using a parametric model of the intensities of point processes, we analyze the connections between the instants of sequences of times of the largest local maxima of instantaneous amplitudes, averaged over the number of clusters and the times of earthquakes in the vicinity of Japan with minimum magnitude thresholds of 5.5 for the time interval 2012-2023. It is shown that the sequence of the largest local maxima of instantaneous amplitudes significantly more often precedes the moments of time of earthquakes (roughly speaking, has an "influence") than the reverse "influence" of earthquakes on the maxima of amplitudes.

Spectral Analysis of Heart Rate Variability Based on the Hilbert-Huang Method

Analysis of heart rate variability (HRV) is widely used for noninvasive assessment of the state of the regulation systems of the heart. The aim of this study was to evaluate the capabilities of the Hilbert-Huang method for calculating spectral parameters of HRV in comparison with the commonly used Fourier analysis. Fourier analysis allows estimation of averaged spectral amplitudes and power of HRV oscillations in fixed frequency intervals, which are associated with the activity of sympathetic, parasympathetic, and humoral regulation systems. Using the Hilbert-Huang method, we revealed four spectral components, described by Gaussian functions, in which HRV oscillations are concentrated, and showed the absence of fixed boundaries between them. The obtained energy quantitative characteristics of the spectral components of heart rhythm oscillations can serve as the basis for diagnostic methods of heart rhythm regulation, supplementing the commonly used ones.

A Survey of IoT-Based Fall Detection for Aiding Elderly Care: Sensors, Methods, Challenges and Future Trends

Remote monitoring of a fall condition or activities and daily life (ADL) of elderly patients has become one of the essential purposes for modern telemedicine. Internet of Things (IoT) and artificial intelligence (AI) techniques, including machine and deep learning models, have been recently applied in the medical field to automate the diagnosis procedures of abnormal and diseased cases. They also have many other applications, including the real-time identification of fall accidents in elderly patients. The goal of this article is to review recent research whose focus is to develop AI algorithms and methods of fall detection systems (FDS) in the IoT environment. In addition, the usability of different sensor types, such as gyroscopes and accelerometers in smartwatches, is described and discussed with the current limitations and challenges for realizing successful FDSs. The availability problem of public fall datasets for evaluating the proposed detection algorithms are also addressed in this study. Finally, this article is concluded by proposing advanced techniques such as lightweight deep models as one of the solutions and prospects of futuristic smart IoT-enabled systems for accurate fall detection in the elderly.

Hybrid-Pattern Recognition Modeling with Arrhythmia Signal Processing for Ubiquitous Health Management

We established a web-based ubiquitous health management (UHM) system, "ECG4UHM", for processing ECG signals with AI-enabled models to recognize hybrid arrhythmia patterns, including atrial premature atrial complex (APC), atrial fibrillation (AFib), ventricular premature complex (VPC), and ventricular tachycardia (VT), versus normal sinus rhythm (NSR). The analytical model coupled machine learning methods, such as multiple layer perceptron (MLP), random forest (RF), support vector machine (SVM), and naive Bayes (NB), to process the hybrid patterns of four arrhythmia symptoms for AI computation. The data pre-processing used Hilbert-Huang transform (HHT) with empirical mode decomposition to calculate ECGs' intrinsic mode functions (IMFs). The area centroids of the IMFs' marginal Hilbert spectrum were suggested as the HHT-based features. We engaged the MATLAB^TM compiler and runtime server in the ECG4UHM to build the recognition modules for driving AI computation to identify the arrhythmia symptoms. The modeling extracted the crucial data sets from the MIT-BIH arrhythmia open database. The validated models, including the premature pattern (i.e., APC-VPC) and the fibril-rapid pattern (i.e., AFib-VT) against NSR, could reach the best area under the curve (AUC) of the receiver operating characteristic (ROC) of approximately 0.99. The models for all hybrid patterns, without VPC versus AFib and VT, achieved an average accuracy of approximately 90%. With the prediction test, the respective AUCs of the NSR and APC versus the AFib, VPC, and VT were 0.94 and 0.93 for the RF and SVM on average. The average accuracy and the AUC of the MLP, RF, and SVM models for APC-VT reached the value of 0.98. The self-developed system with AI computation modeling can be the backend of the intelligent social-health system that can recognize hybrid arrhythmia patterns in the UHM and home-isolated cares.

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors.

AIoT-Enabled Rehabilitation Recognition System-Exemplified by Hybrid Lower-Limb Exercises

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.

Influence of Sliding Time Window Size Selection Based on Heart Rate Variability Signal Analysis on Intelligent Monitoring of Noxious Stimulation under Anesthesia

In recent decades, little progress of objective evaluation of pain and noxious stimulation has been achieved under anesthesia. Some researches based on medical signals have failed to provide a general understanding of this problem. This paper presents a feature extraction method for heart rate variability signals, aiming at further improving the evaluation of noxious stimulation. In the process of data processing, the empirical mode decomposition is used to decompose and recombine heart rate variability signals, and the sliding time window approach is used to extract the signal features of noxious stimulation, respectively. The influence of window size on feature extraction is studied by changing the window size. By comparing the results, the feature extraction in the process of data processing is valuable, and the selection of window size has a significant impact. With the increase of selected window sizes, we can get better detection results. But for the best choice of window size, to ensure the accuracy of the results and to make it easy to use, then, we need to get just a suitable window size.

An Intelligent Virtual-Reality System With Multi-Model Sensing for Cue-Elicited Craving in Patients With Methamphetamine Use Disorder

Methamphetamine abuse is getting worse amongst the younger population. While there is methadone or buprenorphine harm-reduction treatment for heroin addicts, there is no drug treatment for addicts with methamphetamine use disorder (MUD). Recently, non-medication treatment, such as the cue-elicited craving method integrated with biofeedback, has been widely used. Further, virtual reality (VR) is proposed to simulate an immersive virtual environment for cue-elicited craving in therapy. In this study, we developed a VR system equipped with flavor simulation for the purpose of inducing cravings for MUD patients in therapy. The VR system was integrated with multi-model sensors, such as an electrocardiogram (ECG), galvanic skin response (GSR) and eye tracking to measure various physiological responses from MUD patients in the virtual environment. The goal of the study was to validate the effectiveness of the proposed VR system in inducing the craving of MUD patients via the physiological data. Clinical trials were performed with 20 MUD patients and 11 healthy subjects. VR stimulation was applied to each subject and the physiological data was measured at the time of pre-VR stimulation and post-VR stimulation. A variety of features were extracted from the raw data of heart rate variability (HRV), GSR and eye tracking. The results of statistical analysis found that quite a few features of HRV, GSR and eye tracking had significant differences between pre-VR stimulation and post-VR stimulation in MUD patients but not in healthy subjects. Also, the data of post-VR stimulation showed a significant difference between MUD patients and healthy subjects. Correlation analysis was made and several features between HRV and GSR were found to be correlated. Further, several machine learning methods were applied and showed that the classification accuracy between MUD and healthy subjects at post-VR stimulation attained to 89.8%. In conclusion, the proposed VR system was validated to effectively induce the drug craving in MUD patients.

An improved method to evaluate heart rate variability based on time-variant cardiorespiratory relation

Frequency domain analysis of heart rate variability (HRV) is a noninvasive method to evaluate the autonomic nervous system (ANS), but the traditional parameters of HRV, i.e., the power spectra of the high-frequency (HF) and low-frequency bands (LF), cannot estimate the activity of the parasympathetic (PNS) and sympathetic nervous systems (SNS) well. The aim of our study was to provide a corrected method to better distinguish the contributions of the PNS and SNS in the HRV spectrum. Respiration has a gating effect on cardiac vagal efferent activity, which induces respiration-locked heart rate (HR) changes because of the fast effect of the PNS. So the respiration-related heart rate (HR_r) is closely related to PNS activity. In this study, HR was decomposed into HR_r and the respiration-unrelated component (HR_ru) based on empirical mode decomposition (EMD) and the relationship between HR and respiration. Time-frequency analysis of HR_r and HR_ru was defined as HF_r and LF_ru, respectively, with specific adaptive bands for every signal. Two experimental data sets, representing SNS and PNS activation, respectively, were used for efficiency analysis of our method. Our results show that the corrected HRV predicted ANS activity well. HF_r could be an index of PNS activity, LF_ru mainly reflected SNS activity, and LF_ru/HF_r could be more accurate in representing the sympathovagal balance.NEW & NOTEWORTHY This study includes the time-varying relationship between respiration and heart rate in the analysis of heart rate variability. Correction for low-frequency and high-frequency components based on respiration significantly improved evaluation of the sympathetic and parasympathetic nervous systems.

Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability

PRIMARY OBJECTIVE

The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality.

DESIGN AND METHODS

Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions.

RESULTS

Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%.

CONCLUSIONS

HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.

Transdermal Optical Imaging Reveal Basal Stress via Heart Rate Variability Analysis: A Novel Methodology Comparable to Electrocardiography

The present study examined the validity of a novel physiological measurement technology called transdermal optical imaging (TOI) technology at assessing basal stress. This technology conveniently, contactlessly, and remotely measures facial blood flow changes using a conventional digital video camera. We compared data from TOI against the pulse data collected from the FDA approved BIOPAC system. One hundred thirty-six healthy adults participated in the study. We found that TOI measurements of heart rate and heart rate variability (HRV), which reflects basal stress, corresponded strongly to those obtained from BIOPAC. These findings indicate that TOI technology is a viable method to monitor heart rate and HRV not only accurately but also conveniently, contactlessly, and remotely. Further, measures of HRV obtained via TOI serves as a valid index of basal stress. Potential applications of this technology in psychological research and other fields are discussed.

Five multiresolution-based calcium volume measurement techniques from coronary IVUS videos: A comparative approach

BACKGROUND AND OBJECTIVE

Fast intravascular ultrasound (IVUS) video processing is required for calcium volume computation during the planning phase of percutaneous coronary interventional (PCI) procedures. Nonlinear multiresolution techniques are generally applied to improve the processing time by down-sampling the video frames.

METHODS

This paper presents four different segmentation methods for calcium volume measurement, namely Threshold-based, Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) embedded with five different kinds of multiresolution techniques (bilinear, bicubic, wavelet, Lanczos, and Gaussian pyramid). This leads to 20 different kinds of combinations. IVUS image data sets consisting of 38,760 IVUS frames taken from 19 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/sec.). The performance of these 20 systems is compared with and without multiresolution using the following metrics: (a) computational time; (b) calcium volume; (c) image quality degradation ratio; and (d) quality assessment ratio.

RESULTS

Among the four segmentation methods embedded with five kinds of multiresolution techniques, FCM segmentation combined with wavelet-based multiresolution gave the best performance. FCM and wavelet experienced the highest percentage mean improvement in computational time of 77.15% and 74.07%, respectively. Wavelet interpolation experiences the highest mean precision-of-merit (PoM) of 94.06 ± 3.64% and 81.34 ± 16.29% as compared to other multiresolution techniques for volume level and frame level respectively. Wavelet multiresolution technique also experiences the highest Jaccard Index and Dice Similarity of 0.7 and 0.8, respectively. Multiresolution is a nonlinear operation which introduces bias and thus degrades the image. The proposed system also provides a bias correction approach to enrich the system, giving a better mean calcium volume similarity for all the multiresolution-based segmentation methods. After including the bias correction, bicubic interpolation gives the largest increase in mean calcium volume similarity of 4.13% compared to the rest of the multiresolution techniques. The system is automated and can be adapted in clinical settings.

CONCLUSIONS

We demonstrated the time improvement in calcium volume computation without compromising the quality of IVUS image. Among the 20 different combinations of multiresolution with calcium volume segmentation methods, the FCM embedded with wavelet-based multiresolution gave the best performance.

Lead toxicity promotes autonomic dysfunction with increased chemoreceptor sensitivity

Mortality and morbidity by toxic metals is an important issue of occupational health. Lead is an ubiquitous heavy metal in our environment despite having no physiological role in biological systems. Being an homeostatic controller is expected that the autonomic nervous system would show a degree of impairment in lead toxicity. In fact, sympathoexcitation associated to high blood pressure and tachypnea has been described together with baroreflex dysfunction. However, the mechanisms underlying the autonomic dysfunction and the interplay between baro- and chemoreflex are not yet fully clarified. The angiotensinogenic PVN-NTS axis (paraventricular nucleus of the hypothalamus - nucleus tractus solitarius axis) is a particularly important neuronal pathway that could be responsible for the autonomic dysfunction and the cardiorespiratory impairment in lead toxicity. Within the current work, we addressed in vivo, baro- and chemoreceptor reflex behaviour, before and after central angiotensin inhibition, in order to better understand the cardiorespiratory autonomic mechanisms underlying the toxic effects of long-term lead exposure. For that, arterial pressure, heart rate, respiratory rate, sympathetic and parasympathetic activity and baro- and chemoreceptor reflex profiles of anaesthetized young adult rats exposed to lead, from foetal period to adulthood, were evaluated. Results showed increased chemosensitivity together with baroreceptor reflex impairment, sympathetic over-excitation, hypertension and tachypnea. Chemosensitivity and sympathetic overexcitation were reversed towards normality values by NTS treatment with A-779, an angiotensin (1-7) antagonist. No parasympathetic changes were observed before and after A-799 treatment. In conclusion, angiotensin (1-7) at NTS level is involved in the autonomic dysfunction observed in lead toxicity. The increased sensitivity of chemoreceptor reflex expresses the clear impairment of autonomic outflow to the cardiovascular and respiratory systems induced by putative persistent, long duration, alert reaction evoked by the long term exposure to lead toxic effects. The present study brings new insights on the central mechanisms implicated in the autonomic dysfunction induced by lead exposure which are relevant for the development of additional therapeutic options to tackle lead toxicity symptoms.

Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability

The complex fluctuations in heart rate variability (HRV) reflect cardiac autonomic modulation and are an indicator of congestive heart failure (CHF). This paper proposes a novel nonlinear approach to HRV investigation, the multi dynamic trend analysis (MDTA) method, based on the empirical mode decomposition algorithm of the Hilbert-Huang transform combined with a variable-sized sliding-window method. Electrocardiographic signal data obtained from the PhysioNet database were used. These data were from subjects with CHF (mean age = 59.4 ± 8.4), an age-matched elderly healthy control group (59.3 ± 10.6), and a healthy young group (30.3 ± 4.8); the HRVs of these subjects were processed using the MDTA method, time domain analysis, and frequency domain analysis. Among all HRV parameters, the MDTA absolute value slope (MDTS) and MDTA deviation (MDTD) exhibited the greatest area under the curve (AUC) of the receiver operating characteristics in distinguishing between the CHF group and the healthy controls (AUC = 1.000) and between the healthy elderly subject group and the young subject group (AUC = 0.834 ± 0.067 for MDTS; 0.837 ± 0.066 for MDTD). The CHF subjects presented with lower MDTA indices than those of the healthy elderly subject group. Furthermore, the healthy elderly subjects exhibited lower MDTA indices than those of the young controls. The MDTA method can adaptively and automatically identify the intrinsic fluctuation on variable temporal and spatial scales when investigating complex fluctuations in the cardiac autonomic regulation effects of aging and CHF.

The association between prolonged sleep onset latency and heart rate dynamics among young sleep-onset insomniacs and good sleepers

A blunting of heart rate (HR) reduction during sleep has been reported to be associated with increased all-cause mortality. An increased incident of cardiovascular events has been observed in patients with insomnia but the relationship between nighttime HR and insomnia remains unclear. Here we investigated the HR patterns during the sleep onset period and its association with the length of sleep onset latency (SOL). Nineteen sleep-onset insomniacs (SOI) and 14 good sleepers had their sleep analyzed. Linear regression and nonlinear Hilbert-Huang transform (HHT) of the HR slope were performed in order to analyze HR dynamics during the sleep onset period. A significant depression in HR fluctuation was identified among the SOI group during the sleep onset period when linear regression and HHT analysis were applied. The magnitude of the HR reduction was associated with both polysomnography-defined and subjective SOL; moreover, we found that the linear regression and HHT slopes of the HR showed great sensitivity with respect to sleep quality. Our findings indicate that HR dynamics during the sleep onset period are sensitive to sleep initiation difficulty and respond to the SOL, which indicates that the presence of autonomic dysfunction would seem to affect the progress of falling asleep.

Assessing in vitro cytotoxicity of cell micromotion by Hilbert-Huang transform

Electric cell-substrate impedance sensing (ECIS) is a powerful instrument for quantifying cell behavior in tissue culture. As cells attach and spread on the sensing electrode, they restrict the current flow and hence cause the increase of electrical impedance. Furthermore, cell motion may reveal itself as electrical fluctuations, which are always associated with living cells and continue even when the cells become fully confluent. The impedance fluctuation is attributed to incessant changes in the size of the cell-substrate space as cells persistently rearrange their cell-substrate adhesion sites. The magnitude of this sort of vertical motion detected by ECIS is of the order of nanometers and referred to as micromotion. In this study, Hilbert-Huang Transform was used as a micromotion analysis tool to distinguish the in vitro cytotoxicity of human umbilical vein endothelial cells (HUVECs) exposed to low levels of cytochalasin B. Hilbert-Huang Transform consists of two procedures: the empirical mode decomposition (EMD) and the Hilbert Transform. The measured impedance fluctuations due to cell micromotion were decomposed into several intrinsic mode functions (IMFs) by EMD, and then these IMFs were transferred to instantaneous frequencies by Hilbert Transform. Both amplitude and phase of instantaneous frequencies were expressed as a time-frequency spectrum, called Hilbert spectrum, which displayed different distribution pattern in response to different cytochalasin B concentration. The total instantaneous energy (IE) of each IMF was also calculated to quantify the spectral difference. In addition to the observation of a dose-dependent relationship, the IE value of the first IMF at 0.1 μM decreased to about 48% of the control value and significantly distinguished the cytotoxic effect of 0.1 μM of cytochalasin B (P<0.05).

Toward Capturing Momentary Changes of Heart Rate Variability by a Dynamic Analysis Method

The analysis of heart rate variability (HRV) has been performed on long-term electrocardiography (ECG) recordings (12~24 hours) and short-term recordings (2~5 minutes), which may not capture momentary change of HRV. In this study, we present a new method to analyze the momentary HRV (mHRV). The ECG recordings were segmented into a series of overlapped HRV analysis windows with a window length of 5 minutes and different time increments. The performance of the proposed method in delineating the dynamics of momentary HRV measurement was evaluated with four commonly used time courses of HRV measures on both synthetic time series and real ECG recordings from human subjects and dogs. Our results showed that a smaller time increment could capture more dynamical information on transient changes. Considering a too short increment such as 10 s would cause the indented time courses of the four measures, a 1-min time increment (4-min overlapping) was suggested in the analysis of mHRV in the study. ECG recordings from human subjects and dogs were used to further assess the effectiveness of the proposed method. The pilot study demonstrated that the proposed analysis of mHRV could provide more accurate assessment of the dynamical changes in cardiac activity than the conventional measures of HRV (without time overlapping). The proposed method may provide an efficient means in delineating the dynamics of momentary HRV and it would be worthy performing more investigations.

Heart rate variability analysis using robust period detection

Objective

Heart rate variability (HRV) analysis, which is an important tool for activity assessment of the cardiac autonomic nervous system, very often includes the estimation of power spectra for series of interbeat intervals (IBI). Ectopic beats and artifacts have a destructive effect on the standard methods (Fourier transform, FFT) for frequency analysis. This study investigates an alternative method for calculation of the periodogram using a robust period detection (RPD).

Method

Error free IBI series of 5 minutes for 221 subjects during one day were artificially distorted by randomly changing IBI values by ±15-40%. The low to high frequency rate (LF/HF) were calculated from periodograms estimated by the FFT, RPD and Lomb (LSP) methods for both error free and distorted series and for series with removed beats. Log transformed LF/HF values for series with distorted/removed beats were compared to undistorted values by linear regression.

Results

For series with 10% of distorted IBI values the regression analysis between distorted and undistorted series showed a goodness of fit, coefficient and intercept of 0.98, 0.94 and 0.02, respectively. In comparison, the values of these parameters were (0.34, 0.46, -1.61) and (0.28, 0.42,-1.32) for the FFT and LSP methods, respectively. Similarly, the comparison between series with removed and undistorted beats yielded goodness of fit, coefficient and intercept of (0.98, 0.96, -0.01), (0.93, 0.78, -0.02) and (0.98, 0.95, 0.19) for RPD, FFT and LSP, respectively.

Conclusion

The RPD method demonstrated superior performance compared to the FFT and LSP method by estimation of power spectral characteristics for HRV analysis.

Electronic supplementary material

The online version of this article (doi:10.1186/1475-925X-13-138) contains supplementary material, which is available to authorized users.

Frequency range extension of spectral analysis of pulse rate variability based on Hilbert-Huang transform

Heart rate variability (HRV) is a well-accepted indicator for neural regulatory mechanisms in cardiovascular circulation. Its spectrum analysis provides the powerful means of observing the modulation between sympathetic and parasympathetic nervous system. The timescale of HRV is limited by discrete beat-to-beat time intervals; therefore, the exploration region of frequency band of HRV spectrum is relatively narrow. It had been proved that pulse rate variability (PRV) is a surrogate measurement of HRV in most of the circumstances. Moreover, arterial pulse wave contains small oscillations resulting from complex regulation of cardiac pumping function and vascular tone at higher frequency range. This study proposed a novel instantaneous PRV (iPRV) measurement based on Hilbert-Huang transform. Fifteen healthy subjects participated in this study and received continuous blood pressure wave recording in supine and passive head-up tilt. The result showed that the very-high-frequency band (0.4-0.9 Hz) varied during head-up tilt and had strong correlation (r = 0.77) with high-frequency band and medium correlation (r = 0.643) with baroreflex sensitivity. The very-high-frequency band of iPRV helps for the exploration of non-stationary autoregulation and provides the non-stationary spectral evaluation of HRV without distortion or information loss.

Quantification of spontaneous and evoked HFO's in SEEG recording and prospective for pre-surgical diagnostics. Case study

High frequency oscillations (HFO) in stereo electroencephalographic (SEEG) signals have been recently the focus of attention as biomarkers that can have potential predictive power for the spatial location and possibly the timing of the onset of epileptic seizures. In this work we present a case study where we compare two quantitative paradigms for automated detection of biomarkers, one based on spontaneous SEEG recordings of HFOs and the other using activity induced by direct electrical stimulation (relative Phase Clustering Index algorithm). We compare the performance of these automated methods with manually detected HFO ripples by a trained EEG analyst and explore their potential diagnostic relevance. Intracranial recordings from patients undergoing pre-surgical evaluation are processed with a combination of morphological filtering and the analysis of the auto-correlation function. The results were compared to those obtained by visual inspection and to results from an active paradigm involving stimulation with 20 Hz trains of biphasic pulses. The quantity of HFOs, estimated automatically, or "rippleness", was found to correspond to the findings of a trained EEG analyst. The relative phase clustering index (rPCI) obtained using periodic stimulation appeared to be associated with the closeness to the seizure onset zone (SOZ) detected from ictal epochs. The HFO estimates were also indicative for the SOZ but with less specificity.