Validity of the Polar V800 monitor for measuring heart rate variability in mountain running route conditions.

PURPOSE: This study was conducted to test, in mountain running route conditions, the accuracy of the Polar V800™ monitor as a suitable device for monitoring the heart rate variability (HRV) of runners. METHOD: Eighteen healthy subjects ran a route that included a range of running slopes such as those encountered in trail and ultra-trail races. The comparative study of a V800 and a Holter SEER 12 ECG Recorder™ included the analysis of RR time series and short-term HRV analysis. A correction algorithm was designed to obtain the corrected Polar RR intervals. Six 5-min segments related to different running slopes were considered for each subject. RESULTS: The correlation between corrected V800 RR intervals and Holter RR intervals was very high (r = 0.99, p < 0.001), and the bias was less than 1 ms. The limits of agreement (LoA) obtained for SDNN and RMSSD were (- 0.25 to 0.32 ms) and (- 0.90 to 1.08 ms), respectively. The effect size (ES) obtained in the time domain HRV parameters was considered small (ES < 0.2). Frequency domain HRV parameters did not differ (p > 0.05) and were well correlated (r ≥ 0.96, p < 0.001). CONCLUSION: Narrow limits of agreement, high correlations and small effect size suggest that the Polar V800 is a valid tool for the analysis of heart rate variability in athletes while running high endurance events such as marathon, trail, and ultra-trail races.

Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis.

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and non-linear methods.

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

Assessment of sedation-analgesia by means of Poincaré analysis of the electroencephalogram.

Monitoring the levels of sedation-analgesia may be helpful for managing patient stress on minimally invasive medical procedures. Monitors based on EEG analysis and designed to assess general anesthesia cannot distinguish reliably between a light and deep sedation. In this work, the Poincaré plot is used as a nonlinear technique applied to EEG signals in order to characterize the levels of sedation-analgesia, according to observed categorical responses that were evaluated by means of Ramsay Sedation Scale (RSS). To study the effect of high frequencies due to EMG activity, three different frequency ranges (FR1=0.5-110 Hz, FR2=0.5-30 Hz and FR3=30-110 Hz) were considered. Indexes from power spectral analysis and plasma concentration of propofol and remifentanil were also compared with the bispectral index BIS. An adaptive Neurofuzzy Inference System was applied to model the interaction of the best indexes with respect to RSS score for each analysis, and leave-one-out cross validation method was used. The ability of the indexes to describe the level of sedation-analgesia, according with the RSS score, was evaluated using the prediction probability (Pk). The results showed that the ratio SD1/SD2FR3 contains useful information about the sedation level, and SD1FR2 and SD2FR2 had the best performance classifying response to noxious stimuli. Models including parameters from Poincaré plot emerge as a good estimator of sedation-analgesia levels.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing.

AIM: The present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset. METHODS: Two groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests. RESULTS: Non-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients. CONCLUSION: The regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies.

The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS). Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.

Evaluation of acceleration and deceleration cardiac processes using phase-rectified signal averaging in healthy and idiopathic dilated cardiomyopathy subjects.

The aim of the present study was to investigate the suitability of the Phase-Rectified Signal Averaging (PRSA) method for improved risk prediction in cardiac patients. Moreover, this technique, which separately evaluates acceleration and deceleration processes of cardiac rhythm, allows the effect of sympathetic and vagal modulations of beat-to-beat intervals to be characterized. Holter recordings of idiopathic dilated cardiomyopathy (IDC) patients were analyzed: high-risk (HR), who suffered sudden cardiac death (SCD) during the follow-up; and low-risk (LR), without any kind of cardiac-related death. Moreover, a control group of healthy subjects was analyzed. PRSA indexes were analyzed, for different time scales T and wavelet scales s, from RR series of 24 h-ECG recordings, awake periods and sleep periods. Also, the behavior of these indexes from simulated data was analyzed and compared with real data results. Outcomes demonstrated the PRSA capacity to significantly discriminate healthy subjects from IDC patients and HR from LR patients on a higher level than traditional temporal and spectral measures. The behavior of PRSA indexes agrees with experimental evidences related to cardiac autonomic modulations. Also, these parameters reflect more regularity of the autonomic nervous system (ANS) in HR patients.

Measuring heart rate variability by means of information entropies based on Choi-Williams distribution.

The Shannon entropy theory was applied to the Choi-Williams time-frequency distribution (CWD) of cardiac time series (RR series) in order to extract entropy information in both time and frequency domains. From this distribution, four indexes were defined: (1) instantaneous partial entropy; (2) spectral partial entropy; (3) instantaneous complete entropy; (4) spectral complete entropy. These indexes were used for analyzing the heart rate variability of ischemic cardiomyopathy patients (ICM) with different sudden cardiac death risk. The results have shown that the values of these indexes tend to decrease, with different proportion, when the severity of pathological condition increases. Statistical differences (p-value < 0.0005) of these indexes were found comparing low risk and high risk of cardiac death during night and between daytime and nighttime periods of ICM patients. Finally, these indexes have demonstrated to be useful tools to quantify the different complex components of the cardiac time series.

Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis.

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyopathy patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyopathies were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopathies, obtaining an accuracy of 85.7%.

Symbolic dynamics to discriminate healthy and ischaemic dilated cardiomyopathy populations: an application to the variability of heart period and QT interval.

Myocardial ischaemia is hypothesized to stimulate the cardiac sympathetic excitatory afferents and, therefore, the spontaneous changes of heart period (approximated as the RR interval), and the QT interval in ischaemic dilated cardiomyopathy (IDC) patients might reflect this sympathetic activation. Symbolic analysis is a nonlinear and powerful tool for the extraction and classification of patterns in time-series analysis, which implies a transformation of the original series into symbols and the construction of patterns with the symbols. The aim of this work was to investigate whether symbolic transformations of RR and QT cardiac series can provide a better separation between IDC patients and healthy control (HC) subjects compared with traditional linear measures. The variability of these cardiac series was studied during daytime and night-time periods and also during the complete 24 h recording over windows of short data sequences of approximately 5 min. The IDC group was characterized by an increase in the occurrence rate of patterns without variations (0 V%) and a reduction in the occurrence rate of patterns with one variation (1 V%) and two variations (2 V%). Concerning the RR variability during the daytime, the highest number of patterns had 0 V%, whereas the rates of 1 V% and 2 V% were lower. During the night, 1 V% and 2 V% increased at the expense of diminishing 0 V%. Patterns with and without variations between consecutive symbols were able to increase the separation between the IDC and HC groups, allowing accuracies higher than 80%. With regard to entropy measures, an increase in RR regularity was associated with cardiac disease described by accuracy >70% in the RR series and by accuracy >60% in the QTc series. These results could be associated with an increase in the sympathetic tone in IDC patients.

Correntropy measures to detect daytime sleepiness from EEG signals.

Excessive daytime sleepiness (EDS) is one of the main symptoms of several sleep related disorders and has a great impact on patients' lives. While many studies have been carried out in order to assess daytime sleepiness, automatic EDS detection still remains an open problem. In this work, a novel approach to this issue based on correntropy function analysis of EEG signals was proposed in order to detect patients suffering from EDS. Multichannel EEG signals were recorded during five Maintenance of Wakefulness Tests (MWT) and Multiple Sleep Latency Tests (MSLT) alternated throughout the day for patients suffering from sleep disordered breathing (SDB). A group of 20 patients with EDS was compared with a group of 20 patients without daytime sleepiness (WDS), by analyzing 60 s EEG windows in a waking state. Measures obtained from the cross-correntropy function (CCORR) and auto-correntropy function (ACORR) were calculated in the EEG frequency bands: δ, 0.1-4 Hz; θ, 4-8 Hz; α, 8-12 Hz; ß, 12-30 Hz; total band TB, 0.1-45 Hz. These functions permitted the quantification of complex signal properties and the non-linear couplings between different areas of the scalp. Statistical differences between EDS and WDS groups were mainly found in the ß band during MSLT events (p-value < 0.0001). The WDS group presented more complexity in the occipital zone than the EDS group, while a stronger nonlinear coupling between the occipital and frontal regions was detected in EDS patients than in the WDS group. At best, ACORR and CCORR measures yielded sensitivity and specificity above 80% and the area under ROC curve (AUC) was above 0.85 in classifying EDS and WDS patients. These performances represent an improvement with respect to classical EEG indices applied in the same database (sensitivity and specificity were never above 80% and AUC was under 0.75).

Evaluation of very low amplitude intra-QRS potentials during the initial minutes of acute transmural myocardial ischemia.

INTRODUCTION: Low-level electrocardiographic changes from depolarization wavefront may accompany acute myocardial ischemia. The purpose of this study was to assess the changes of microvolt amplitude intra-QRS potentials induced by elective percutaneous coronary interventions (PCI). METHODS: Fifty-seven patients with balloon inflation periods ranging from 3.1 to 7.3 minutes (4.9±0.7 min) were studied. Nine leads continuous high-resolution ECG before and during PCI were recorded and signal-averaged. Abnormal intra-QRS at microvolt level (µAIQP) were obtained using a signal modeling approach. µAIQP, R-wave amplitude and QRS duration were measured in the processed ECG during baseline and PCI episodes. RESULTS: The mean µAIQP amplitude significantly decreased for each of the standard 12 leads at the PCI event respect to baseline. Left anterior descending artery (LAD) occlusion resulted in a decrease µAIQP in both the precordial leads and the limb leads, while right coronary (RCA) and left circumflex (LCx) arteries occlusions mainly affected limb leads. R-wave amplitude increased during PCI in RCA and LCx groups in lead III but decreased in the precordial leads, while the amplitude decreased in the LAD group in lead III. The average duration of the QRS augmented in groups RCA and LCx but not in the LAD group. CONCLUSIONS: Abnormal intra-QRS potentials at the level of µV provide an excellent tool to characterize the very-low amplitude fragmentation of the QRS complex and its changes due to ischemic injuries. µAIQP shows promise as a new ECG index to measure electrophysiologic changes associated with acute myocardial ischemia.

Multiscale complexity analysis of the cardiac control identifies asymptomatic and symptomatic patients in long QT syndrome type 1.

The study assesses complexity of the cardiac control directed to the sinus node and to ventricles in long QT syndrome type 1 (LQT1) patients with KCNQ1-A341V mutation. Complexity was assessed via refined multiscale entropy (RMSE) computed over the beat-to-beat variability series of heart period (HP) and QT interval. HP and QT interval were approximated respectively as the temporal distance between two consecutive R-wave peaks and between the R-wave apex and T-wave end. Both measures were automatically taken from 24-hour electrocardiographic Holter traces recorded during daily activities in non mutation carriers (NMCs, n = 14) and mutation carriers (MCs, n = 34) belonging to a South African LQT1 founder population. The MC group was divided into asymptomatic (ASYMP, n = 11) and symptomatic (SYMP, n = 23) patients according to the symptom severity. Analyses were carried out during daytime (DAY, from 2PM to 6PM) and nighttime (NIGHT, from 12PM to 4AM) off and on beta-adrenergic blockade (BBoff and BBon). We found that the complexity of the HP variability at short time scale was under vagal control, being significantly increased during NIGHT and BBon both in ASYMP and SYMP groups, while the complexity of both HP and QT variability at long time scales was under sympathetic control, being smaller during NIGHT and BBon in SYMP subjects. Complexity indexes at long time scales in ASYMP individuals were smaller than those in SYMP ones regardless of therapy (i.e. BBoff or BBon), thus suggesting that a reduced complexity of the sympathetic regulation is protective in ASYMP individuals. RMSE analysis of HP and QT interval variability derived from routine 24-hour electrocardiographic Holter recordings might provide additional insights into the physiology of the cardiac control and might be fruitfully exploited to improve risk stratification in LQT1 population.

Algorithm for automatic forced spirometry quality assessment: technological developments.

We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.

Clinical decision support system to enhance quality control of spirometry using information and communication technologies.

BACKGROUND: We recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings. OBJECTIVE: The objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry. METHODS: The CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes. RESULTS: The CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity. CONCLUSIONS: Consequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting.

Interaction between EEG and drug concentration to predict response to noxious stimulation during sedation-analgesia: effect of the A118G genetic polymorphism.

The level of sedation in patients undergoing medical procedures is affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The presence of the A118G single nucleotide polymorphism (SNP) in the OPRM1 gene affects the requirements of opioids for patients undergoing sedation-analgesia. The purpose of this work is to evaluate the influence of the SNP A118G in OPRM1 on EEG measures for the prediction of the response to pain stimulation during endoscopy procedure. The proposed measures were based on power spectral density and auto-mutual information function. It was found that the statistical performances of the EEG measures improved when the presence of the SNP was taken into account (prediction probability Pk>0.9).

Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals.

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient (ρ), mean of coherence function (C), and rate of absolute error (RAE). All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with ρ>0.85, C>0.8, and RAE<0.5. These values were significantly better than the performance of LMS adaptive filter (ρ<0.85, C<0.6, and RAE>1).

Characterization of the respiratory pattern variability of patients with different pressure support levels.

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

Analysis of epileptic EEG signals in children by symbolic dynamics.

Epilepsy is one of the most prevalent neurological disorders among children. The study of surface EEG signals in patients with epilepsy by techniques based on symbolic dynamics can provide new insights into the epileptogenic process and may have considerable utility in the diagnosis and treatment of epilepsy. The goal of this work was to find patterns from a methodology based on symbolic dynamics to characterize seizures on surface EEG in pediatric patients with intractable epilepsy. A total of 76 seizures were analyzed by their pre-ictal, ictal and post-ictal phases. An analytic signal envelope algorithm was applied to each EEG segment and its performance was evaluated. Several variables were defined from the distribution of words constructed on the EEG transformed into symbols. The results showed strong evidences of detectable non-linear changes in the EEG dynamics from pre-ictal to ictal phase and from ictal to post-ictal phase, with an accuracy higher than 70%.