Xosé A. Vila

Detecting Sleep Apnea by Heart Rate Variability Analysis: Assessing the Validity of Databases and Algorithms

Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. However, for validation purposes, it is obvious and evident the lack of a common standard database, worldwide recognized and accepted by the scientific community. In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.

An open source tool for heart rate variability spectral analysis

In this paper we describe a software package for developing heart rate variability analysis. This package, called RHRV, is a third party extension for the open source statistical environment R, and can be freely downloaded from the R-CRAN repository. We review the state of the art of software related to the analysis of heart rate variability (HRV). Based upon this review, we motivate the development of an open source software platform which can be used for developing new algorithms for studying HRV or for performing clinical experiments. In particular, we show how the RHRV package greatly simplifies and accelerates the work of the computer scientist or medical specialist in the HRV field. We illustrate the utility of our package with practical examples.

Nocturnal evolution of heart rate variability indices in sleep apnea

Heart rate variability (HRV) is a valuable clinical tool in diagnosing multiple diseases. This paper presents the results of a spectral HRV analysis conducted with 46 patients. HRV indices for the whole night show differences among patients with severe and mild apnea, and healthy subjects. These differences also appear when performing the analysis over 5-min intervals, regarding apneas being present or not in the intervals. Differences were also observed when analyzing the HRV nocturnal evolution. Results are consistent with the hypothesis that cardiovascular risk remains constant for OSA patients while it increases towards the end of the night for healthy subjects.

A new algorithm for wavelet-based heart rate variability analysis

Abstract One of the most promising non-invasive markers of the activity of the autonomic nervous system is heart rate variability (HRV). HRV analysis toolkits often provide spectral analysis techniques using the Fourier transform, which assumes that the heart rate series is stationary. To overcome this issue, the Short Time Fourier Transform (STFT) is often used. However, the wavelet transform is thought to be a more suitable tool for analyzing non-stationary signals than the STFT. Given the lack of support for wavelet-based analysis in HRV toolkits, such analysis must be implemented by the researcher. This has made this technique underutilized. This paper presents a new algorithm to perform HRV power spectrum analysis based on the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The algorithm calculates the power in any spectral band with a given tolerance for the bands boundaries. The MODWPT decomposition tree is pruned to avoid calculating unnecessary wavelet coefficients, thereby optimizing execution time. The center of energy shift correction is applied to achieve optimum alignment of the wavelet coefficients. This algorithm has been implemented in RHRV, an open-source package for HRV analysis. To the best of our knowledge, RHRV is the first HRV toolkit with support for wavelet-based spectral analysis.

gHRV: Heart rate variability analysis made easy

In this paper, the gHRV software tool is presented. It is a simple, free and portable tool developed in python for analysing heart rate variability. It includes a graphical user interface and it can import files in multiple formats, analyse time intervals in the signal, test statistical significance and export the results. This paper also contains, as an example of use, a clinical analysis performed with the gHRV tool, namely to determine whether the heart rate variability indexes change across different stages of sleep. Results from tests completed by researchers who have tried gHRV are also explained: in general the application was positively valued and results reflect a high level of satisfaction. gHRV is in continuous development and new versions will include suggestions made by testers.

Heart rate variability in patients with severe chronic obstructive pulmonary disease in a home care program

BACKGROUND Chronic obstructive pulmonary disease (COPD) patients present functional and structural changes of the respiratory system that have a profound influence on cardiac autonomic dysfunction. OBJETIVE To analyse heart rate variability in COPD patients under stable condition and during acute exacerbation episodes (AECOPD). METHODS Twenty three severe COPD male patients, 69.6 ± 7.3 years, in stable condition were followed up for two years. Home visits were carried out by a nurse every month, and home or hospital visits were arranged on demand. Every three months an ECG, oxygen saturation and spirometric recording was obtained for each patient. If the patient presented AECOPD compatible clinical data the same measurements were performed before any change of treatment. Spectral parameters of heart rate variability in time and frequency domains were obtained from ECG. The time evolution of power in low frequency (LF) and high frequency (HF) bands were obtained from the spectrogram. In addition, we calculated the LF/HF ratio and total heart rate variability power (POW). RESULTS We analysed 154 patient-visit records during the follow up, pertaining to 23 patients and 8 controls; 19 of the patients had experienced at least one AECOPD. Stable COPD patients had higher HF values than control subjects. No significant differences were found in LF, LF/HF ratio or POW variables. AECOPD patients had higher LF, HF and POW than the stable COPD and control groups. CONCLUSION AECOPD patients exhibited signs of increased autonomic activity compared with stable COPD.

Detection of premature ventricular contractions using the RR-interval signal: A simple algorithm for mobile devices

BACKGROUND Premature ventricular contractions (PVCs) are cardiac abnormalities that may occur in subjects with/without cardiovascular disorder. Detection is usually performed from electrocardiograms (ECGs); heart activity for a long period of time must be recorded at hospital or with ambulatory electrocardiography. An alternative with a common mobile device would be very interesting, because a simple heart rate sensor should be sufficient. OBJECTIVE To develop an algorithm to detect PVCs using the RR-interval (distance between consecutive beats) extracted from ECGs or from the heart rate signal captured by mobile devices. METHODS Feature extraction and classification techniques were included: 1) two timing interval features (prematurity and compensatory pause) were extracted. 2) A linear classifier was applied. To validate the method, the MIT-BIH Arrhythmia Database was used. Considering the existence of unbalanced classes (normal beats and PVCs) at different decision costs, validation was performed with receiver operating characteristic (ROC) analysis. RESULTS A sensitivity of 90.13% and a specificity percentage of 82.52% were achieved. The area under the ROC curve (AUC) was 0.928. CONCLUSIONS The method is advantageous since it only uses the RR-interval signal for PVC detection, and results compare well with more complex methods that use ECG recording.

Loading, Plotting, and Filtering RR Intervals

The initial steps to work with RHRV functions are presented in this chapter. The process starts with the loading of records containing beat positions that should be preprocessed prior to frequency, time, or nonlinear analysis. Data can be stored in various types of files, and RHRV routines can deal with different data formats. Next, heart rate must be obtained from beat positions. It may occur that spurious points appear in the heart rate signal. RHRV allows users to delete these outliers, when necessary. Besides, the signal can be filtered to reject automatically points that do not correspond to acceptable physiological values.

Heart Rate Variability Analysis with the R package RHRV

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A channel-dependent algorithm for heart beats detection in ECG recordings

price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. C.A. García Martínez, A. Otero Quintana, X.A. Vila, M.J. Lado Touriño, L. RodríguezLiñares, J.M. Rodríguez Presedo, A.J. Méndez Penín Heart Rate Variability Analysis with the R package RHRV