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Background and objective: nowadays, numerous mobile applications capable of measuring the Heart Rate (HR) are continuously being launched. However, these tools do not allow to record and label the acquired HR signals while users are doing activities such as practising sports or viewing images. They do not allow to perform simultaneous HR acquisition and real-time HRV analysis, either. VARSE is an app for Android mobile devices capable of acquiring and labelling HR signals and of performing real-time HRV analysis. Methods: VARSE was developed for Android devices. It includes functionalities to acquire HR signals from any Bluetooth device that implements the Heart Rate Profile specification (such as a chest strap), while labelling segments of the HR data in different situations. Not only can these signals be stored, but also time and frequency HRV analyses can be carried out over them. The application is distributed under the MIT license [1], and it is also available to be installed via Google Play [2]. Functionality, ease-of-use and friendliness of VARSE were evaluated employing an User Experience Questionnaire (UEQ). Its reliability was proven by comparative studies against other existing acquisition (gVARVI) or HRV analysis (RHRV) software. Results: high values were obtained for all the dimensions evaluated in the UEQ, proving the quality of the application, as well as its ease-of-use and efficiency. Both HR signal acquisition and HRV analysis yielded results similar to the ones obtained using other applications. Conclusions: VARSE is a tool with complete functionality, that can be easily downloaded or installed on Android mobile devices. It can be used by anyone who wishes to record HR signals while performing different activities, and also by the medical scientific community to perform real-time HRV analyses easily. Future versions will improve its capabilities and allow its integration with other open source applications. Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG Xunta de Galicia | Ref. ED431E 2018/07

Several works studied the elicitation of emotions through the exposure of individuals to relevant stimuli, using spectral analysis of Heart Rate Variability (HRV) when people are subject to emotional elicitation. If correlation exists between HRV and emotional responses, spectral analysis can be used to study emotion regulation under external stimuli. In this work, we studied the relationship between visual elicitation and emotion regulation, employing HRV. Images (with pleasant, unpleasant and neutral emotional content) were selected from the IAPS (International Affective Picture System) dataset. Ninety-eight participants were enrolled, and subject to view all images, displayed in random order for each participant. Heart rate was recorded during the experiment, and HRV analysis was performed. Spectral values were studied for the different images. The presentation order of images was relevant, mainly when unpleasant images were viewed in first place; this significantly affects HRV values. Spectral values were higher for men, being this difference stronger when pleasant pictures were displayed. Age and gender dependences of spectral indexes were found. The influence of visual elicitation, with different emotional contents, over HRV, was assessed. Results indicate that HRV parameters are affected when individuals are subject to external, emotional-based stimuli.

Heart rate variability (HRV) analysis is a powerful instrument that provides information about the heart conditions. However, there exist some limitations in the use of HRV in the clinical practice. Examples are the lack of reference values for healthy populations, different HR (Heart Rate) acquisition systems, and varying software packages. Other factors that affect HRV values are the influence of lifestyle, drugs and alcohol consumption, and pollution. In this work, recommendations to perform HRV-based experiments were established. These suggestions refer to best moment of the day to record data, the optimal body position, and the quality and duration of the recorded signals. In this way, HR data from 6 healthy subjects (2 women, 4 men), with median age of 50 years old, were recorded during 15 days, 3 times a day. Recordings were performed in the following situations: both supine and sitting body positions, in the morning, in the afternoon and at night. Data were processed and HRV analysis was performed. Distorting factors affecting HRV have been determined. The most stable HRV indexes (less variation over the days) have also been established. For this task, a variation coefficient was calculated for each parameter, as the ratio between the standard deviation and the mean value. Results indicated that HR data should be recorded in the morning, the sitting position. Related to signals duration, when comparing HR signals, they should be of equal length (same recording time). In addition, HRVi (HRV triangular index) and MADRR (median of the absolute differences between adjacent RR intervals) resulted in the most robust indexes in both low and high frequency domains. For global indexes, the ApEn (approximate entropy) measure emerged as the most stable one. As a conclusion, researchers must be extremely cautious in studies involving HRV analysis; the moment of the day to record data, the body position, or the quality of recorded data will produce different HR signals, and thus, the values of the HRV parameters will be different in each case. This may clearly bias the conclusions of the study.

Object indoor location is a field that receives much research effort but that is lacking enough maturity for its integration in popular devices like mobile phones. This paper describes the results of an experiment carried out to compare different pattern recognition algorithms in order to process the information from a set of Bluetooth transmitters, located in fixed positions, with the aim of locating an object in a precise position. Our conclusion is that the best algorithms, among the five we tested, are random forests and model-based clustering, which gave accuracies around 90%. We have also conducted experiments to analyse the influence of the number of Bluetooth transmitters and to determine the sets of features with better performance. The proposed approach is simple and gives 90% of accuracy for locating objects with 1 m precision, making it suitable for a wide range of applications., Facultad de Informática

Background and objective At present, tools capable of acquiring heart rate data can be found both in commercial and research fields. However, these tools do not allow users to manage experiments comprising sequences of activities or to store the information needed to perform heart rate variability analysis across different activities. One exception is VARVI, a simple software tool developed previously in our research group that does not have a graphical user interface and it works only with visual stimuli. In this paper, we present gVARVI, a software tool aimed at obtaining heart rate data signals while the user is either receiving a sequence of external stimuli or performing a sequence of actions (an activity). Methods gVARVI is an open source application developed in Python programming language. It can acquire heart rate data by means of a wireless chest strap using either Bluetooth or ANT+ protocols. Users can define activities of different types (video, sounds, pictures or keyboard controlled actions) which will associate contextual information to the heart rate data. gVARVI allows users to preview this data or to store it to be used for heart rate variability studies. Our tool was validated by 15 researchers, who worked with the application and filled in a usability questionnaire. Results The outcome of the usability test was satisfactory, giving a mean score of 4.75 in a 1–5 scale (1 – strongly disagree, 5 – strongly agree). Participants also contributed with valuable comments, which we used to include new features in the last version of our tool. Conclusions gVARVI is an open source tool that offers new possibilities to both physicians and clinicians to perform heart rate variability studies. It allows users to acquire heart rate data including information on the activity performed by subjects while recording. In this paper, we describe all the functionalities included in gVARVI, and a complete example of use is provided.

Evaluating research results is not always an easy task. Although it is interesting to compare personal achievements against similar works from other researchers, in many ocassions it is hard to replicate exactly the same conditions, because papers do not reflect all the variables of the context. To mitigate this problem, some standard systems have been created for researchers to unify this step in their works. One example of this is the bxb (beat-by-beat) tool from PhysioNet's PhysioToolkit, developed to compare results in beat detection. Delineation is an important part inside Electrocardiogram (ECG) diagnostic systems, because it is necessary to properly identify waves inside the beats. Besides the position of each wave into the signal, onset and offset positions for a wave are also needed. The more precise these positions are, the better the diagnostic is. But unlike the case of the beat detection, there are no generic tools for delineation evaluation, and papers that deepen in this subject in most cases do not explain clearly how they get their results, or directly asked an expert to supervise the outcome of their systems. Because of this, the main purpose of the present work is to introduce an evaluation tool, named wxw, to assess wave delineation for a computer-aided ECG diagnostic system.

This paper proposes a computerized heartbeat detection method in single-channel electrocardiograms (ECGs). First, the well-known Pan-Tompkins technique was implemented, and next, a channel-dependent version was developed, by adjusting threshold values and reducing false QRS detections. The algorithms were tested with the MIT-BIH Arrhythmia Database (original algorithm), and with the St. Petersburg Database (modified version). When validating the performances of the original Pan-Tompkins algorithm, we have achieved a sensitivity of Se = 99.81, at a positive predictivity (P+) = 99.85%. The F-Score was 0.9587, and the RMS RR Interval Error (RMSRRIE) resulted to be 4,480.46 ms. When analysing the performance of the modified algorithm, results provided an average value of Se = 99.92%, P+ = 99.98%, F-Score = 0.9718, and a mean value of 111.05 ms. for the RMSRRIE. In conclusion, the improved Pan-Tompkins algorithm provides higher values for sensitivity and positive predictivity, increased F-Score, and it significantly reduces the temporal error when estimating the positions of QRS complexes. Thus, it could be used as a starting point to detect heartbeat positions in more sophisticated computerized detection systems.

This paper proposes a computerized technique aimed at detecting heart beat positions in electrocardiograms (ECGs). The algorithm was implemented by adapting one of the most popular methods (Pan-Tompkins) developed to detect beat positions in ECG records, to be used in different leads of multi-channel ECGs. First, the Pan-Tompkins algorithm was programmed, and next, a lead-dependent version was implemented, adapting thesholds for each of the ECG channels. The original algorithm was tested with the MIT-BIH Arrhythmia Database, while the performance of the improved technique was tested on the different channels of records from the St. Petersburg Database. For the original Pan-Tompkins algorithm, a sensitivity of Se = 99.81% was obtained, with a positive predictivity (P+) = 99.85%. The F-Score was 0.9587, and the RMS RR Interval Error (RMSRRIE) resulted to be 4,480.46 ms. For the modified algorithm, results yielded an average value for Se = 99.92%, P+ = 99.98%, F-Score = 0.9718, and a mean value of 111.05 ms. for the RMSRRIE. Results indicate that our improved method provides similar better sensitivity and positive predictivity, increased F-Score, and it significantly reduces the temporal error when estimating the positions of QRS complexes (combination of Q, R and S waves representing ventricular depolarization in an ECG signal).

Background: Nowadays, the assessment of psychopathy relies on semistructured interviews plus file reviews. In order to improve the predictive validity of psychopathy at the individual level, tools that are not based on the rating of signs and symptoms are in great need. Sampling and Methods: The present study was conducted in a representative sample of 204 Spanish sentenced inmates. These inmates have served at least 6 months of their sentence at the Pereiro de Aguiar (Ourense, Spain) penitentiary. Psychopathy signs and symptoms were scored through interview and file review. The Implicit Association Test (IAT) and heart rate variability (HRV) experiments were also conducted. The Iowa Gambling Task (IGT) was performed as a control measure. Results: Spectral HRV indices were able to detect psychopathic inmates at a significant level, while IAT experiments and the IGT could not discriminate them. HRV indices showed a more significant difference when assessing the affective-interpersonal dimensions of psychopathy. Conclusions: An HRV experiment is better than IAT in order to detect psychopathy in a representative sample of Spanish inmates.

This chapter presents an example of a complete HRV analysis of several long-duration ECG records employing the RHRV software package, including frequency, nonlinear, and time analysis. First, 3-h intervals of each ECG were extracted, corresponding to morning, afternoon, and night periods. Next, HRV analyses in both time and frequency domains were performed over each portion, and nonlinear values were also estimated. Finally, statistical analysis was applied over the variability parameters corresponding to these three types of fragments, to verify if differences existed among the morning, afternoon, and night ECG intervals. Some statistically significant differences were found between the morning and night periods. In particular, HRVi (HRV index, time) and Poincare \(SD_{2}\) (nonlinear) parameters differ in a statistical way.

Heart rate variability can be analyzed by several methods, being time-domain methods the simplest measures that can be obtained from RR intervals series. Numerical indices summarizing the variability of the series are calculated from the RR intervals series. These measures are easily implemented and have low computational cost. They are usually calculated over longer segments of data than frequency-domain methods, typically 24 h. However, they can be also estimated from smaller segments (typically 5 min) in order to compare different episodes. In this chapter, a review of time-domain measures and their calculation within RHRV will be shown.

There are many complex systems in nature that can be explained by nonlinear interactions of its main components. The heart rate regulation is one of the most complex systems in the human body. Heart rhythm is innervated by both the parasympathetic and sympathetic branches of the ANS. At the same time, the autonomic nervous system is influenced by humoral effects, hemodynamic variables, respiratory rhythm, and stroke volume, among others. Furthermore, there exist feedback loops among these mechanisms influencing each other in a nonlinear way. A consequence of these nonlinear interactions is that heart rate modulation cannot be fully understood by studying its components in isolation. However, the study of the heart rhythm modulation as a whole is a formidable task. A more common approach in the literature, more modest than the full comprehension of the heart rate regulation system, is trying to quantify the complexity of heart rate using nonlinear statistics derived from the chaos theory or from fractal processes. In this chapter, we summarize the most widely used statistics based on nonlinear and fractal dynamics, and we show how to compute them with RHRV.

Intervals of physiological interest within the heartbeat time series (such as apnea and ischemia) may be marked making use of tags in so-called episodes. Episodic information can be incorporated into RHRV from external files or can be added using functions included in the package. This information can be useful to compare sections of HRV records both visually and numerically.

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.

Searching on Google Scholar the string “heart rate variability” (HRV) provides about half a million references, which gives us an idea of the research activity around this concept. This chapter describes the historical development of this research field, from the pioneering work of the eighteenth and nineteenth centuries to the boom of the final decade of the twentieth century. HRV analysis is important from a clinical point of view because of its direct relationship with the autonomic nervous system (ANS). Therefore, we include a section explaining the physiological basis of HRV and its relationship with the ANS. Finally, we review the most relevant clinical applications of HRV in three distinct lines: patient monitoring, acute care, and chronic disorders. The reader of this chapter must bear in mind that the purpose of this book is to present a software tool, RHRV, which greatly simplifies performing heart rate variability analysis. This chapter does not pretend to be a comprehensive review of the physiology of heart rate variation, but just a brief introduction to the field whose main purpose is building a common language to be able to present concepts more effectively throughout the book.

a b s t r a c t 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 (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.

This paper analyzes the impact of hearing of different language versions of the National Anthem of Ecuador among 60 university students of diverse ethnic and geographical origin, through measuring Heart Rate Variability (HRV). Research shows a perspective of neuroscience applied to social sciences, through the use of common tools of health sciences for the measurement of perception.

Automatic recognition of anomalous human activities and falls in an indoor setting from video sequences could be an enabling technology for low-cost, home-based health care systems. Detection systems based upon intelligent computer vision software can greatly reduce the costs and inconveniences associated with sensor based systems. In this paper, we propose such a software based upon a spatio-temporal motion representation, called Motion Vector Flow Instance (MVFI) templates, that capture relevant velocity information by extracting the dense optical flow from video sequences of human actions. Automatic recognition is achieved by first projecting each human action video sequence, consisting of approximately 100 images, into a canonical eigenspace, and then performing supervised learning to train multiple actions from a large video database. We show that our representation together with a canonical transformation with PCA and LDA of image sequences provides excellent action discrimination. We also demonstrate that by including both the magnitude and direction of the velocity into the MVFI, sequences with abrupt velocities, such as falls, can be distinguished from other daily human action with both high accuracy and computational efficiency. As an added benefit, we demonstrate that, once trained, our method for detecting falls is robust and we can attain real-time performance.

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.

Gesture and action recognition for video surveillance is an active field of computer vision. Nowadays, there are several techniques that attempt to address this problem by 3D mapping with a high computational cost. This paper describes software algorithms that can detect the persons in the scene and analyze different actions and gestures in real time. The motivation of this paper is to create a system for thetele-assistance of elderly, which could be used as early warning monitor for anomalous events like falls or excessively long periods of inactivity. We use a method for foreg-round-background segmentation and create a feature vectorfor discriminating and tracking several people in the scene. Finally, a simple real-time histogram based algorithm is described for discriminating gestures and body positions through a K-Means clustering.

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.

This paper proposes a beat detection algorithm specially tailored to be used with 12 channel ECG records. The algorithm first obtains beat positions on each channel, and then combines this information to get an improved estimate. The detection process involves two stages: 1) single-channel detection: implemented by improving one of the most popular methods (Pan-Tompkins) developed to detect beat positions; and 2) multichannel detection: an algorithm that combines the information of the beat positions obtained in each of the 12 channels. In this way, our results clearly improve those obtained with the single-channel detection method, discarding detection errors, false positives, and duplicated beats. Besides, our single-channel method significantly reduces the temporal error when estimating the positions of QRS complexes. In the multichannel detection, the assessment of our algorithm against one-channel based approaches shows a significant improvement in detection outcome (Se = 99.86%, P+ = 99.98%, RMS RR Interval Error = 25.98 ms, F-Score = 0.9992), making it a good starting point for automatic diagnosis of heart conditions.

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 band's 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.

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.

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.

In outdoor localization, global positioning systems (GPS) has been widely used. Indoor applications require a precise estimation that GPS can not achieve. Several technologies have been tried out as WI-FI, RFID, Bluetooth, Zigbee and others. This paper describes an experiment conducted in a medium- sized room in which six zones have been identified and two Bluetooth transmitters were installed. The aim is to enable continuous monitoring of areas where a person moves. For this purpose, we have used the technique of RSSI fingerprinting and tested three different algorithms. The best results were obtained with an algorithm based on SVM, which yielded success rates of 88.54%. Based on this algorithm, we intend to develop a cheap and easily configurable indoor localization system.

Behavior determination and multiple object tracking for video surveillance are two of the most active fields of computer vision. The reason for this activity is largely due to the fact that there are many application areas. This paper describes work in developing software algorithms for the tele-assistance for the elderly, which could be used as early warning monitor for anomalous events.We treat algorithms for both the multiple object tracking problem as well simple behavior detectors based on human body positions. There are several original contributions proposed by this paper. First, a method for comparing foreground - background segmention is proposed. Second a feature vector based tracking algorithm is developed for discriminating multiple objects. Finally, a simple real-time histogram based algorithm is described for discriminating movements and body positions.

We describe a novel software package, RHRV, which we have developed for studying heart rate variability (HRV) as an indicator for the state of the cardiovascular control system and as a discriminator for chronic sleep disorders. The RHRV package is written in the open source statistical environment R, and has recently been made available as a third party contribution to the R-CRAN repository. In this article, we motivate the necessity for the development of an open-source software platform which can be used for developing new algorithms for studying HRV. We demonstrate aspects of our software platform through the analysis of representative signals.