VARSE: Android app for real-time acquisition and analysis of heart rate signals.

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.

A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue.

This paper presents an algorithm for background modeling and foreground detection that uses scaling coefficients, which are defined with a new color model called lightness-red-green-blue (LRGB). They are employed to compare two images by finding pixels with scaled lightness. Three backgrounds are used: 1) verified background with pixels that are considered as background; 2) testing background with pixels that are tested several times to check if they belong to the background; and 3) final background that is a combination of the testing and verified background (the testing background is used in places, where the verified background is not defined). If a testing background pixel matches pixels from previous frames (the match is tested using scaling coefficients), it is copied to the verified background, otherwise the pixel is set as the weighted average of the corresponding pixels of the last input images. After the background is computed, foreground objects are detected by using the scaling coefficients and additional criteria. The algorithm was evaluated using the SABS data set, Wallflower data set and a subset of the CDnet 2014 data set. The average F measure and sensitivity with the SABS Data set were 0.7109 and 0.8725, respectively. In the Wallflower data set, the total number of errors was 5280 and the total F-measure was 0.9089. In the CDnet 2014 data set, the F-measure for the baseline test case was 0.8887 and for the shadow test case was 0.8300.

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.

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.

Computerized detection of breast masses in digitized mammograms.

We propose a system to detect malignant masses on mammograms. We investigated the behavior of an iris filter at different scales. After iris filter was applied, suspicious regions were segmented by means of an adaptive threshold. Suspected regions were characterized with features based on the iris filter output and, gray level, texture, contour-related, and morphological features extracted from the image. A backpropagation neural network classifier was trained to reduce the number of false positives. The system was developed and evaluated with two completely independent data sets. Results for a test set of 66 malignant and 49 normal cases, evaluated with free-response receiver operating characteristic analysis, yielded a sensitivity of 88% and 94% at 1.02 false positives per image for lesion-based and case-based evaluation, respectively. Results suggest that the proposed method could help radiologists as a second reader in mammographic screening.

Computer aided diagnosis for breast masses detection on a telemammography system.

A Computer-Aided Diagnosis (CAD) scheme for breast masses detection has been developed and integrated as a part of a telemammography system. This work derives from the close cooperation between the Laboratory for Radiologic Image Research of the University of Santiago de Compostela (Spain) and the company Intelsis Sistemas Inteligentes (Santiago de Compostela, Spain). This cooperation has been supported by funds from different projects, mainly from the European Union, the Spanish Health Administration, and the Galician Public Health's Service. As a result, a first prototype is ready to begin a demonstration project.

Prospective evaluation of implants connected to teeth.

PURPOSE: This prospective clinical trial examined the effect on teeth and implants when rigidly or non-rigidly connected in a cross-arch model. MATERIALS AND METHODS: Thirty patients received 2 implants, 1 on each side of the mandible, and were restored with 3-unit fixed partial dentures connected either rigidly or non-rigidly to an abutment tooth. Patients were followed for at least 5 years post-restoration. RESULTS: Repeated-measures analysis revealed no significant difference in crestal bone loss at implants (rigid versus non-rigid methods). An overall significant difference (P < .001) was found comparing methods for teeth. Paired t tests revealed no significant differences in crestal bone levels for implants or teeth at the 5-year recall. Kaplan-Meier methods and the Cox proportional hazards model showed no differences between attachment methods with regard to success based on survival and bone loss criteria. During the 5-year recall period, 1 implant (rigid side) was removed. Four implants developed bone loss greater than 2 mm during the course of this trial. One tooth on the rigid side and 2 teeth on the non-rigid side had greater than 2 mm of crestal bone loss and were removed secondary to fractures. In all, 5 abutment teeth were removed, all of which had been treated with root canal therapy and fractured at the interface of the post within the tooth. There was no clear relationship of tooth fracture to attachment. Repeated-measures analysis of mobility values revealed no significant changes over the time course of this study, and paired t tests revealed no statistically significant differences between implants for mobility. Repeated-measures analysis and paired t tests for probing depth revealed no significant changes over the time course of this study. There were no significant differences in soft tissue indices for either attachment method. The percentage of patients who had measurable intrusion was 66% for the non-rigid group, and 44% for the rigid group; 25% of the non-rigid teeth had greater than 0.5 mm intrusion, compared with 12.5% for the rigid group. For the 2 time periods evaluated, there was no significant increase in intrusion over time. The non-rigid-side implant required more nonscheduled visits to treat problems than the rigid implant and the teeth. DISCUSSION: Most patients were treated successfully with rigid or non-rigid attachment of implants to teeth. CONCLUSION: The high incidence of intrusion and non-scheduled patient visits suggest that alternative treatments without connecting implants to teeth may be indicated.