gVARVI: A graphical software tool for the acquisition of the heart rate in response to external stimuli.

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.

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.

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.

[Automatic detection of pulmonary nodules on computed tomography: a preliminary study]. / Detección automática de nódulos pulmonares en tomografía computarizada.Un estudio preliminar.

OBJECTIVES: Recent years have seen growing interest in the development of algorithms for computer-assisted diagnosis (CAD) for the detection of pulmonary nodules on both plain-film radiographs and computed tomography (CT) studies. The purpose of CAD algorithms in this context is to alert radiologists to suspicious radioopacities that might represent cancer in the images. We are developing a CAD system for the detection of pulmonary nodules on helical CT images. MATERIAL AND METHODS: We collected cases of patients with pulmonary nodules examined with helical CT. A total of 64 nodules, including both calcified and noncalcified lesions, ranging from 3 to 30 mm in diameter were included in the study. Studies were acquired on one 4-slice and one 64-slice CT scanners. Three chest radiologists at two institutions interpreted the studies to determine whether pulmonary nodules were present. We calculated the sensitivity and the number of false positives per image to evaluate the CAD system. RESULTS: We have developed and evaluated an algorithm for the automatic detection of pulmonary nodules on CT images. For a sensitivity of 76%, the false-positive rate was 1.3 per image. CONCLUSIONS: Our preliminary results suggest that the system might be useful for radiologists in the detection of pulmonary nodules on helical CT images.

A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms.

A computerized scheme to detect clustered microcalcifications in digital mammograms has been developed. Detection of individual microcalcifications in regions of interest (ROIs) was also performed. The mammograms were previously classified into fatty and dense, according to their breast tissue. The most appropriate wavelet basis and reconstruction levels were selected. To select the wavelet basis, 40 profiles of microcalcifications were decomposed and reconstructed using different types of wavelet functions and different combinations of wavelet coefficients. The symlets with a basis of length 8 were chosen for fatty tissue. For dense tissue, the Daubechies' wavelets with a four-element basis were employed. Two methods to detect individual microcalcifications were evaluated: (a) two-dimensional wavelet transform, and (b) one-dimensional wavelet transform. The second technique yielded the best results, and was used to detect clustered microcalcifications in the complete mammogram. When detecting individual microcalcifications by using two-dimensional wavelet transform we have obtained, for fatty ROIs, a sensitivity of 71.11% at a false positive rate of 7.13 per image. For dense ROIs the sensitivity was 60.76% and the false positive rate, 7.33. The areas (A1) under the AFROC curves were 0.33+/-0.04 and 0.28+/-0.02, respectively. The one-dimensional wavelet transform method yielded 80.44% of sensitivity and 6.43 false positives per image (A1=0.39+/-0.03) for fatty ROIs, and 62.17% and 5.82 false positives per image (A1=0.37+/-0.02) for dense ROIs. For the detection of clusters of microcalcifications in the entire mammogram, the sensitivity was 80.00% with 0.94 false positives per image (A1=0.77+/-0.09) for fatty mammograms, and 72.85% of sensitivity at a false positive detection rate of 2.21 per image (A1=0.64+/-0.07) for dense mammograms. Globally, a sensitivity of 76.43% at a false positive detection rate of 1.57 per image was obtained.

Computer-aided diagnosis: automatic detection of malignant masses in digitized mammograms.

A computerized method to automatically detect malignant masses on digital mammograms based on bilateral subtraction to identify asymmetries between left and right breast images was developed. After the digitization, in order to align left and right mammograms the breast border and nipple were automatically detected. Images were corrected to avoid differences in brightness due to the recording procedure. Left and right mammograms were subtracted and a threshold was applied to obtain a binary image with the information of suspicious areas. The suspicious regions or asymmetries were delimited by a region growing algorithm. Size and eccentricity tests were used to eliminate false-positive responses and texture features were extracted from suspicious regions to reject normal tissue regions. The scheme, tested in 70 pairs of digital mammograms, achieved a true-positive rate of 71% with an average number of 0.67 false positives per image. Computerized detection was evaluated by using free-response operating characteristic analysis (FROC). An area under the AFROC (A1) of 0.667 was obtained. Our results show that the scheme may be helpful to the radiologists by serving as a second reader in mammographic screening. The low number of false positives indicates that our scheme would not confuse the radiologist by suggesting normal regions as suspicious.

Real and simulated clustered microcalcifications in digital mammograms. ROC study of observer performance.

We have developed a model to simulate clustered microcalcifications on digital mammograms. Wavelet transform techniques were used to detect real clustered microcalcifications. A feature analysis process was applied to automatically extract the features describing the individual simulated microcalcifications and clusters from the values of the real clustered microcalcifications present in the mammogram. Subsequently, a database of simulated and real clustered microcalcifications was created. Clusters of microcalcifications from this database were tested for indistinguishability from real ones. Two radiologists and one physicist were asked to indicate whether the microcalcifications were either real or simulated. The responses of the readers were evaluated with a ROC analysis and the area under the curve was calculated. The average ROC area was 0.54 +/- 0.03, indicating there was no statistical difference between real and simulated clustered microcalcifications. The method allows for the creations of simulated clustered microcalcifications that are virtually indistinguishable from real microcalcifications in digital mammograms and could be used to evaluate different image processing techniques.

Automatic detection of breast border and nipple in digital mammograms.

Advances in the area of computerized image analysis applied to mammography may have very important practical applications in automatically detecting asymmetries (masses, architectural distortions, etc.) between the two breasts. We have developed a fully automatic technique to detect the breast border and the nipple, this being a necessary prerequisite for further image analysis. To detect the breast border, an algorithm that computes the gradient of gray levels was applied. To detect the nipple, three algorithms were compared (maximum height of the breast border, maximum gradient, and maximum second derivative of the gray levels across the median-top section of the breast). A combined method was also designed. The algorithms were tested on 156 digitized mammograms. The breast segmentation results were evaluated by two expert radiologists and one physicist. In 89% of the mammograms, the computed border was in close agreement with the radiologist's estimated border. Segmentation results were acceptable to be used in computer-aided diagnostic schemes. The mean distance between the position of the nipple indicated by two radiologists by consensus and the position calculated by the computer was 6 mm.

A limited PACS dedicated to a research environment. Nine years' experience in Santiago de Compostela.

A digital image network has been installed at the Department of Radiology of the University of Santiago de Compostela, Spain, to create a "limited' Picture Archiving and Communication System (PACS). This experience is being dedicated to address problems associated with digital techniques in a research environment. The backbone of the system is a multiprotocol ethernet network. Attached to the network are a number of advanced devices such as DEC VAX and UNIX workstations. Currently, a high resolution film digitizer and a laser printer are under evaluation for radiologic image research. During a period of nine years, 1987 to 1995, experimental and clinical trials have been conducted on different film based digital radiography apparatus primarily dedicated to chest and breast imaging. Several research projects have been completed. In this paper we describe the results of these investigations and discuss the advantages and disadvantages of this digital technique. The results of the different completed studies will be presented separately. A description of the physical characteristics of the limited PACS system dedicated to a research environment will serve as background.