Heart Rate Variability Monitoring Using a Wearable Armband

A wearable electrocardiogram (ECG) monitor is evaluated as heart rate variability (HRV) monitor. The device consists of an armband designed to be worn on the left upper arm which provides 3 ECG channels based on 3 pairs of dry (no hydrogel) electrodes. Armband-ECG and conventional-Holter-ECG signals were simultaneously recorded from 14 subjects during 5 minutes in supine position. Spacial principal component analysis was used to obtain a unique armband ECG signal in which the electromyogram contribution is attenuated. QRS complexes were automatically detected. Five traditional HRV parameters were derived: SDNN, RMSSD, pNN50, and powers within low frequency (LF, [0.04, 0.15] Hz) and high frequency (HF, [0.15, 0.4] Hz) bands. The Pearson''s correlation coefficient between the measurements from the armband device and the measures from the Holter device was computed. Results show very high correlations (1.0000, 0.9999, 0.9984, 1.0000, and 0.9999 for SDNN, RMSSD, pNN50, and powers at LF and HF, respectively), suggesting that the quality of armband-ECG signals is enough to estimate HRV parameters during stationary movement restricted conditions

Electrocardiogram Derived Respiration for Tracking Changes in Tidal Volume from a Wearable Armband

A pilot study on tracking changes in tidal volume (TV) using ECG signals acquired by a wearable armband is presented. The wearable armband provides three ECG channels by using three pairs of dry electrodes, resulting in a device that is convenient for long-term daily monitoring. An additional ECG channel was derived by computing the first principal component of the three original channels (by means of principal component analysis). Armband and spirometer signals were simultaneously recorded from five healthy subjects who were instructed to breathe with varying TV. Three electrocardiogram derived respiration (EDR) methods based on QRS complex morphology were studied: the QRS slopes range (SR), the R-wave angle (), and the R-S amplitude (RS). The peak-to-peak amplitudes of these EDR signals were estimated as surrogates for TV, and their correlations with the reference TV (estimated from the spirometer signal) were computed. In addition, a multiple linear regression model was calculated for each subject, using the peak-to-peak amplitudes from the three EDR methods from the four ECG channels. Obtained correlations between TV and EDR peak-to-peak amplitude ranged from 0.0448 up to 0.8491. For every subject, a moderate correlation (>0.5) was obtained for at least one EDR method. Furthermore, the correlations obtained for the subject-specific multiple linear regression model ranged from 0.8234 up to 0.9154, and the goodness of fit was 0.73±0.07 (median ± standard deviation). These results suggest that the peak-to-peak amplitudes of the EDR methods are linearly related to the TV. opening the possibility of estimating TV directly from an armband ECG device.Clinical Relevance - This opens the door to possible continuous monitoring of TV from the armband by using EDR

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a “bag of subregions”. The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

The COST292 experimental framework for TRECVID 2007

In this paper, we give an overview of the four tasks submitted to TRECVID 2007 by COST292. In shot boundary (SB) detection task, four SB detectors have been developed and the results are merged using two merging algorithms. The framework developed for the high-level feature extraction task comprises four systems. The first system transforms a set of low-level descriptors into the semantic space using Latent Semantic Analysis and utilises neural networks for feature detection. The second system uses a Bayesian classifier trained with a "bag of subregions". The third system uses a multi-modal classifier based on SVMs and several descriptors. The fourth system uses two image classifiers based on ant colony optimisation and particle swarm optimisation respectively. The system submitted to the search task is an interactive retrieval application combining retrieval functionalities in various modalities with a user interface supporting automatic and interactive search over all queries submitted. Finally, the rushes task submission is based on a video summarisation and browsing system comprising two different interest curve algorithms and three features

Route Selection Problem Based on Hopfield Neural Network

Transport network is a key factor of economic, social and every other form of development in the region and the state itself. One of the main conditions for transport network development is the construction of new routes. Often, the construction of regional roads is dominant, since the design and construction in urban areas is quite limited. The process of analysis and planning the new roads is a complex process that depends on many factors (the physical characteristics of the terrain, the economic situation, political decisions, environmental impact, etc.) and can take several months. These factors directly or indirectly affect the final solution, and in combination with project limitations and requirements, sometimes can be mutually opposed. In this paper, we present one software solution that aims to find Pareto optimal path for preliminary design of the new roadway. The proposed algorithm is based on many different factors (physical and social) with the ability of their increase. This solution is implemented using Hopfield's neural network, as a kind of artificial intelligence, which has shown very good results for solving complex optimization problems

Music Performers Classification by Using Multifractal Features : A Case Study

In this paper, we investigated the possibility to classify different performers playing the same melodies at the same manner being subjectively quite similar and very difficult to distinguish even for musically skilled persons. For resolving this problem we propose the use of multifractal (MF) analysis, which is proven as an efficient method for describing and quantifying complex natural structures, phenomena or signals. We found experimentally that parameters associated to some characteristic points within the MF spectrum can be used as music descriptors, thus permitting accurate discrimination of music performers. Our approach is tested on the dataset containing the same songs performed by music group ABBA and by actors in the movie Mamma Mia. As a classifier we used the support vector machines and the classification performance was evaluated by using the four-fold cross-validation. The results of proposed method were compared with those obtained using mel-frequency cepstral coefficients (MFCCs) as descriptors. For the considered two-class problem, the overall accuracy and F-measure higher than 98% are obtained with the MF descriptors, which was considerably better than by using the MFCC descriptors when the best results were less than 77%

Improving CBIR Systems Using Automated Ranking

The most common way of searching images on the Internet and in private collections is based on a similarity measuring of a series of text words that are assigned to each image with users query series. This method imposes strong constraints (the number of words to describe the image, the time necessary to thoroughly describe the subjective experience of images, the level of details in the picture, language barrier, etc.), and is therefore very inefficient. Modern researches in this area are focused on the contentbased searching images (CBIR). In this way, all described disadvantages are overcome and the quality of searching results is improved. This paper presents a solution for CBIR systems where the search procedure is enhanced using sophisticated extraction and ranking of extracted images. The searching procedure is based on extraction and preprocessing of a large number of low level image features. Thus, when the user defines a query image, the proposed algorithm based on artificial intelligence, shows to the user a group of images which are most similar to a query image by content. The proposed algorithm is iterative, so the user can direct the searching procedure to an expected outcome and get a set of images that are more similar to the query one