A comparison of three heart rate detection algorithms over ballistocardiogram signals

Heart rate (HR) detection from ballistocardiogram (BCG) signals is challenging for various reasons. For example, BCG signals' morphology can vary between and within-subjects. Also, it differs from one sensor to another. Hence, it is essential to evaluate HR detection algorithms across different datasets and under different experimental setups. This paper investigated the suitability of three algorithms (i.e., MODWT-MRA, CWT, and template matching) for HR detection across three independent BCG datasets. The first two datasets (Datset1 and DataSet2) were obtained using a microbend fiber optic (MFOS) sensor, while the last one (DataSet3) was obtained using a fiber Bragg grating (FBG) sensor. DataSet1 was collected from 10 OSA patients during an in-lab PSG study, Datset2 was obtained from 50 subjects in a sitting position, and DataSet3 was gathered from 10 subjects in a sleeping position. The CWT with derivative of Gaussian (Gaus2) provided superior results than the MODWT-MAR, CWT (frequency B-spline-Fbsp-2-1-1), and CWT (Shannon-Shan1.5-1.0) for DataSet1 and DataSet2. That said, a BCG template was constructed from DataSet1. Then, it was applied for HR detection across DataSet2. The template matching method achieved slightly superior results than CWT-Gaus2 for DataSet2. Furthermore, it has proved useful for HR detection across DataSet3 despite that BCG signals were obtained from a different sensor and under different conditions. Overall, the time required to analyze a 30-second BCG signal was in a millisecond resolution for the three proposed methods. The MODWT-MRA had the highest performance, with an average time of 4.92 ms.

Ballistocardiogram Signal Processing: A Literature Review

Time-domain algorithms are focused on detecting local maxima or local minima using a moving window, and therefore finding the interval between the dominant J-peaks of ballistocardiogram (BCG) signal. However, this approach has many limitations due to the nonlinear and nonstationary behavior of the BCG signal. This is because the BCG signal does not display consistent J-peaks, which can usually be the case for overnight, in-home monitoring, particularly with frail elderly. Additionally, its accuracy will be undoubtedly affected by motion artifacts. Second, frequency-domain algorithms do not provide information about interbeat intervals. Nevertheless, they can provide information about heart rate variability. This is usually done by taking the fast Fourier transform or the inverse Fourier transform of the logarithm of the estimated spectrum, i.e., cepstrum of the signal using a sliding window. Thereafter, the dominant frequency is obtained in a particular frequency range. The limit of these algorithms is that the peak in the spectrum may get wider and multiple peaks may appear, which might cause a problem in measuring the vital signs. At last, the objective of wavelet-domain algorithms is to decompose the signal into different components, hence the component which shows an agreement with the vital signs can be selected i.e., the selected component contains only information about the heart cycles or respiratory cycles, respectively. An empirical mode decomposition is an alternative approach to wavelet decomposition, and it is also a very suitable approach to cope with nonlinear and nonstationary signals such as cardiorespiratory signals. Apart from the above-mentioned algorithms, machine learning approaches have been implemented for measuring heartbeats. However, manual labeling of training data is a restricting property.

Automatic Classification of Bright Retinal Lesions via Deep Network Features

The diabetic retinopathy is timely diagonalized through color eye fundus images by experienced ophthalmologists, in order to recognize potential retinal features and identify early-blindness cases. In this paper, it is proposed to extract deep features from the last fully-connected layer of, four different, pre-trained convolutional neural networks. These features are then feeded into a non-linear classifier to discriminate three-class diabetic cases, i.e., normal, exudates, and drusen. Averaged across 1113 color retinal images collected from six publicly available annotated datasets, the deep features approach perform better than the classical bag-of-words approach. The proposed approaches have an average accuracy between 91.23% and 92.00% with more than 13% improvement over the traditional state of art methods.

Automated Breast Lesion Segmentation in Ultrasound Images

The main objective of this project is to segment different breast ultrasound images to find out lesion area by discarding the low contrast regions as well as the inherent speckle noise. The proposed method consists of three stages (removing noise, segmentation, classification) in order to extract the correct lesion. We used normalized cuts approach to segment ultrasound images into regions of interest where we can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion. For every original image, an annotated ground-truth image is given to perform comparison with the obtained experimental results, providing accurate evaluation measures.

Detecting and avoiding frontal obstacles from monocular camera for micro unmanned aerial vehicles

In literature, several approaches are trying to make the UAVs fly autonomously i.e., by extracting perspective cues such as straight lines. However, it is only available in well-defined human made environments, in addition to many other cues which require enough texture information. Our main target is to detect and avoid frontal obstacles from a monocular camera using a quad rotor Ar.Drone 2 by exploiting optical flow as a motion parallax, the drone is permitted to fly at a speed of 1 m/s and an altitude ranging from 1 to 4 meters above the ground level. In general, detecting and avoiding frontal obstacle is a quite challenging problem because optical flow has some limitation which should be taken into account i.e. lighting conditions and aperture problem.

Automatic Discrimination of Color Retinal Images using the Bag of Words Approach

Diabetic retinopathy (DR) and age related macular degeneration (ARMD) are among the major causes of visual impairment worldwide. DR is mainly characterized by red spots, namely microaneurysms and bright lesions, specifically exudates whereas ARMD is mainly identified by tiny yellow or white deposits called drusen. Since exudates might be the only manifestation of the early diabetic retinopathy, there is an increase demand for automatic retinopathy diagnosis. Exudates and drusen may share similar appearances, thus discriminating between them is of interest to enhance screening performance. In this research, we investigative the role of bag of words approach in the automatic diagnosis of retinopathy diabetes. We proposed to use a single based and multiple based methods for the construction of the visual dictionary by combining the histogram of word occurrences from each dictionary and building a single histogram. The introduced approach is evaluated for automatic diagnosis of normal and abnormal color fundus images with bright lesions. This approach has been implemented on 430 fundus images, including six publicly available datasets, in addition to one local dataset. The mean accuracies reported are 97.2% and 99.77% for single based and multiple based dictionaries respectively.