Analysis of cardiac signals using spatial filling index and time-frequency domain

BACKGROUND: Analysis of heart rate variation (HRV) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system (ANS). HRV analysis is based on the concept that fast fluctuations may specifically reflect changes of sympathetic and vagal activity. It shows that the structure generating the signal is not simply linear, but also involves nonlinear contributions. These signals are essentially non-stationary; may contain indicators of current disease, or even warnings about impending diseases. The indicators may be present at all times or may occur at random in the time scale. However, to study and pinpoint abnormalities in voluminous data collected over several hours is strenuous and time consuming. METHODS: This paper presents the spatial filling index and time-frequency analysis of heart rate variability signal for disease identification. Renyi's entropy is evaluated for the signal in the Wigner-Ville and Continuous Wavelet Transformation (CWT) domain. RESULTS: This Renyi's entropy gives lower 'p' value for scalogram than Wigner-Ville distribution and also, the contours of scalogram visually show the features of the diseases. And in the time-frequency analysis, the Renyi's entropy gives better result for scalogram than the Wigner-Ville distribution. CONCLUSION: Spatial filling index and Renyi's entropy has distinct regions for various diseases with an accuracy of more than 95%

NEW BACTERIA FORAGING AND PARTICLE SWARM HYBRID ALGORITHM FOR MEDICAL IMAGE COMPRESSION

For perfect diagnosis of brain tumour, it is necessary to identify tumour affected regions in the brain in MRI (Magnetic Resonance Imaging) images effectively and compression of these images for transmission over a communication channel at high speed with better visual quality to the experts. An attempt has been made in this paper for identifying tumour regions with optimal thresholds which are optimized with the proposed Hybrid Bacteria Foraging Optimization Algorithm (BFOA) and Particle Swarm Optimization (PSO) named (HBFOA-PSO) by maximizing the Renyi’s entropy and Kapur’s entropy. BFOA may be trapped into local optimal problem and delay in execution time (convergence time) because of random chemo taxis steps in the procedure of algorithm and to get global solution, a theory of swarming is commenced in the structure of HBFOA-PSO. Effectiveness of this HBFOA-PSO is evaluated on six different MRI images of brain with tumours and proved to be better in Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Fitness Function

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

The paper proposes a robust approach to automatic segmentation of leukocyte‟s nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert haematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm

Optimization Methods for Image Thresholding: A review

Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated

A Diagnosis Feature Space for Condition Monitoring and Fault Diagnosis of Ball Bearings

The problem of fault diagnosis and condition monitoring of ball bearings is a multidisciplinary subject. It involves research subjects from diverse disciplines of mechanical engineering, electrical engineering and in particular signal processing. In the first step, one should identify the correct method of investigation. The methods of investigation for condition monitoring of ball bearings include acoustic emission measurements, temperature monitoring, electrical current monitoring, debris analysis and vibration signal analysis. In this thesis the vibration signal analysis is employed. Once the method of analysis is selected, then features sensitive to faults should be calculated from the signal. While some of the features may be useful for condition monitoring, some of the calculated features might be extra and may not be helpful. Therefore, a feature reduction module should be employed. Initially, six features are selected as a candidate for the diagnosis feature space. After analyzing the trend of the features, it was concluded that three of the features are not appropriate for fault diagnosis. In this thesis, two problem is investigated. First the problem of identifying the effects of the fault size on the vibration signal is investigated. Also the performance of the feature space is tested in distinguishing the healthy ball bearings from the defective vibration signals

Computational methods for the image segmentation of pigmented skin lesions: a review

Background and objectives: Because skin cancer affects millions of people worldwide, computational methods for the segmentation of pigmented skin lesions in images have been developed in order to assist dermatologists in their diagnosis. This paper aims to present a review of the current methods, and outline a comparative analysis with regards to several of the fundamental steps of image processing, such as image acquisition, pre-processing and segmentation. Methods: Techniques that have been proposed to achieve these tasks were identified and reviewed. As to the image segmentation task, the techniques were classified according to their principle. Results: The techniques employed in each step are explained, and their strengths and weaknesses are identified. In addition, several of the reviewed techniques are applied to macroscopic and dermoscopy images in order to exemplify their results. Conclusions: The image segmentation of skin lesions has been addressed successfully in many studies; however, there is a demand for new methodologies in order to improve the efficiency

Computational Analysis of Greek folk music of the Aegean islands

Αν και έχουν αναπτυχθεί νεότερα και πιο ανεπτυγμένα μοντέλα υπολογιστικής μουσικής ανάλυσης με στόχο την αύξηση διαθέσιμης πληροφορίας στον κλάδο της μουσικολογίας, υπάρχει πολύ λίγη έρευνα στην υπολογιστική ανάλυση δημοτικής μουσικής γενικότερα και ελληνικής δημοτικής μουσικής ειδικότερα. Στόχος της παρούσας εργασίας είναι η διερεύνηση ποικίλων τύπων μουσικών χαρακτηριστικών και προτύπων στη δημοτική μουσική των νησιών του Αιγαίου και η παροχή χρήσιμης πληροφορίας σχετικά με τη δομή και το περιεχόμενο του εν λόγω είδους. Επιπρόσθετα, με στόχο τη σύγκριση μουσικών αποσπασμάτων χορών Συρτού και Μπάλου, αλλά και γεωγραφικών περιοχών από τις οποίες προέρχονται, 73 αποσπάσματα συγκεντρώθηκαν συνολικά σε μια βάση δεδομένων και αναλύθηκαν. Η εξαγωγή χαρακτηριστικών και η ανάλυση προτύπων ανέδειξαν μελωδικές και ρυθμικές διαφορές τόσο ανάμεσα στα δύο είδη χορών όσο και στις διάφορες νησιωτικές περιοχές, ενώ υπήρξαν επίσης ποικίλες ομοιότητες σε όλο το σύνολο των δεδομένων.While newer, advanced computational music analysis models have been developed with the intentions of increasing available information in this field, very little research exists on the computational analysis of folk music in general and Greek folk music in specific. The aim of this study was to examine various types of musical features and patterns in the folk music of the Aegean islands and provide useful information about the structure and the content of this music style. In addition, to compare the tunes of Syrtos and Mpalos dances, but also the various island regions from which they originate, a total of 73 tunes were included in the constructed dataset and the analyses. Feature extraction and pattern analysis revealed that there are indeed melodic and temporal differences both between the two dance types and between the island regions, while there were also various important similarities throughout the whole dataset

A survey on wearable sensor modality centred human activity recognition in health care

Increased life expectancy coupled with declining birth rates is leading to an aging population structure. Aging-caused changes, such as physical or cognitive decline, could affect people's quality of life, result in injuries, mental health or the lack of physical activity. Sensor-based human activity recognition (HAR) is one of the most promising assistive technologies to support older people's daily life, which has enabled enormous potential in human-centred applications. Recent surveys in HAR either only focus on the deep learning approaches or one specific sensor modality. This survey aims to provide a more comprehensive introduction for newcomers and researchers to HAR. We first introduce the state-of-art sensor modalities in HAR. We look more into the techniques involved in each step of wearable sensor modality centred HAR in terms of sensors, activities, data pre-processing, feature learning and classification, including both conventional approaches and deep learning methods. In the feature learning section, we focus on both hand-crafted features and automatically learned features using deep networks. We also present the ambient-sensor-based HAR, including camera-based systems, and the systems which combine the wearable and ambient sensors. Finally, we identify the corresponding challenges in HAR to pose research problems for further improvement in HAR