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Automatic classification of normal and abnormal heart sounds is a popular area of research. However, building a robust algorithm unaffected by signal quality and patient demography is a challenge. In this paper we have analysed a wide list of Phonocardiogram (PCG) features in time and frequency domain along with morphological and statistical features to construct a robust and discriminative feature...
Identification of pulmonary diseases comprises of accurate auscultation as well as elaborate and expensive pulmonary function tests. Prior arts have shown that pulmonary diseases lead to abnormal lung sounds such as wheezes and crackles. This paper introduces novel spectral and spectrogram features, which are further refined by Maximal Information Coefficient, leading to the classification of healthy...
Phonocardiogram (PCG) or auscultation via a stethoscope forms the basis of preliminary medical screening. But PCG recorded in an uncontrolled environment is inherently noisy. In this paper we have derived novel features from the spectral domain and autocorrelation waveforms. These are used to identify the quality of a PCG recording and accepting only diagnosable quality recordings for further analysis...
This paper presents a demo proposal of a standalone smartphone application that can automatically analyse the signal quality of PCG, as it is recorded on a low-cost smartphonebased digital stethoscope. Features, related to the inherent pattern of the autocorrelated signal envelope, have been used for classifying and discarding the noisy portions from a continuous PCG. Our application has been successfully...
Analysis of heart sounds is a popular research area for non invasive identification of several heart diseases. This paper proposes a set of 88 time-frequency features along with five different methodologies for classifying normal and abnormal heart sounds. State of the art approach was applied for segregating the fundamental heart sounds. Apart from a baseline two class classifier, separate classifiers...
The alarming statistics of Diabetes Mellitus (DM) Type 2 as the most common and prevalent disease in India and world over [1] has fuelled research in the direction of non-invasive and continuous monitoring of this disease. This paper describes a demonstration of an inexpensive mobile-phone based android application which can collect Photoplethysmogram (PPG) from fingertip via built-in camera and flash...
Anomaly detection is the identification of an anomaly with regards to misplaced objects or intruders in an indoor environment. It can be done by mapping a room along with its stationary objects with an ultrasonic acoustic frequency response. In this paper, we explore the feasibility of using active acoustic imagery in air and simulate phased array beam forming techniques to achieve a suitable acoustic...
In this paper we present a different methodology for an acoustic source localization in an unknown environment. The developed methodology includes an acoustic based localization system, a converging target localization based on the recursive direction of arrival (DOA) error minimization, and a regressive obstacle avoidance function. Our method is able to augment the existing proven localization techniques...
Mobile smartphones have revolutionized the concept of mobile phones as different apps are built to offer various interesting applications in healthcare, gaming, etc. rather than using the phone only for voice services. The application developers take advantage of onboard sensors, web connectivity and powerful processing units of the smartphones to develop such interesting apps. In this paper, we present...
Robotic platforms are becoming increasingly important and have their utility and cost completely justified during missions which require substituting humans. In this paper, we present our ongoing work in developing a multi-sensor robotic platform intended for deploying in an indoor environment in hazardous situations. The prime objective of such portable robots will be to conduct surveillance missions...
Robot audition systems are expected to support a variety of civilian and rescue applications in hazardous situations. Sensed data can only be interpreted meaningfully when referenced to the location of the sensor, making localization an important area of research. In this paper, we have developed sound source localization system for our Fire Bird VI robot. Localization algorithms like cross correlation...
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