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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...
Non-invasive cuff-less Blood Pressure (BP) estimation from Photoplethysmogram (PPG) is a well known challenge in the field of affordable healthcare. This paper presents a set of improvements over an existing method that estimates BP using 2-element Windkessel model from PPG signal. A noisy PPG corpus is collected using fingertip pulse oximeter, from two different locations in India. Exhaustive pre-processing...
Heart rate variability (HRV) measures the instantaneous change in heart rate and is an important marker for checking physical condition as well as mental stress of a person. In this paper, we propose a methodology to calculate HRV of a person using smart phone audio. Heart sound is captured in the inbuilt microphone of a smart phone, by placing the device on the chest of the person. We propose a process...
Photoplethysmography (PPG) signals, captured using smart phones are generally noisy in nature. Although they have been successfully used to determine heart rate from frequency domain analysis, further indirect markers like blood pressure (BP) require time domain analysis for which the signal needs to be substantially cleaned. In this paper we propose a methodology to clean such noisy PPG signals....
Smartphone based reflective Photoplethysmography (PPG) measures reflected light from blood capillaries, typically from the fingertip of a user. It has gained popularity as a means of unobtrusive affordable physiological sensing. The orientation and relative distance between smartphone flash and camera; fingertip placement direction etc. highly influences the captured PPG signal quality, and hence...
Simple and non-invasive methods to estimate vital signs are very important for preventive healthcare. In this paper, we present a methodology to estimate Blood Pressure (BP) using Photoplethysmography (PPG). Instead of directly relating systolic and diastolic BP values with PPG features, our proposed methodology initially maps PPG features with some person specific intermediate latent parameters and...
This paper presents a simple method to indirectly estimate the range of certain important electrocardiogram (ECG) parameters using photoplethysmography (PPG). The proposed method, termed as PhotoECG, extracts a set of time and frequency domain features from fingertip PPG signal. A feature selection algorithm utilizing the concept of Maximal Information Coefficient (MIC) is presented to rank the PPG...
This paper describes a system for predicting water logging prone areas in multiple routes. The approach is based on the theory that water tends to accumulate in low-lying areas and hence a route which contains more and bigger basins is more likely to behave worse on a rainy day. Using this basic principle, algorithms are formulated and applied to identify and quantify water logging zones on a route...
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