![]() ![]() Like ECG, PPG can also be used to monitor various cardiac conditions. Another important modality is photoplethysmography (PPG) that employs light-based sensors to estimate the rate of flow of blood by measuring the changes in the reflected/transmitted light. The middle wave of ECG is known as ‘QRS’ complex that, in general, comprises three deflections ‘Q’ (the first negative deflection) ‘R’ (the first positive deflection), and ‘S’ (the negative deflection following the ‘R’ wave). The electrical activity of heart is recorded in the form of electrocardiogram which is composed of three main waves, ‘P’ wave, ‘QRS’ complex, and ‘T’ wave. The most popular of these is the electrocardiogram or ECG. Different modalities are known to exist to monitor the health of heart. According to surveys conducted by the World Health Organization (WHO), 33% of all deaths are the result of CVDs. Heart-related diseases, known as cardiovascular diseases (CVD), are responsible for a major proportion of deaths all around the world. Healthy heart is very important for the normal day to day working of human body as blood carries important nutrients to the organs. During the pumping action, electrical and mechanical activities are carried out resulting in the flow of blood. Human heart is the most important organ in the body that provides blood to all parts of the body using a pump-like action. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. ![]()
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