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Phonocardiogram

About: Phonocardiogram is a research topic. Over the lifetime, 1109 publications have been published within this topic receiving 15700 citations. The topic is also known as: PCG & phonocardiography.


Papers
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Journal ArticleDOI
TL;DR: It is postulated that a defect in the mechanical performance of the heart is responsible for the abnormal systolic time intervals in human heart failure.
Abstract: The duration of the systolic time intervals in nondigitalized patients with heart failure was determined from simultaneous fast speed recordings of the electrocardiogram, phonocardiogram, and carotid arterial pulsation. These were compared with the systolic time intervals corrected for heart rate and sex in 211 normal subjects. The failing left ventricle is characterized by a prolongation in the systolic pre-ejection period and a diminution in the left ventricular ejection time while total electromechanical systole remains relatively unaltered. Both components of the pre-ejection period, the Q-1 interval and the isovolumic contraction time, were prolonged. These alterations in the phases of systole occur in the absence of a measurable change in ventricular depolarization time. The prolongation in the pre-ejection period is well correlated with the reduced stroke volume and cardiac output in heart failure and is independently augmented by high levels of arterial pressure. The abbreviation in left ventricul...

1,272 citations

Journal ArticleDOI
TL;DR: A public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016, which comprises nine different heart sound databases sourced from multiple research groups around the world is described.
Abstract: In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

477 citations

Proceedings ArticleDOI
07 Sep 1997
TL;DR: A segmentation algorithm which separates the heart sound signal into four parts: the first heart sound, the systole, the second heart sound and the diastole is described, based on the normalized average Shannon energy of a PCG signal.
Abstract: Desribes the development of a segmentation algorithm which separates the heart sound signal into four parts: the first heart sound, the systole, the second heart sound and the diastole. The segmentation of phonocardiogram (PCG) signals is the first step of analysis and the most important procedure in the automatic diagnosis of heart sounds. This algorithm is based on the normalized average Shannon energy of a PCG signal. The performance of the algorithm has been evaluated using 515 periods of PCG signals recording from 37 objects including normal and abnormal. The algorithm has achieved a 93 percent correct ratio.

387 citations

Journal ArticleDOI
TL;DR: This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation, and implements a modified Viterbi algorithm for decoding the most likely sequence of states.
Abstract: The identification of the exact positions of the first and second heart sounds within a phonocardiogram (PCG), or heart sound segmentation, is an essential step in the automatic analysis of heart sound recordings, allowing for the classification of pathological events. While threshold-based segmentation methods have shown modest success, probabilistic models, such as hidden Markov models, have recently been shown to surpass the capabilities of previous methods. Segmentation performance is further improved when a priori information about the expected duration of the states is incorporated into the model, such as in a hidden semi-Markov model (HSMM). This paper addresses the problem of the accurate segmentation of the first and second heart sound within noisy real-world PCG recordings using an HSMM, extended with the use of logistic regression for emission probability estimation. In addition, we implement a modified Viterbi algorithm for decoding the most likely sequence of states, and evaluated this method on a large dataset of 10 172 s of PCG recorded from 112 patients (including 12 181 first and 11 627 second heart sounds). The proposed method achieved an average $F_{1}$ score of 95.63 $\,\pm \,$ 0.85%, while the current state of the art achieved 86.28 $\pm \,$ 1.55% when evaluated on unseen test recordings. The greater discrimination between states afforded using logistic regression as opposed to the previous Gaussian distribution-based emission probability estimation as well as the use of an extended Viterbi algorithm allows this method to significantly outperform the current state-of-the-art method based on a two-sided paired t-test.

366 citations

Proceedings ArticleDOI
14 Sep 2016
TL;DR: The authors' classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.
Abstract: The goal of the 2016 PhysioNet/CinC Challenge is the development of an algorithm to classify normal/abnormal heart sounds. A total of 124 time-frequency features were extracted from the phonocardiogram (PCG) and input to a variant of the AdaBoost classifier. A second classifier using convolutional neural network (CNN) was trained using PCGs cardiac cycles decomposed into four frequency bands. The final decision rule to classify normal/abnormal heart sounds was based on an ensemble of classifiers combining the outputs of AdaBoost and the CNN. The algorithm was trained on a training dataset (normal= 2575, abnormal= 665) and evaluated on a blind test dataset. Our classifier ensemble approach obtained the highest score of the competition with a sensitivity, specificity, and overall score of 0.9424, 0.7781, and 0.8602, respectively.

230 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202352
2022122
202170
202086
201975
201876