Heart Sounds | ScienceGate

Detection and localization of S1 and S2 heart sounds by 3rd order normalized average Shannon energy envelope algorithm

Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine ◽

10.1177/0954411921998108 ◽

2021 ◽

pp. 095441192199810

Author(s):

Madhwendra Nath ◽

Subodh Srivastava ◽

Niharika Kulshrestha ◽

Dilbag Singh

Keyword(s):

Success Rate ◽

Heart Sound ◽

Heart Diseases ◽

Heart Sounds ◽

Sound Signal ◽

Ecg Signal ◽

Shannon Energy ◽

Detection And Localization ◽

Pcg Signals ◽

Heart Sound Signal

Adults born after 1970s are more prone to cardiovascular diseases. Death rate percentage is quite high due to heart related diseases. Therefore, there is necessity to enquire the problem or detection of heart diseases earlier for their proper treatment. As, Valvular heart disease, that is, stenosis and regurgitation of heart valve, are also a major cause of heart failure; which can be diagnosed at early-stage by detection and analysis of heart sound signal, that is, HS signal. In this proposed work, an attempt has been made to detect and localize the major heart sounds, that is, S1 and S2. The work in this article consists of three parts. Firstly, self-acquisition of Phonocardiogram (PCG) and Electrocardiogram (ECG) signal through a self-assembled, data-acquisition set-up. The Phonocardiogram (PCG) signal is acquired from all the four auscultation areas, that is, Aortic, Pulmonic, Tricuspid and Mitral on human chest, using electronic stethoscope. Secondly, the major heart sounds, that is, S1 and S2are detected using 3rd Order Normalized Average Shannon energy Envelope (3rd Order NASE) Algorithm. Further, an auto-thresholding has been used to localize time gates of S1 and S2 and that of R-peaks of simultaneously recorded ECG signal. In third part; the successful detection rate of S1 and S2, from self-acquired PCG signals is computed and compared. A total of 280 samples from same subjects as well as from different subjects (of age group 15–30 years) have been taken in which 70 samples are taken from each auscultation area of human chest. Moreover, simultaneous recording of ECG has also been performed. It was analyzed and observed that detection and localization of S1 and S2 found 74% successful for the self-acquired heart sound signal, if the heart sound data is recorded from pulmonic position of Human chest. The success rate could be much higher, if standard data base of heart sound signal would be used for the same analysis method. The, remaining three auscultations areas, that is, Aortic, Tricuspid, and Mitral have smaller success rate of detection of S1 and S2 from self-acquired PCG signals. So, this work justifies that the Pulmonic position of heart is most suitable auscultation area for acquiring PCG signal for detection and localization of S1 and S2 much accurately and for analysis purpose.

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Heart Sounds and Murmurs: A Clinical and Phonocardiography Study

The Medical Journal of Australia ◽

10.5694/j.1326-5377.1961.tb69333.x ◽

1961 ◽

Vol 1 (25) ◽

pp. 939-939

Keyword(s):

Heart Sounds

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A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽

10.3390/healthcare9020169 ◽

2021 ◽

Vol 9 (2) ◽

pp. 169

Author(s):

Sergi Gómez-Quintana ◽

Christoph E. Schwarz ◽

Ihor Shelevytsky ◽

Victoriya Shelevytska ◽

Oksana Semenova ◽

...

Keyword(s):

Patent Ductus Arteriosus ◽

Heart Defects ◽

Ductus Arteriosus ◽

Area Under The Curve ◽

Clinical Decision ◽

Heart Sounds ◽

Decision Tree Classifier ◽

Support Tool ◽

Tree Classifier ◽

Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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A two-stage amplified PZT sensor for monitoring lung and heart sounds in discharged pneumonia patients

Microsystems & Nanoengineering ◽

10.1038/s41378-021-00274-x ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Hongbin Chen ◽

Shuai Yu ◽

Haiyang Liu ◽

Jie Liu ◽

Yongguang Xiao ◽

...

Keyword(s):

High Sensitivity ◽

Cost Effective ◽

High Ratio ◽

Heart Sounds ◽

Two Stage ◽

Diagnosis And Prognosis ◽

Sensor Device ◽

Cost Effective Alternative ◽

A Charge ◽

Highly Sensitive Detection

AbstractAssessment of lung and heart states is of critical importance for patients with pneumonia. In this study, we present a small-sized and ultrasensitive accelerometer for continuous monitoring of lung and heart sounds to evaluate the lung and heart states of patients. Based on two-stage amplification, which consists of an asymmetric gapped cantilever and a charge amplifier, our accelerometer exhibited an extremely high ratio of sensitivity to noise compared with conventional structures. Our sensor achieves a high sensitivity of 9.2 V/g at frequencies less than 1000 Hz, making it suitable to use to monitor weak physiological signals, including heart and lung sounds. For the first time, lung injury, heart injury, and both lung and heart injuries in discharged pneumonia patients were revealed by our sensor device. Our sound sensor also successfully tracked the recovery course of the discharged pneumonia patients. Over time, the lung and heart states of the patients gradually improved after discharge. Our observations were in good agreement with clinical reports. Compared with conventional medical instruments, our sensor device provides rapid and highly sensitive detection of lung and heart sounds, which greatly helps in the evaluation of lung and heart states of pneumonia patients. This sensor provides a cost-effective alternative approach to the diagnosis and prognosis of pneumonia and has the potential for clinical and home-use health monitoring.

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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