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Proceedings ArticleDOI

An algorithm for automatic segmentation of heart sound signal acquired using seismocardiography

TL;DR: In this paper, a novel algorithm for automatic segmentation of the heart sound signal is proposed, which uses a sensor called accelerometer, which is of small size and low weight and thus convenient to wear.
Abstract: Automatic diagnosis of the heart valve diseases generally requires the segmentation of heart sound signal. Henceforth, in this paper a novel algorithm for automatic segmentation of the heart sound signal is proposed. The heart sound signal is acquired using seismocardiography (SCG), which uses a sensor called accelerometer. The accelerometer is of small size and low weight and thus convenient to wear. The proposed algorithm performs in three steps. First, the signal is filtered using the developed denoising algorithm based on discrete wavelet transform. The computational complexity of this algorithm is reduced by processing only two levels, which are expected to have heart sound signal, and other levels are discarded. To improve the performance of denoising, an adaptive threshold is obtained for both the levels separately, and applied. Then, the denoised signal is obtained by reconstructing the thresholded coefficients. In the second step, peaks are detected in the denoised signal using an adaptive threshold, obtained using Otsu's method. Then, false detected peaks and noise contaminated parts of the signal are identified and discarded from further analyses. In the third step, the heart sound components are identified as S1, and S2 based on the energy of the particular component and segmentation is performed. The results of denoising, show that the developed algorithm outperforms the existing method. Further, the segmentation results show that the developed algorithm is able to identify the heart sound components, accurately, even in the presence of noise.
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Journal ArticleDOI
14 Jan 2019
TL;DR: This paper reviews the recent advances in the field of SCG and focuses on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification.
Abstract: Cardiovascular disease is a major cause of death worldwide. New diagnostic tools are needed to provide early detection and intervention to reduce mortality and increase both the duration and quality of life for patients with heart disease. Seismocardiography (SCG) is a technique for noninvasive evaluation of cardiac activity. However, the complexity of SCG signals introduced challenges in SCG studies. Renewed interest in investigating the utility of SCG accelerated in recent years and benefited from new advances in low-cost lightweight sensors, and signal processing and machine learning methods. Recent studies demonstrated the potential clinical utility of SCG signals for the detection and monitoring of certain cardiovascular conditions. While some studies focused on investigating the genesis of SCG signals and their clinical applications, others focused on developing proper signal processing algorithms for noise reduction, and SCG signal feature extraction and classification. This paper reviews the recent advances in the field of SCG.

145 citations

01 Jan 2018

19 citations


Cites background from "An algorithm for automatic segmenta..."

  • ...However, a few studies have utilized/proposed more advanced noise removal techniques [22,33,86,93,99,100]....

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  • ...[84,85] 1-Acc DS1104, DSPACE xiphoid process [86] 3-Acc ADXL 335, Analog Devices chest wall [46] 3-SP-Acc iPhone6, Apple midclavicular line and 4th intercostal space, belly above navel [29,87] 3-Acc 356A32, PCB Piezotronics left sternal border along the 4th intercostal space [88] 3-Acc X6-2mini, GCDC left sternal border along the 4th intercostal space [71] 1-MEMS-Acc SCA620, Murata Electronic sternum – anterior chest...

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References
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Journal ArticleDOI

37,017 citations


"An algorithm for automatic segmenta..." refers methods in this paper

  • ...Then, for each window, the threshold is set to half of the obtained threshold using Otsu’s method [20]....

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  • ...The Otsu’s method selects a threshold to classify a set of data into two classes, such that the intra-class variance will be minimum [20]....

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Journal ArticleDOI
TL;DR: In this paper, the authors assess the prevalence and effect of valve disease on overall survival in the general population and find that moderate or severe valve disease is common in this population and increase with age.

3,468 citations

Journal Article
TL;DR: Moderate or severe valvular heart diseases are notably common in this population and increase with age and in the community, women are less often diagnosed than are men, which could indicate an important imbalance in view of the associated lower survival.

3,210 citations


"An algorithm for automatic segmenta..." refers background in this paper

  • ...Heart valve disease is one of the cardiovascular disease (CVD), which is increasing every year [1]....

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Journal ArticleDOI
TL;DR: The global burden of IHD deaths has shifted to low-and-middle income countries as lifestyles approach those of high income countries, and the progressive decline in age-standardised IHD mortality in high-income countries shows that increasing I HD mortality is not inevitable.

744 citations


"An algorithm for automatic segmenta..." refers background in this paper

  • ...The situation is worse in the low-and-middle income countries, where these diseases account for more than 80% of global burden [3]....

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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


"An algorithm for automatic segmenta..." refers methods in this paper

  • ...Envelop has been extracted using Shannon energy [15], Shannon entropy [16], normalized average Shannon entropy, Hilbert transform, and cardiac sound characteristic waveform (CSCW) [17]....

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