Automatic heart sounds segmentation based on the correlation coefficients matrix for similar cardiac cycles identification

Belmecheri, Mecheri Zeid; Ahfir, Maamar; Kale, I.

doi:10.1016/j.bspc.2018.03.009

Cited by 15 publications

(7 citation statements)

References 10 publications

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“…where the cyclic autocorrelation function R x ( ) x ( ) g a with infinite length of data was given in (7) and (8). In this study, FFT accumulation method (FAM) To reduce the computational burden of calculating CFSD, the data are low-pass filtered and down-sampled to 250 Hz before performing FAM, as down-sampling would hardly influence cardiac sound periodicity [27]. For online separation consideration, the quasi-cyclostationarity of lung sound is not involved.…”

Section: Training With Quasi-cyclostationaritymentioning

confidence: 99%

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

Yang

et al. 2023

Biomed. Phys. Eng. Express

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Objective. Cardiopulmonary auscultation is promising to get smart due to the emerging of electronic stethoscopes. Cardiac and lung sounds often appear mixed at both time and frequency domain, hence deteriorating the auscultation quality and the further diagnosis performance. The conventional cardiopulmonary sound separation methods may be challenged by the diversity in cardiac/lung sounds. In this study, the data-driven feature learning advantage of deep autoencoder and the common quasi-cyclostationarity characteristic are exploited for monaural separation. Approach. Different from most of the existing separation methods that only handle the amplitude of short-time Fourier transform (STFT) spectrum, a complex-valued U-net (CUnet) with deep autoencoder structure, is built to fully exploit both the amplitude and phase information. As a common characteristic of cardiopulmonary sounds, quasi-cyclostationarity of cardiac sound is involved in the loss function for training. Main Results. In experiments to separate cardiac/lung sounds for heart valve disorder auscultation, the averaged achieved signal distortion ratio (SDR), signal interference ratio (SIR), and signal artifact ratio (SAR) in cardiac sounds are 7.84 dB, 21.72 dB, and 8.06 dB, respectively. The detection accuracy of aortic stenosis can be raised from 92.21% to 97.90%. Significance. The proposed method can promote the cardiopulmonary sound separation performance, and may improve the detection accuracy for cardiopulmonary diseases.

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Section: Training With Quasi-cyclostationaritymentioning

confidence: 99%

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

Yang

et al. 2023

Biomed. Phys. Eng. Express

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“…Although this algorithm detected the S1-S2 with 93% accuracy, the algorithm was found noise-sensitive [3]. An adaptive-thresholding based approach for localization of S1 and S2 was proposed by Belmecheri et al, but a loss of information was found due to the computational complexity of the method [4]. Bajelani et al made an effort to detect S1 and S2 based on empirical mode decomposition in which the accuracy of detection of S1-S2 was shown 88.3%, but the method was too noise-prone [5].…”

Section: Introductionmentioning

confidence: 99%

4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction

Nath¹,

Srivastava²

2023

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The recent reports of the World Health Organization showed that a huge extent of the population below 55 years has become much prone to cardiac disease, and the death percentage has increased caused by various cardio vascular diseases (CVD). Moreover, in the Covid-19 pandemic situation, the people suffering from heart disease were found severely vulnerable to viral infections, which proved to be a major cause of increased death percentage. The CVD could be caused by dysfunction of heart valves which could end up with cardiac arrest. The prime method for early-stage detection of the heart valve dysfunction is analysis of major heart sounds occurring in a cardiac cycle. The proposed work dealt with exploration of S1 and S2, which are supposed to be prime sounds of Phonocardiogram (PCG) signal. Here, the proposed analysis has six steps. First, signalacquisition set-up which was assembled for acquiring PCG and ECG signals from the people having age between 15 to 40 years. Second step, pre-processing: in which the samples of PCG and ECG signals were prepared and the signal was denoised using modified Butterworth worth filter. The third step was the incorporation of Empirical Mode Decomposition to get Intrinsic Mode Functions i.e., frequency components of the PCG. Further, only two appropriate IMFs were selected and recombined to generate a combined component signal (CChs). In the fourth step; a Modified Shannon Energy Envelope algorithm (MSEE) i.e., 4th order Shannon energy Envelope was implemented to frame energy envelopes. In the fifth step; an adaptive-thresholding was used for the time-lobes formation followed by peak correction algorithm i.e., correction of time-lobe peaks. In the sixth and final step; time-lobes of the PCG signal were computed and were correlated with R-peaks of ECG signal, through which localization of S1 and S2 was done. A total of 40 samples of the PCG signal consisting of 195 cardiac cycles were taken for the analysis. It came out from the analysis of the self-acquired PCG signal that the best result of localization of S1 and S2 is obtained for the PCG signal acquired from the Pulmonic position. After analyzing the confusion matrix for the findings of the proposed method; accuracy & precision were 90.20%, sensitivity 100%, and an error rate of 9.8% was obtained. The accuracy of the method was found lesser if the PCG was acquired from the remaining three auscultation areas of the human chest. The proposed method was compared with three other earlier algorithms, out of which the proposed method showed a greater improvement. Moreover, the implementation of EMD followed by choosing a few specific IMFs for the formation energy envelope reduced the computation cost and enhanced the accuracy of the method, too.

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“…These tools can be essentially categorized into envelogram-based methods and artificial intelligence-based methods [ 10 , 17 , 18 , 19 , 20 , 21 ]. Envelogram-based methods generally extract an envelope from the PCG signal by using a Shannon energy operator [ 24 , 28 , 29 ], a Hilbert transform [ 30 , 31 , 32 ], or a Teager–Kaiser energy operator [ 26 ], among others [ 33 , 34 ]. A fixed or adaptive threshold is then applied to the envelope to locate the peaks, to therefore identify the boundaries of the signal chunks corresponding to heart sounds.…”

Section: Introductionmentioning

confidence: 99%

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

Centracchio,

Parlato,

Esposito

et al. 2024

Sensors

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Cardiac auscultation is an essential part of physical examination and plays a key role in the early diagnosis of many cardiovascular diseases. The analysis of phonocardiography (PCG) recordings is generally based on the recognition of the main heart sounds, i.e., S1 and S2, which is not a trivial task. This study proposes a method for an accurate recognition and localization of heart sounds in Forcecardiography (FCG) recordings. FCG is a novel technique able to measure subsonic vibrations and sounds via small force sensors placed onto a subject’s thorax, allowing continuous cardio-respiratory monitoring. In this study, a template-matching technique based on normalized cross-correlation was used to automatically recognize heart sounds in FCG signals recorded from six healthy subjects at rest. Distinct templates were manually selected from each FCG recording and used to separately localize S1 and S2 sounds, as well as S1–S2 pairs. A simultaneously recorded electrocardiography (ECG) trace was used for performance evaluation. The results show that the template matching approach proved capable of separately classifying S1 and S2 sounds in more than 96% of all heartbeats. Linear regression, correlation, and Bland–Altman analyses showed that inter-beat intervals were estimated with high accuracy. Indeed, the estimation error was confined within 10 ms, with negligible impact on heart rate estimation. Heart rate variability (HRV) indices were also computed and turned out to be almost comparable with those obtained from ECG. The preliminary yet encouraging results of this study suggest that the template matching approach based on normalized cross-correlation allows very accurate heart sounds localization and inter-beat intervals estimation.

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Automatic heart sounds segmentation based on the correlation coefficients matrix for similar cardiac cycles identification

Cited by 15 publications

References 10 publications

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

4th Order Shannon Energy Envelope Approach for Localization of S1 and S2 for Early-Stage Detection of Heart Valve Dysfunction

Accurate Localization of First and Second Heart Sounds via Template Matching in Forcecardiography Signals

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