Automatic cardiac phase detection of mitral and aortic valves stenosis and regurgitation via localization of active valves

Saeidi, Azra; Almasganj, Farshad; Shojaeifard, Maryam

doi:10.1016/j.bspc.2017.03.005

Cited by 7 publications

(4 citation statements)

References 22 publications

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“…The S2-1 period occurred in early S2, when the ventricles entered diastole, the blood flow decelerated abruptly in the aorta and pulmonary artery impinged on the valves to produce S2. At this time, the aortic and pulmonary valves began to close [22][23][24]. As shown in Figure 13, the modulus of the signals gradually increased during S2-1, and the higher modulus areas were concentrated on the third and fourth intercostal spaces of the left chest, and the second intercostal space at the right edge of the sternum.…”

Section: Cardiac Acoustic Mapmentioning

confidence: 93%

“…The cardiac acoustic maps of S2-2 in Figure 13 correspond to the signals in Figure 11 from 0.482 s to 0.502 s. The S2-2 period was closely connected to the S2-1 period. In this period the aortic and pulmonary valves closed abruptly due to the impact of blood flow, and the heart sounds had high frequency and modulus [23,24]. From Figure 13, the modulus of heart sounds during S2-2 gradually increased to the highest and gradually decreased to lowers.…”

Section: Cardiac Acoustic Mapmentioning

confidence: 96%

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Development of a Multi-Channel Wearable Heart Sound Visualization System

Guo

Tang

Xia

et al. 2022

JPM

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A multi-channel wearable heart sound visualization system based on novel heart sound sensors for imaging cardiac acoustic maps was developed and designed. The cardiac acoustic map could be used to detect cardiac vibration and heart sound propagation. The visualization system acquired 72 heart sound signals and one ECG signal simultaneously using 72 heart sound sensors placed on the chest surface and one ECG analog front end. The novel heart sound sensors had the advantages of high signal quality, small size, and high sensitivity. Butterworth filtering and wavelet transform were used to reduce noise in the signals. The cardiac acoustic map was obtained based on the cubic spline interpolation of the heart sound signals. The results showed the heart sound signals on the chest surface could be detected and visualized by this system. The variations of heart sounds were clearly displayed. This study provided a way to select optimal position for auscultation of heart sounds. The visualization system could provide a technology for investigating the propagation of heart sound in the thoracic cavity.

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Section: Cardiac Acoustic Mapmentioning

confidence: 93%

Section: Cardiac Acoustic Mapmentioning

confidence: 96%

Development of a Multi-Channel Wearable Heart Sound Visualization System

Guo

Tang

Xia

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

“…On the other side, it has demonstrated superior performances in clinical classification tasks compared to its single-channel counterpart. The multi-channel recording was successfully used to classify subjects with coronary artery disease from normal subjects [24][25][26], to identify and classify cardiac murmurs [27,28], and to estimate the Left Ventricular Ejection Fraction [29]. In the context of CTIs, Paiva et al employed a two-channel approach to improve the accuracy of their Pre-Ejection Period estimation by selecting the best channel based on a feature related to signal contrast [30].…”

Section: State Of the Artmentioning

confidence: 99%

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

Giordano

Rosati

Balestra

et al. 2023

Sensors

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The home monitoring of patients affected by chronic heart failure (CHF) is of key importance in preventing acute episodes. Nevertheless, no wearable technological solution exists to date. A possibility could be offered by Cardiac Time Intervals extracted from simultaneous recordings of electrocardiographic (ECG) and phonocardiographic (PCG) signals. Nevertheless, the recording of a good-quality PCG signal requires accurate positioning of the stethoscope over the chest, which is unfeasible for a naïve user as the patient. In this work, we propose a solution based on multi-source PCG. We designed a flexible multi-sensor array to enable the recording of heart sounds by inexperienced users. The multi-sensor array is based on a flexible Printed Circuit Board mounting 48 microphones with a high spatial resolution, three electrodes to record an ECG and a Magneto-Inertial Measurement Unit. We validated the usability over a sample population of 42 inexperienced volunteers and found that all subjects could record signals of good to excellent quality. Moreover, we found that the multi-sensor array is suitable for use on a wide population of at-risk patients regardless of their body characteristics. Based on the promising findings of this study, we believe that the described device could enable the home monitoring of CHF patients soon.

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“…Segmenting the PCGs into the heart cycles or localizing the S1 and S2 heart sounds are the most popular methods in heart sound segmentation. For example, in 2017, Saeidi et al proposed an automatic cardiac phase detection algorithm to detect mitral and aortic valves stenosis and regurgitation based on active valves localization [22]. In this study, they performed the S1 and S2 localization method to segment PCGs into systole and diastole phases.…”

Section: Literature Reviewmentioning

confidence: 99%

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

Mehrabbeik¹,

Rashidi²,

Fallah³

et al. 2020

Biomed. Phys. Eng. Express

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Mitral Valve Prolapse (MVP) is a common condition among people, which is often benign and does not need any serious treatment. However, this doesn’t mean that MVP can’t cause any problems. In malignant conditions, MVP can cause mitral failure and also heart failure. Early diagnosis of MVP is significantly important to control and reduce its complications. Since the phonocardiogram signal provides useful information about heart valves function, it can be used for MVP detection. To detect MVP, the signal was denoised and segmented into heart cycles and constant three-second pieces in the first and second approaches, respectively. Next, based on the Fractional Fourier Transform (FrFT), the desired features were extracted. Then, the extracted features were windowed by a Moving Logarithmic Median Window (MLMW) and optimum features were selected using Mahalanobis, Bhattacharyya, Canberra, and Minkowski distance criteria. Finally, using the selected features, classification was performed by using the K-Nearest Neighbor (KNN) and the Suppor Vector Machine (SVM) classifiers to find out whether a segment is prolapsed. The best results of the experiment on the collected database contain 15 prolapsed and 6 non-prolapsed subjects using the A-test method show 96.25 ± 2.43 accuracy, 98.5 ± 3.37 sensitivity, 94.0 ± 5.16 specificity, 96.0 ± 3.44 precision, 92.5 ± 4.86 kappa, and 96.6 ± 2.34 f-score with the SVM classifier.

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Automatic cardiac phase detection of mitral and aortic valves stenosis and regurgitation via localization of active valves

Cited by 7 publications

References 22 publications

Development of a Multi-Channel Wearable Heart Sound Visualization System

Development of a Multi-Channel Wearable Heart Sound Visualization System

A Wearable Multi-Sensor Array Enables the Recording of Heart Sounds in Homecare

Phonocardiography-based mitral valve prolapse detection with using fractional fourier transform

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