Detection of heart sounds S1 and S2 using optimized S-transform and back — Propagation Algorithm

Shivhare, Vishal Kumar; Sharma, Sanjeev; Shakya, Devendra Kumar

doi:10.1109/ibss.2015.7456661

Cited by 5 publications

(2 citation statements)

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“…Fortunately, most of these advanced methods can also be used to detect fHS in the extracted fPCG signal. These include for example methods based on energy detection and analysis [69]- [71], [74], [75], duration-dependent hidden Markov model (DHMM) [76], Hilbert transform [77], Gaussian regression [78], or EEMD algorithm in combination with the kurtosis features [79].…”

Section: Heart Sounds Detectionmentioning

confidence: 99%

A Review of Recent Advances and Future Developments in Fetal Phonocardiography

Kahánková

Mikolasova

Jaroš

et al. 2023

IEEE Rev. Biomed. Eng.

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Fetal phonocardiography (fPCG) is receiving attention as it is a promising method for continuous fetal monitoring due to its non-invasive and passive nature. However, it suffers from the interference from various sources, overlapping the desired signal in the time and frequency domains. This paper introduces the state-of-the-art methods used for fPCG signal extraction and processing, as well as means of detection and classification of various features defining fetal health state. It also provides an extensive summary of remaining challenges, along with the practical insights and suggestions for the future research directions.

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Section: Heart Sounds Detectionmentioning

confidence: 99%

A Review of Recent Advances and Future Developments in Fetal Phonocardiography

Kahánková

Mikolasova

Jaroš

et al. 2023

IEEE Rev. Biomed. Eng.

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“…Content may change prior to final publication. [48] used an S1 and S2 sound detection and classification algorithm consisting of 4 steps. In the first step, all sounds were localized using optimized S-transform, then the detection of S1 and S2 was performed based on Shannon energy of Stransform, followed by feature extraction using singular value decomposition (SVD) and classification using artificial neural network (ANN).…”

Section: B Detection Of Heart Soundsmentioning

confidence: 99%

Passive Fetal Monitoring by Advanced Signal Processing Methods in Fetal Phonocardiography

et al. 2020

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Fetal phonocardiography (fPCG) is a non-invasive technique for detection of fetal heart sounds (fHSs), murmurs and vibrations. This acoustic recording is passive and provides an alternative lowcost method to ultrasonographic cardiotocography (CTG). Unfortunately, the fPCG signal is often disturbed by the wide range of artifacts that make it difficult to obtain significant diagnostic information from this signal. The study focuses on the filtering of an fPCG signal containing three types of noise (ambient noise, Gaussian noise, and movement artifacts of the mother and the fetus) having different amplitudes. Three advanced signal processing methods: empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and adaptive wavelet transform (AWT) were tested and compared. The evaluation of the extraction was performed by determining the accuracy of S1 sounds detection and by determining the fetal heart rate (fHR). The evaluation of the effectiveness of the method was performed using signal-to-noise ratio (SNR), mean error of heart interval measurement (|∆T i |), and the statistical parameters of accuracy (ACC), sensitivity (SE), positive predictive value (PPV), and harmonic mean between SE and PPV (F1). Using the EMD method, ACC > 95% was achieved in 7 out of 12 types and levels of interference with average values of ACC = 88.73%, SE = 91.57%, PPV = 94.80% and F1 = 93.12%. Using the EEMD method, ACC > 95% was achieved in 9 out of 12 types and levels of interference with average values of ACC = 97.49%, SE = 97.89%, PPV = 99.53% and F1 = 98.69%. In this study, the best results were achieved using the AWT method, which provided ACC > 95% in all 12 types and levels of interference with average values of ACC = 99.34%, SE = 99.49%, PPV = 99.85% a F1 = 99.67%. INDEX TERMSFetal phonocardiography (fPCG), fetal heart rate (fHR), non-invasive fetal monitoring, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), adaptive wavelet transform (AWT).

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