Phonocardiography of the human fetus

Sampson, John J.; McCalla, R.L.; Kerr, Wm. J.

doi:10.1016/s0002-8703(26)90434-1

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1927

2024

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Cited by 14 publications

(2 citation statements)

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“…Their pitch was lower and their con figuration different from those recorded in adults. Subjects with such murmurs in fetal life had perfectly normal hearts after birth [26,27].…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Fetal Heart Sounds and Murmurs in the Second Half of Gestation

Urbach¹,

Carrington²,

Friel³

et al. 1968

Neonatology

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“…Their pitch was lower and their con figuration different from those recorded in adults. Subjects with such murmurs in fetal life had perfectly normal hearts after birth [26,27].…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Fetal Heart Sounds and Murmurs in the Second Half of Gestation

Urbach¹,

Carrington²,

Friel³

et al. 1968

Neonatology

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“…Fetal heart sounds used for many years to detect murmurs in fetal heart sounds [1]. Fetal heart sounds before birth used to detect any change about the status of the fetus.…”

Section: Introductionmentioning

confidence: 99%

Gender of Fetus Identification Using Modified Mel-Frequency Cepstral Coefficients Based on Fractional Discrete Cosine Transform

Azmy

2024

IEEE Access

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Fetal heart sounds is measured to follow the developing status of fetus. The used database of fetal heart sounds is obtained from Physionet challenge. In this paper, novel models are created to extract features from fetal heart sounds; to identify the gender of fetus (male or female), using modified Mel-Frequency Cepstral Coefficients (MMFCC). The high pass filter which is used in the pre-emphasis step of MMFCC, used to remove noise from fetal heart sounds. These models are compared; to obtain high classification parameters (i.e. the accuracy rate and the area under curve). Classification method is based on deep learning, using recurrent neural networks (RNN) based on bidirectional long short-term memory (BiLSTM). The programming code of 13 models is created by the author, using Matlab software environment. Both accuracy rate and area under curve (AUC) are obtained using MMFCC based on fractional discrete cosine transform (FRDCT), overperform the same results of other models. So, this model is selected to extract features of fetal heart sounds. The contribution here is using novel model. FRDCT is new fractional transform, which is decomposed from fractional Fourier transform (FRFT) and discrete cosine transform (DCT). The secret behind selecting FRDCT based on MMFCC as a model; FRDCT decomposes signals perfectly in time-frequency domain. The best model can be programmed on a portable device; to know the gender of fetus by a caregiver in rural areas, without needing to use ultrasound.

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Beitrag zur Frage der fetalen Herzarhythmie

Rihl¹,

Weinzierl²

1927

Arch. Gynak.

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Phonocardiography of the human fetus

Cited by 14 publications

References 5 publications

Characteristics of Fetal Heart Sounds and Murmurs in the Second Half of Gestation

Characteristics of Fetal Heart Sounds and Murmurs in the Second Half of Gestation

Gender of Fetus Identification Using Modified Mel-Frequency Cepstral Coefficients Based on Fractional Discrete Cosine Transform

Beitrag zur Frage der fetalen Herzarhythmie

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