Статті в журналах: "PCG signals"

1

Damani, Devanshi N., Divaakar Siva Baala Sundaram, Shivam Damani, Anoushka Kapoor, Adelaide M. Arruda Olson, and Shivaram P. Arunachalam. "INVESTIGATION OF SYNCHRONIZED ACQUISITION OF ELECTROCARDIOGRAM AND PHONOCARDIOGRAM SIGNALS TOWARDS ELECTROMECHANICAL PROFILING OF THE HEART." Biomedical Sciences Instrumentation 57, no. 2 (April 1, 2021): 305–12. http://dx.doi.org/10.34107/yhpn9422.04305.

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Cardiac diseases are the leading cause of death in the world. Electrocardiogram (ECG and Phonocardiogram (PCG signals play a significant role in the diagnosis of various cardiac diseases. Simultaneous acquisition of ECG and PCG signals can open new avenues of signal processing approaches for electromechanical profiling of the heart. However, there are no standard approaches to ensure high fidelity synchronous data acquisition to enable the development of such novel technologies. In this work, the authors report results on various data capture positions that could lead to standardization of simultaneous ECG and PCG data collection. Presence of lung sounds, variations in posture, depth and frequency of breathing can lead to differences in the ECG-PCG signals recorded. This necessitates a standard approach to record and interpret the data collected. The authors recorded ECG-PCG simultaneously in six healthy subjects using a digital stethoscope to understand the differences in signal quality in various recording positions (prone, supine, bending, semi recumbent, standing, left lateral and sitting with normal and deep breathing conditions. The collected digitized signals are processed offline for signal quality using custom MATLAB software for SNR. The results indicate minimal differences in signal quality across different recording positions. Validation of this technique with larger dataset is required. Future work will investigate changes in characteristic ECG and PCG features due to position and breathing patterns.

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2

DEBBAL, S. M. "Heart Cardiac Sounds analysis using the Wigner distribution (WD) Method." Clinical Cardiology and Cardiovascular Interventions 04, no. 15 (September 20, 2021): 01–04. http://dx.doi.org/10.31579/2641-0419/216.

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This paper is concerned a “The Wigner distribution (WD)” analysis of the Heart cardiac (or phonocardiogram signals: PCG). The Wigner distribution (WD) and the corresponding WVD (Wigner Ville Distribution) have shown good performances in the analysis of non-stationary and quantitative measurements of the time-frequency PCG signal characteristics. It is shown that these transforms provides enough features of the PCG signals that will help clinics to obtain diagnosis.

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3

DEBBAL, S. M., and F. BEREKSI-REGUIG. "DISCRIMINATION OF PATHOLOGICAL CASES OF THE CARDIACS SOUNDS SIGNALS BY THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 05, no. 04 (December 2005): 517–30. http://dx.doi.org/10.1142/s0219519405001679.

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In order to highlight the cardiac sounds or phonocardiogram (PCG) signals analysis according to their added murmur importance, we try to apply the wavelet transform in its multi resolution analysis version. We then look for reconstruction error between the original signal and the synthesized signal. In this case, the original PCG signal is decomposed over seven levels and the seventh detail of decomposition is considered as the synthesized signal. According to the results we obtain, the reconstruction error can be considered as an important parameter in the classification and discrimination of the pathological severity of the PCG signals.

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4

Pauline, S. Hannah, Samiappan Dhanalakshmi, R. Kumar, R. Narayanamoorthi, and Khin Wee Lai. "A Low-Cost Multistage Cascaded Adaptive Filter Configuration for Noise Reduction in Phonocardiogram Signal." Journal of Healthcare Engineering 2022 (April 30, 2022): 1–24. http://dx.doi.org/10.1155/2022/3039624.

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Phonocardiogram (PCG), the graphic recording of heart signals, is analyzed to determine the cardiac mechanical function. In the recording of PCG signals, the major problem encountered is the corruption by surrounding noise signals. The noise-corrupted signal cannot be analyzed and used for advanced processing. Therefore, there is a need to denoise these signals before being employed for further processing. Adaptive Noise Cancellers are best suited for signal denoising applications and can efficiently recover the corrupted PCG signal. This paper introduces an optimal adaptive filter structure using a Sign Error LMS algorithm to estimate a noise-free signal with high accuracy. In the proposed filter structure, a noisy signal is passed through a multistage cascaded adaptive filter structure. The number of stages to be cascaded and the step size for each stage are adjusted automatically. The proposed Variable Stage Cascaded Sign Error LMS (SELMS) adaptive filter model is tested for denoising the fetal PCG signal taken from the SUFHS database and corrupted by Gaussian and colored pink noise signals of different input SNR levels. The proposed filter model is also tested for pathological PCG signals in the presence of Gaussian noise. The simulation results prove that the proposed filter model performs remarkably well and provides 8–10 dB higher SNR values in a Gaussian noise environment and 2-3 dB higher SNR values in the presence of colored noise than the existing cascaded LMS filter models. The MSE values are improved by 75–80% in the case of Gaussian noise. Further, the correlation between the clean signal and its estimate after denoising is more than 0.99. The PSNR values are improved by 7 dB in a Gaussian noise environment and 1-2 dB in the presence of pink noise. The advantage of using the SELMS adaptive filter in the proposed filter model is that it offers a cost-effective hardware implementation of Adaptive Noise Canceller with high accuracy.

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5

DEBBAL, S. M., and F. BEREKSI-REGUIG. "HEARTBEAT SOUND ANALYSIS WITH THE WAVELET TRANSFORM." Journal of Mechanics in Medicine and Biology 04, no. 02 (June 2004): 133–41. http://dx.doi.org/10.1142/s0219519404000916.

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This paper is concerned with a synthesis study of the continuous wavelet transform (CWT) in analyzing the phonocardiogram (PCG). It is shown that the CWT provides enough features of the PCG signals that will help physicians to obtain qualitative and quantitative measurements of the time and the time-frequency PCG signal characteristics, and consequently aid to diagnosis.

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6

DEBBAL, S. M., F. BEREKSI-REGUIG, and A. MEZIANE TANI. "THE FAST FOURIER TRANSFORM AND THE CONTINUOUS WAVELET TRANSFORM ANALYSIS OF THE PHONOCARDIOGRAM SIGNAL." Journal of Mechanics in Medicine and Biology 04, no. 03 (September 2004): 257–72. http://dx.doi.org/10.1142/s0219519404001028.

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This paper is concerned with a synthesis study of the fast Fourier transform (FFT) and the continuous wavelet transform (CWT) in analysing the phonocardiogram signal (PCG). It is shown that the continuous wavelet transform provides enough features of the PCG signals that will help clinics to obtain qualitative and quantitative measurements of the time-frequency PCG signal characteristics and consequently aid to diagnosis. Similary, it is shown that the frequency content of such a signal can be determined by the FFT without difficulties.

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7

YANG, LIJUN, SHUANG LI, ZHI ZHANG, and XIAOHUI YANG. "CLASSIFICATION OF PHONOCARDIOGRAM SIGNALS BASED ON ENVELOPE OPTIMIZATION MODEL AND SUPPORT VECTOR MACHINE." Journal of Mechanics in Medicine and Biology 20, no. 01 (February 2020): 1950062. http://dx.doi.org/10.1142/s0219519419500623.

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The prevention and diagnosis of cardiovascular diseases have become one of the primary problems in the medical community since the mortality of this kind of diseases accounts for 31% of global deaths in 2016. Heart sound, which is an important physiological signal of human body, mainly comes from the pulsing of cardiac structures and blood turbulence. The analysis of heart sounds plays an irreplaceable role in early diagnosis of heart disease since they contain a large amount of pathological information about each part of human heart. Heart sounds can be detected and recorded by Phonocardiogram (PCG). As a noninvasive method to detect and diagnose heart disease, PCG signals have been paid more and more attention by researchers. In this paper, a novel envelope extraction model is proposed and used to estimate the cardiac cycle of each PCG signal. We present a strategy combining empirical mode decomposition (EMD) technique and the proposed envelope model to extract the time-domain features. After applying EMD process to each PCG signal, the second intrinsic mode function is chosen for further analysis. Based on the proposed envelope model, the cardiac cycles of PCG signals can be estimated and then the time-domain features can be extracted. Combining with the frequency-domain features and wavelet-domain features, the feature vectors are obtained. Finally, the support vector machine (SVM) classifier is used to detect the normal and abnormal PCG signals. Two public datasets are used to test our framework in this paper. And classification accuracies of more than [Formula: see text] on both datasets show the effectiveness of the proposed model.

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8

Chien, Ying-Ren, Kai-Chieh Hsu, and Hen-Wai Tsao. "Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications." Applied Sciences 10, no. 17 (August 24, 2020): 5842. http://dx.doi.org/10.3390/app10175842.

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Phonocardiography (PCG) signals that can be recorded using the electronic stethoscopes play an essential role in detecting the heart valve abnormalities and assisting in the diagnosis of heart disease. However, it consumes more bandwidth when transmitting these PCG signals to remote sites for telecare applications. This paper presents a deep convolutional autoencoder to compress the PCG signals. At the encoder side, seven convolutional layers were used to compress the PCG signals, which are collected on the patients in the rural areas, into the feature maps. At the decoder side, the doctors at the remote hospital use the other seven convolutional layers to decompress the feature maps and reconstruct the original PCG signals. To confirm the effectiveness of our method, we used an open accessed dataset on PHYSIONET. The achievable compress ratio (CR) is 32 when the percent root-mean-square difference (PRD) is less than 5%.

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9

Aziz, Sumair, Muhammad Umar Khan, Majed Alhaisoni, Tallha Akram, and Muhammad Altaf. "Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features." Sensors 20, no. 13 (July 6, 2020): 3790. http://dx.doi.org/10.3390/s20133790.

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Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

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10

Berraih, Sid Ahmed, Yettou Nour Elhouda Baakek, and Sidi Mohammed El Amine Debbal. "Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals." Polish Journal of Medical Physics and Engineering 27, no. 1 (March 1, 2021): 73–85. http://dx.doi.org/10.2478/pjmpe-2021-0010.

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Abstract Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.

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11

Arora, Vinay, Rohan Singh Leekha, and Inderveer Chana. "An Efficacy of Spectral Features with Boosted Decision Tree Algorithm for Automatic Heart Sound Classification." Journal of Medical Imaging and Health Informatics 11, no. 2 (February 1, 2021): 513–28. http://dx.doi.org/10.1166/jmihi.2021.3287.

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This research work aims to classify the audio signals received from heart into normal/abnormal. The heart sound perceived has been referred as phonocardiogram (PCG) signals. An attempt has been made to identify a set of features that provide more accurate results for classifying PCG under designated categories using a variant of decision tree algorithm. After applying 6th order butter worth band-pass filter on PCG signals, the new features, viz. Tonnetz, Spectral contrast, and Chroma have been extracted. Further, XGBOOST, a variant of the decision tree has been used for classifying unsegmented PCG signals. The benchmark datasets, PhysioNet 2016, and PASCAL 2011 have been taken for validating the proposed methodology presented here. PhysioNet 2016 is comprised of sub-datasets, viz. A–F which contain a total of 3,240 PCG recordings, whereas the PASCAL 2011 contains 415 heart sound signals. The proposed approach considers a new feature set in conjunction with the existing ones; and it has resulted in mean accuracy, sensitivity, and specificity scores as 95.2, 94.22 and 96.18 respectively.

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12

Rouis, Mohamed, Abdelkrim Ouafi, and Salim Sbaa. "Optimal level and order detection in wavelet decomposition for PCG signal denoising." Biomedical Engineering / Biomedizinische Technik 64, no. 2 (April 24, 2019): 163–76. http://dx.doi.org/10.1515/bmt-2018-0001.

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Abstract The recorded phonocardiogram (PCG) signal is often contaminated by different types of noises that can be seen in the frequency band of the PCG signal, which may change the characteristics of this signal. Discrete wavelet transform (DWT) has become one of the most important and powerful tools of signal representation, but its effectiveness is influenced by the issue of the selected mother wavelet and decomposition level (DL). The selection of the DL and the mother wavelet are the main challenges. This work proposes a new approach for finding an optimal DL and optimal mother wavelet for PCG signal denoising. Our approach consists of two algorithms designed to tackle the problems of noise and variability caused by PCG acquisition in a real clinical environment for different categories of patients. The results obtained are evaluated by examining the coherence analysie (Coh) correlation coefficient (Corr) and the mean square error (MSE) and signal-to-noise ratio (SNR) in simulated noisy PCG signals. The experimental results show that the proposed method can effectively reduce noise.

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13

Tiwari, Anita Devi, Abhishek Misal, and G. R. Sinha. "Analysis of PCG Signals Using Daubechies Wavelet Family." i-manager's Journal on Communication Engineering and Systems 2, no. 2 (April 15, 2013): 23–29. http://dx.doi.org/10.26634/jcs.2.2.2243.

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14

ARORA, VINAY, EDDIE YIN-KWEE NG, ROHAN SINGH LEEKHA, KARUN VERMA, TAKSHI GUPTA, and KATHIRAVAN SRINIVASAN. "HEALTH OF THINGS MODEL FOR CLASSIFYING HUMAN HEART SOUND SIGNALS USING CO-OCCURRENCE MATRIX AND SPECTROGRAM." Journal of Mechanics in Medicine and Biology 20, no. 06 (August 2020): 2050040. http://dx.doi.org/10.1142/s0219519420500402.

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Cardiovascular diseases have become one of the world’s leading causes of death today. Several decision-making systems have been developed with computer-aided support to help the cardiologists in detecting heart disease and thereby minimizing the mortality rate. This paper uses an unexplored sub-domain related to textural features for classifying phonocardiogram (PCG) as normal or abnormal using Grey Level Co-occurrence Matrix (GLCM). The matrix has been applied to extract features from spectrogram of the PCG signals taken from the Physionet 2016 benchmark dataset. Random Forest, Support Vector Machine, Neural Network, and XGBoost have been applied to assess the status of the human heart using PCG signal spectrogram. The result of GLCM is compared with the two other textural feature extraction methods, viz. structural co-occurrence matrix (SCM), and local binary patterns (LBP). Experimental results have proved that applying machine learning model to classify PCG signal on the dataset where GLCM has extracted the feature-set, the accuracy attained is greater as compared to its peer approaches. Thus, this methodology can go a long way to help the medical specialists in precisely and accurately assessing the heart condition of a patient.

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Omari, Tahar, and Fethi Bereksi-Reguig. "An automatic wavelet denoising scheme for heart sounds." International Journal of Wavelets, Multiresolution and Information Processing 13, no. 03 (May 2015): 1550016. http://dx.doi.org/10.1142/s0219691315500162.

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Phonocardiograms (PCGs), recording of heart sounds, have many advantages over traditional auscultation in that they may be replayed and analyzed for spectral and frequency information. PCG is not a widely used diagnostic tool as it could be. One of the major problems with PCG is noise corruption. Many sources of noise may pollute a PCG signal including lung and breath sounds, environmental noise and blood flow noises which are known as murmurs. These murmurs contain many information on heart hemodynamic which can be used particularly in detecting of heart valve diseases. An automated system for heart murmurs processing can be an important tool in diagnostic of heart diseases using a simple electronic stethoscope. However, the first step before developing any automated system is the segmentation of the PCG signals from which the murmurs can be separated. A robust segmentation algorithm must have a robust denoising technique, where, wavelet transform (WT) is among the ones which exhibits very high satisfactory results in such situations. However, the selection of level of decomposition and the mother wavelet are the major challenges. This paper proposes a novel approach for an automatic selection of mother wavelet and level of decomposition that can be used in heart sounds denoising. The obtained results on both simulative and real PCG signals showed that the proposed approach can successfully select the best level of decomposition with the best mother wavelet that can be used in extraction operation of main PCG sound components (S1 and S2) from various types of murmurs.

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16

Baakek, Yettou Nour El Houda, Imane Debbal, Hidayat Boudis, and Sidi Mohammed El Amine Debbal. "Study of the impact of clicks and murmurs on cardiac sounds S1 and S2 through bispectral analysis." Polish Journal of Medical Physics and Engineering 27, no. 1 (March 1, 2021): 63–72. http://dx.doi.org/10.2478/pjmpe-2021-0009.

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Abstract This paper presents a study of the impact of clicks, and murmurs on cardiac sound S1, and S2, and the measure of severity degree through synchronization degree between frequencies, using bispectral analysis. The algorithm is applied on three groups of Phonocardiogram (PCG) signal: group A represents PCG signals having a morphology similar to that of the normal PCG signal without click or murmur, group B represents PCG signals with a click (reduced murmur), and group C represent PCG signals with murmurs. The proposed algorithm permits us to evaluate and quantify the relationship between the two sounds S1 and S2 on one hand and between the two sounds, click and murmur on the other hand. The obtained results show that the clicks and murmurs can affect both the heart sounds, and vice versa. This study shows that the heart works in perfect harmony and that the frequencies of sounds S1, S2, clicks, and murmurs are not accidentally generated; but they are generated by the same generator system. It might also suggest that one of the obtained frequencies causes the others. The proposed algorithm permits us also to determine the synchronization degree. It shows high values in group C; indicating high severity degrees, low values for group B, and zero in group A. The algorithm is compared to Short-Time Fourier Transform (STFT) and continuous wavelet transform (CWT) analysis. Although the STFT can provide correctly the time, it can’t distinguish between the internal components of sounds S1 and S2, which are successfully determined by CWT, which, in turn, cannot find the relationship between them. The algorithm was also evaluated and compared to the energetic ratio. the obtained results show very satisfactory results and very good discrimination between the three groups. We can conclude that the three algorithms (STFT, CWT, and bispectral analysis) are complementary to facilitate a good approach and to better understand the cardiac sounds

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17

Chen, Wei, Qiang Sun, Jue Wang, Huiqun Wu, Hui Zhou, Hongjun Li, Hongming Shen, and Chen Xu. "Phonocardiogram Classification Using Deep Convolutional Neural Networks with Majority Vote Strategy." Journal of Medical Imaging and Health Informatics 9, no. 8 (October 1, 2019): 1692–704. http://dx.doi.org/10.1166/jmihi.2019.2704.

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Most current automated phonocardiogram (PCG) classification methods are relied on PCG segmentation. It is universal to make use of the segmented PCG signals and then extract efficiency features for computer-aided auscultation or heart sound classification. However, the accurate segmentation of the fundamental heart sounds depends greatly on the quality of the heart sound signals. In addition these methods that heavily relied on segmentation algorithm considerably increase the computational burden. To solve above two issues, we have developed a novel approach to classify normal and abnormal cardiac diseases with un-segmented PCG signals. A deep Convolutional Neural Networks (DCNNs) method is proposed for recognizing normal and abnormal cardiac diseases. In the proposed method, one-dimensional heart sound signals are first converted into twodimensional feature maps which have three channels and each of them represents Mel-frequency spectral coefficients (MFSC) features including static, delta and delta–delta. These artificial images are then fed to the proposed DCNNs to train and evaluate normal and abnormal heart sound signals. We combined the method of majority vote strategy to finally obtain the category of PCG signals. Sensitivity (Se), Specificity (Sp) and Mean accuracy (MAcc) are used as the evaluation metrics. Results: Experiments demonstrated that our approach achieved a significant improvement, with the high Se, Sp, and MAcc of 92.73%, 96.90% and 94.81% respectively. The proposed method improves the MAcc by 5.63% compared with the best result in the CinC Challenge 2016. In addition, it has better robustness performance when applying for the long heart sounds. The proposed DCNNs-based method can achieve the best accuracy performance on recognizing normal and abnormal heart sounds without the preprocessing of segmental algorithm. It significantly improves the classification performance compared with the current state-of-art algorithm.

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Golpaygani, Ali Tavakoli, Nahid Abolpour, Kamran Hassani, Kourosh Bajelani, and D. John Doyle. "Detection and identification of S1 and S2 heart sounds using wavelet decomposition method." International Journal of Biomathematics 08, no. 06 (October 15, 2015): 1550078. http://dx.doi.org/10.1142/s1793524515500783.

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Phonocardiogram (PCG), the digital recording of heart sounds is becoming increasingly popular as a primary detection system for diagnosing heart disorders and it is relatively inexpensive. Electrocardiogram (ECG) is used during the PCG in order to identify the systolic and diastolic parts manually. In this study a heart sound segmentation algorithm has been developed which separates the heart sound signal into these parts automatically. This study was carried out on 100 patients with normal and abnormal heart sounds. The algorithm uses discrete wavelet decomposition and reconstruction to produce PCG intensity envelopes and separates that into four parts: the first heart sound, the systolic period, the second heart sound and the diastolic period. The performance of the algorithm has been evaluated using 14,000 cardiac periods from 100 digital PCG recordings, including normal and abnormal heart sounds. In tests, the algorithm was over 93% correct in detecting the first and second heart sounds. The presented automatic segmentation algorithm using wavelet decomposition and reconstruction to select suitable frequency band for envelope calculations has been found to be effective to segment PCG signals into four parts without using an ECG.

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Abed, Wasam Samer, and Rashid Ali Fayadh. "Wavelet Packet VS Backpropagation for localization and Classification PCG Signals." IOP Conference Series: Materials Science and Engineering 881 (August 11, 2020): 012104. http://dx.doi.org/10.1088/1757-899x/881/1/012104.

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20

Chakir, Fatima, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Recognition of cardiac abnormalities from synchronized ECG and PCG signals." Physical and Engineering Sciences in Medicine 43, no. 2 (April 28, 2020): 673–77. http://dx.doi.org/10.1007/s13246-020-00875-2.

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El-Dahshan, El-Sayed A., Mahmoud M. Bassiouni, Septavera Sharvia, and Abdel-Badeeh M. Salem. "PCG signals for biometric authentication systems: An in-depth review." Computer Science Review 41 (August 2021): 100420. http://dx.doi.org/10.1016/j.cosrev.2021.100420.

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Liu, Shing-Hong, Jia-Jung Wang, Chun-Hung Su, and Da-Chuan Cheng. "Improvement of Left Ventricular Ejection Time Measurement in the Impedance Cardiography Combined with the Reflection Photoplethysmography." Sensors 18, no. 9 (September 11, 2018): 3036. http://dx.doi.org/10.3390/s18093036.

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Cardiac stroke volume (SV) is an essential hemodynamic indicator that can be used to assess whether the pump function of the heart is normal. Non-invasive SV measurement is currently performed using the impedance cardiography (ICG). In this technology, left ventricular ejection time (LVET) is an important parameter which can be determined from the ICG signals. However, the ICG signals are inherently susceptible to artificial noise interference, which leads to an inaccurate LVET measurement and then yields an error in the calculation of SV. Therefore, the goal of the study was to measure LVETs using both the transmission and reflection photoplethysmography (PPG), and to assess whether the measured LVET was more accurate by the PPG signal than the ICG signal. The LVET measured by the phonocardiography (PCG) was used as the standard for comparing with those by the ICG and PPG. The study recruited ten subjects whose LVETs were simultaneously measured by the ICG using four electrodes, the reflection PPG using neck sensors (PPGneck) and the transmission PPG using finger sensors (PPGfinger). In each subject, ten LVETs were obtained from ten heartbeats selected properly from one-minute recording. The differences of the measured LVETs between the PCG and one of the ICG, PPGneck and PPGfinger were −68.2 ± 148.6 ms, 4.8 ± 86.5 ms and −7.0 ± 107.5 ms, respectively. As compared with the PCG, both the ICG and PPGfinger underestimated but the PPGneck overestimated the LVETs. Furthermore, the measured LVET by the PPGneck was the closest to that by the PCG. Therefore, the PPGneck may be employed to improve the LVET measurement in applying the ICG for continuous monitoring of SV in clinical settings.

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NAM, KYOUNG WON, JI MIN AHN, YOUNG JUN HWANG, GYE ROK JEON, DONG PYO JANG, and IN YOUNG KIM. "A PHONOCARDIOGRAM-BASED NOISE-ROBUST REAL-TIME HEART RATE MONITORING ALGORITHM FOR OUTPATIENTS DURING NORMAL ACTIVITIES." Journal of Mechanics in Medicine and Biology 18, no. 05 (August 2018): 1850044. http://dx.doi.org/10.1142/s0219519418500446.

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For outpatients who need continuous monitoring of heart rate (HR) variation, it is important that HR can be monitored during normal activities such as speaking and walking. In this study, a noise-robust real-time HR monitoring algorithm based on phonocardiogram (PCG) signals is proposed. PCG signals were recorded using an electronic stethoscope; electrocardiogram (ECG) signals were recorded simultaneously with HR references. The proposed algorithm consisted of pre-processing, peak/nonpeak classification, voice noise processing, walking noise processing, and HR calculation. The performance of the algorithm was evaluated using PCG/ECG signals from 11 healthy participants. For comparison, the absolute errors between manually extracted ECG-based HR values and automatically calculated PCG-based HR values were calculated for the proposed algorithm and the comparison algorithm in two different test protocols. Experimental results showed that the average absolute errors of the proposed algorithm were 72.03%, 22.92%, and 36.39% of the values of the comparison algorithm for resting-state, speaking-state, and walking-state data, respectively, in protocol-1. In protocol-2, the average absolute error was 36.99% of that of the comparison algorithm. A total of 1102 cases in protocol-1 and 783 in protocol-2 had an absolute error [Formula: see text] beats per minute (BPM) using the comparison algorithm and an absolute error [Formula: see text] BPM using the proposed algorithm. On the basis of these results, we anticipate that the proposed algorithm can potentially improve the performance of continuous real-time HR monitoring during activities of normal life, thereby improving the safety of outpatients with cardiovascular diseases.

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Chowdhury, Tanzil Hoque, Khem Narayan Poudel, and Yating Hu. "Time-Frequency Analysis, Denoising, Compression, Segmentation, and Classification of PCG Signals." IEEE Access 8 (2020): 160882–90. http://dx.doi.org/10.1109/access.2020.3020806.

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Yaseen, Gui-Young Son, and Soonil Kwon. "Classification of Heart Sound Signal Using Multiple Features." Applied Sciences 8, no. 12 (November 22, 2018): 2344. http://dx.doi.org/10.3390/app8122344.

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Cardiac disorders are critical and must be diagnosed in the early stage using routine auscultation examination with high precision. Cardiac auscultation is a technique to analyze and listen to heart sound using electronic stethoscope, an electronic stethoscope is a device which provides the digital recording of the heart sound called phonocardiogram (PCG). This PCG signal carries useful information about the functionality and status of the heart and hence several signal processing and machine learning technique can be applied to study and diagnose heart disorders. Based on PCG signal, the heart sound signal can be classified to two main categories i.e., normal and abnormal categories. We have created database of 5 categories of heart sound signal (PCG signals) from various sources which contains one normal and 4 are abnormal categories. This study proposes an improved, automatic classification algorithm for cardiac disorder by heart sound signal. We extract features from phonocardiogram signal and then process those features using machine learning techniques for classification. In features extraction, we have used Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) features from the heart sound signal, and for learning and classification we have used support vector machine (SVM), deep neural network (DNN) and centroid displacement based k nearest neighbor. To improve the results and classification accuracy, we have combined MFCCs and DWT features for training and classification using SVM and DWT. From our experiments it has been clear that results can be greatly improved when Mel Frequency Cepstral Coefficient and Discrete Wavelets Transform features are fused together and used for classification via support vector machine, deep neural network and k-neareast neighbor(KNN). The methodology discussed in this paper can be used to diagnose heart disorders in patients up to 97% accuracy. The code and dataset can be accessed at “https://github.com/yaseen21khan/Classification-of-Heart-Sound-Signal-Using-Multiple-Features-/blob/master/README.md”.

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Cheng, Wai Kit, Ismail Mohd Khairuddin, Anwar P.P. Abdul Majeed, and Mohd Azraai Mohd Razman. "The Classification of Heart Murmurs: The Identification of Significant Time Domain Features." MEKATRONIKA 2, no. 2 (December 13, 2020): 36–43. http://dx.doi.org/10.15282/mekatronika.v2i2.6748.

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Phonocardiogram (PCG) is a type of acoustic signal collected from the heartbeat sound. PCG signals collected in the form of wave files and collected type of heart sound with a specific period. The difficulty of the binomial class in supervised machine learning is the steps-by-steps workflow. The consideration and decision make for every part are importantly stated so that misclassification will not occur. For the heart murmurs classification, data extraction has highly cared for it as we might have fault data consisting of outside signals. Before classifying murmurs in binomial, it will involve multiple features for selection that can have a better classification of the heart murmurs. Nevertheless, since classification performance is vital to conclude the results, models are needed to compare the research's output. The main objective of this study is to classify the signal of the murmur via time-domain based EEG signals. In this study, significant time-domain features were identified to determine the best features by using different feature selection methods. It continues with the classification with different models to compete for the highest accuracy as the performance for murmur classification. A set of Michigan Heart Sound and Murmur database (MHSDB) was provided by the University of Michigan Health System with chosen signals listening with the bell of the stethoscope at the apex area by left decubitus posture of the subjects. The PCG signals are pre-processed by segmentation of three seconds, downsampling eight thousand Hz and normalized to -1, +1. Features are extracted into ten features: Root Mean Square, Variance, Standard Deviation, Maximum, Minimum, Median, Skewness, Shape Factor, Kurtosis, and Mean. Two cross-validation methods applied, such as data splitting and k-fold cross-validation, were used to measure this study's data. Chi-Square and ANOVA technique practice to identify the significant features to improve the classification performance. The classification learners are enrolled and compared by Ada Boost, Random Forest (RF) and Support Vector Machine (SVM). The datasets will separate into a ratio of 70:30 and 80:20 for training and testing data, respectively. The chi-Square selection method was the best features selection method and 80:20 data splitting with better performance than the 70:30 ratio. The best classification accuracy for the models significantly come by SVM with all the categories with 100% except 70:30 test on test data with 97.2% only.

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Lee, Soomin, Qun Wei, Heejoon Park, Yuri Na, Donghwa Jeong, and Hongjoon Lim. "Development of a Finger-Ring-Shaped Hybrid Smart Stethoscope for Automatic S1 and S2 Heart Sound Identification." Sensors 21, no. 18 (September 20, 2021): 6294. http://dx.doi.org/10.3390/s21186294.

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Cardiac auscultation is one of the most popular diagnosis approaches to determine cardiovascular status based on listening to heart sounds with a stethoscope. However, heart sounds can be masked by visceral sounds such as organ movement and breathing, and a doctor’s level of experience can more seriously affect the accuracy of auscultation results. To improve the accuracy of auscultation, and to allow nonmedical staff to conduct cardiac auscultation anywhere and anytime, a hybrid-type personal smart stethoscope with an automatic heart sound analysis function is presented in this paper. The device was designed with a folding finger-ring shape that can be worn on the finger and placed on the chest to measure photoplethysmogram (PPG) signals and acquire the heart sound simultaneously. The measured heart sounds are detected as phonocardiogram (PCG) signals, and the boundaries of the heart sound variation and the peaks of the PPG signal are detected in preprocessing by an advanced Shannon entropy envelope. According to the relationship between PCG and PPG signals, an automatic heart sound analysis algorithm based on calculating the time interval between the first and second heart sounds (S1, S2) and the peak of the PPG was developed and implemented via the manufactured prototype device. The prototype device underwent accuracy and usability testing with 20 young adults, and the experimental results showed that the proposed smart stethoscope could satisfactorily collect the heart sounds and PPG signals. In addition, within the developed algorithm, the device was as accurate in start-points of heart sound detection as professional physiological signal-acquisition systems. Furthermore, the experimental results demonstrated that the device was able to identify S1 and S2 heart sounds automatically with high accuracy.

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Lubaib, P., and K. V. Ahammed Muneer. "The Heart Defect Analysis Based on PCG Signals Using Pattern Recognition Techniques." Procedia Technology 24 (2016): 1024–31. http://dx.doi.org/10.1016/j.protcy.2016.05.225.

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Wang, Tao, Shifu Xiao, Xia Li, Bei Ding, Huawei Ling, Kemin Chen, and Yiru Fang. "Using proton magnetic resonance spectroscopy to identify mild cognitive impairment." International Psychogeriatrics 24, no. 1 (June 16, 2011): 19–27. http://dx.doi.org/10.1017/s1041610211000962.

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ABSTRACTBackground: Single-volume proton magnetic resonance spectroscopy (1H MRS) has considerable diagnostic potential for Alzheimer's disease (AD). This study investigated 1H MRS in specific regions of the brain, the posterior cingulate gyri (PCG) and the hippocampus, in patients with AD, amnestic mild cognitive impairment (aMCI), and in normal control subjects.Methods:1H MRS analysis was carried out on 47 patients with AD, 32 patients with aMCI and 56 normal control subjects (NC group). Volumes of the PCG and hippocampus were assessed, and the metabolic signals of N-acetylaspartate (NAA), choline compounds (Cho), myo-inositol (mI), and creatine (Cr) were quantified.Results: In the PCG, differences between the test groups were found in NAA/Cr, Cho/Cr, mI/Cr and NAA/mI ratios. Group differences were also found in mI/Cr and NAA/mI ratios in the left hippocampus, and in mI/Cr and NAA/mI ratios in the right hippocampus. NAA/Cr ratios increased in the PCG between AD and aMCI patients, and between aMCI and NC patients. Conversely, mI/Cr ratios in the PCG and left hippocampus decreased across AD, aMCI, and NC subjects. In discriminate and ROC (Receiver Operating Characteristic) analyses, a NAA/Cr ratio of ≤ 1.50 in the PCG indicated optimal potential for discriminating between aMCI patients and normal control subjects. Discriminating potential was also found to be high for a NAA/mI ratio in the PCG of ≤ 2.72. Despite significant differences between NC and aMCI patients in the mI/Cr ratio in the PCG and in the NAA/mI ratio in the left hippocampus, their sensitivity and specificity were all lower than 75%.Conclusion: Proton MRS of the PCG using the NAA/Cr ratio as a metabolic marker indicates considerable potential for distinguishing between aMCI and NC subjects.

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Abo-Zahhad, M., Sabah M. Ahmed, and S. N. Abbas. "Biometric authentication based on PCG and ECG signals: present status and future directions." Signal, Image and Video Processing 8, no. 4 (December 5, 2013): 739–51. http://dx.doi.org/10.1007/s11760-013-0593-4.

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Baghel, Neeraj, Malay Kishore Dutta, and Radim Burget. "Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network." Computer Methods and Programs in Biomedicine 197 (December 2020): 105750. http://dx.doi.org/10.1016/j.cmpb.2020.105750.

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Debbal, Sidi. "Cardiac Severity Analysis." Journal of Thoracic Disease and Cardiothoracic Surgery 2, no. 2 (August 11, 2021): 01–09. http://dx.doi.org/10.31579/2693-2156/023.

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Phonocardiogram (PCG) signal is a particular approach to explore cardiac activity, to develop technics that may serve medical staff to diagnose several cardiac diseases. We took advantage of PCG signal that shows heart murmurs on its tracing dissimilar to other cardiac signals, to design an algorithm to study and classify heart murmurs. In this paper, the importance is given to the severity of murmurs to highlight its impact, since depending on its stage the patient could be in life-threatening point; therefore, the purpose of this paper is focused on three essential steps: according to the algorithm, extracting murmurs and classifying them to deferent stages then investigate the impact of severity on cardiac frequency through some parameters. The severity stage calculation was based on energy ratio (ER) which is recommended by recent studies as an effective factor, however, we succeed to validate that murmur energy (ME) is also a qualified feature to determine severity. But despite that murmur duration, it's an inefficient way to judge the cardiac severity, which is a very important indicator of the general health of the human body. This study is done on considering many patients and it reveals very interesting results.

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Jadhav, Gabbar. "Automatic Detection of Mitral Regurgitation from Heart Sounds using SODP of Imperical Mode Decomposition." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1874–77. http://dx.doi.org/10.22214/ijraset.2021.35435.

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In this paper we discussed the heart valve disease. This heart valve disease occur throughout the world due to the more ethical estimation and grow curator of heart valve diseases use the diagnosis for this type of valve disease . Actually Phonocardiogram (PCG) signals are used because it having less price and acquire the signals. In this we learn five different kind of heart areas, Also typical are aortic stenosis, mitral valve prolapse, mitral stenosis and mitral regurgitation.

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Debbal, Sid. "Heart cardiac’s sounds signals segmentation by using the discrete wavelet transform (DWT)." Biomedical Research and Clinical Reviews 4, no. 3 (July 23, 2021): 01–15. http://dx.doi.org/10.31579/2692-9406/052.

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The presence of abnormal sounds in one cardiac cycle, provide valuable information on various diseases.Early detection of various diseases is necessary; it is done by a simple technique known as: phonocardiography. The phonocardiography, based on registration of vibrations or oscillations of different frequencies, audible or not, that correspond to normal and abnormal heart sounds. It provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intracardiac phonocardiography, combined with signal processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm based on the denoising by wavelet transform (DWT) and the Shannon energy of the PCG signal, for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This algorithm makes it possible to isolate individual sounds (S1 or S2) and murmurs to give an assessment of their average duration.

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Ghosh, Samit Kumar, R. N. Ponnalagu, R. K. Tripathy, and U. Rajendra Acharya. "Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings." BioMed Research International 2020 (December 21, 2020): 1–16. http://dx.doi.org/10.1155/2020/8843963.

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The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.

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Rouis, Mohamed, Salim Sbaa, and Nasser Edinne Benhassine. "The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform." Biomedical Engineering / Biomedizinische Technik 65, no. 3 (May 26, 2020): 353–66. http://dx.doi.org/10.1515/bmt-2019-0197.

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AbstractThe greatest problem with recording heart sounds is parasitic noise effects. A reasonable solution to reduce noise can be carried out by minimization of extraneous noises in the vicinity of the patient during recording, in addition to the methods of signal processing that must be effective in noisy environments. Wavelet transform has become an essential tool for many applications, but its effectiveness is influenced by main parameters. Determination of mother wavelet function and decomposition level (DL) are important key factors to demonstrate the advantages of wavelet denoising. So, selection of optimal mother wavelet with DL is a main challenge to current algorithms. The principal aim of this study was the choice of an appropriate criterion for finding the optimal DL and the optimal mother wavelet function according to four criteria which are: signal-to-noise ratio (SNR), mean square error (MSE), percentage root-mean-square difference (PRD) and the structure similarity index measure (SSIM) for testing the robustness of the proposed algorithm. The proposed method is applied to the PCG signal contaminated with four colored noise types, in addition to the Gaussian noise. The obtained results show the effectiveness of the proposed method in reducing noise from the noisy PCG signals, especially at a low SNR.

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Wang, Jiaming, Tao You, Kang Yi, Yaqin Gong, Qilian Xie, Fei Qu, Bangzhou Wang, and Zhaoming He. "Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease." Journal of Healthcare Engineering 2020 (May 11, 2020): 1–9. http://dx.doi.org/10.1155/2020/9640821.

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Heart auscultation is a convenient tool for early diagnosis of heart diseases and is being developed to be an intelligent tool used in online medicine. Currently, there are few studies on intelligent diagnosis of pediatric murmurs due to congenital heart disease (CHD). The purpose of the study was to develop a method of intelligent diagnosis of pediatric CHD murmurs. Phonocardiogram (PCG) signals of 86 children were recorded with 24 children having normal heart sounds and 62 children having CHD murmurs. A segmentation method based on the discrete wavelet transform combined with Hadamard product was implemented to locate the first and the second heart sounds from the PCG signal. Ten features specific to CHD murmurs were extracted as the input of classifier after segmentation. Eighty-six artificial neural network classifiers were composed into a classification system to identify CHD murmurs. The accuracy, sensitivity, and specificity of diagnosis for heart murmurs were 93%, 93.5%, and 91.7%, respectively. In conclusion, a method of intelligent diagnosis of pediatric CHD murmurs is developed successfully and can be used for online screening of CHD in children.

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Kalaivani, V., R. Lakshmi Devi, and V. Anusuyadevi. "Phonocardiographic Signal and Electrocardiographic Signal Analysis for the Detection of Cardiovascular Diseases." Biosciences, Biotechnology Research Asia 15, no. 1 (March 25, 2018): 79–89. http://dx.doi.org/10.13005/bbra/2610.

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The main objective is to develop a novel method for the heart sound analysis for the detection of cardiovascular diseases. It can be considered as one of the important phases in the automated analysis of PCG signals. Heart sounds carry information about mechanical activity of the cardiovascular system. This information includes specific physiological state of the subject and the short term variability related to the respiratory cycle. The interpretation of sounds and extraction of changes in the physiological state while maintaining the short term variability are still an open problem and is subject of this paper. The system deals with the process of de-noising of the heart sound signal(PCG) and the signal is decomposed into several sub-bands and the de-noised heart sound signal is segmented into the basic heart sounds S1 and S2, along with the systolic and diastolic interval.. Also, the ECG signal is de-noised. Meanwhile, the R-peaks are identified from the ECG signal and RR interval is obtained. Extraction of features are done from both the heart sound signal and the ECG signal. From the features, the R-peaks are identified from the ECG signal and RR interval is obtained. The attribute selection is to find the best attribute values that can be used for the classification process. Finally, using classification technique, cardiac diseases are detected. This work is implemented by using MATLAB software.

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CHIEN, JIA-REN CHANG, and CHENG-CHI TAI. "A NEW WIRELESS-TYPE PHYSIOLOGICAL SIGNAL MEASURING SYSTEM USING A PDA AND THE BLUETOOTH TECHNOLOGY." Biomedical Engineering: Applications, Basis and Communications 17, no. 05 (October 25, 2005): 229–35. http://dx.doi.org/10.4015/s1016237205000342.

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A light and portable-type wireless physiological signal retrieving system has always been a medical personnel's dream. To fulfill this dream, this paper investigates a feasible method to create a wireless-type physiological signal measuring system using a PDA and the bluetooth technology. The proposed system will reduce the service costs and raise the service efficiency for current medical care systems. The waveforms and data of physiological signals, such as electrocardiograms (ECGs), phonocardiograms (PCGs), electroencephalograms (EEGs), body temperatures, and so forth, are always the vital references for medical doctors to diagnose the patients' body condition. The traditional physiological signal measuring instruments or devices possess some shortcomings, such as high prices, bulky dimensions, ill-portability and excessive connection cables. In contrast, the proposed wireless-type physiological signal measuring system, being able to get rid of said shortcomings, holds apparent advantages in service costs and service efficiency and, hence, shall be the trend for the future. This study has completed some tests in ECG, PCG, and body temperature measurements. The proposed prototype system has successfully using the bluetooth technology to invisibly transmit and receive physical signals through the air.

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Radia, Fandi, and Hadj Slimane Zine Eddine. "A new heart sounds segmentation approach based on the correlation between ECG and PCG signals." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 174. http://dx.doi.org/10.1504/ijbet.2019.097304.

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Radia, Fandi, and Hadj Slimane Zine Eddine. "A new heart sounds segmentation approach based on the correlation between ECG and PCG signals." International Journal of Biomedical Engineering and Technology 29, no. 2 (2019): 174. http://dx.doi.org/10.1504/ijbet.2019.10018404.

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Marzorati, Davide, Dario Bovio, Caterina Salito, Luca Mainardi, and Pietro Cerveri. "Chest Wearable Apparatus for Cuffless Continuous Blood Pressure Measurements Based on PPG and PCG Signals." IEEE Access 8 (2020): 55424–37. http://dx.doi.org/10.1109/access.2020.2981300.

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Facchetti Vinhaes Assumpcao, Anna Luiza, Guoping Fu, Zhanping Lu, Ashley Kuehnl, Renren Wen, and Xuan Pan. "A Lineage-Specific Requirement for YY1 Polycomb Group Protein Function in Early T Cell Development." Blood 136, Supplement 1 (November 5, 2020): 35. http://dx.doi.org/10.1182/blood-2020-139872.

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Анотація:

T cell development originates from hematopoietic stem and progenitor cells in the bone marrow, which migrate to the thymus and obtain T cell identification. Transcription factors play critical roles in regulating early T cell development. While Notch signals are critically required at the early stage of T cell development, the completion of T cell lineage commitment is far from the initial response to Notch signaling. Other transcription factors such as PU.1, Ikaros, and RUNX1 are required to enable progenitor cells to committee T cell lineage before Notch signaling. YY1 is a ubiquitous transcription factor and mammalian Polycomb Group Protein (PcG) with important functions to regulate lymphocytes development, stem cell self-renewal, cell proliferation, and survival. Previous study showed that YY1 can interact with the Notch1 receptor intracellular domain and regulate Notch1 transactivation activities in vitro. Thus, YY1 may also belong to the core T cell lineage regulatory factors and is required for progenitor cell commitment to T cell development. To test how loss-of-function of YY1 impacts early T cell development, we utilized a conditional Yy1 knockout allele Yy1f/f with loxP sites flanking the Yy1 promoter region and exon 1. Yy1f/fmice were crossed to the inducible Mx1-Cre. In Yy1f/fMx1-Cre mice, YY1 deletion was achieved after treatment with the pI-pC. Yy1-/- mice had significantly reduced numbers of lymphoid-primed multipotent progenitor, (LMPP), common lymphoid progenitor (CLP), and double-negative (DN) T cells compared to Yy1+/+ mice. YY1 deficiency resulted in an early T cell developmental blockage at the DN1 stage. In addition, Notch1 mRNA and protein expressions were significantly reduced in Yy1-/- thymocytes compared to Yy1+/+ thymocytes. In Yy1-/- thymocytes, Notch target gene Hes1 was also downregulated. Thus, YY1 is required for early T cell development and Notch1 signaling. YY1 mediates stable PcG-dependent transcriptional repression via recruitment of PcG proteins that catalyze histone modifications. Our previous results demonstrated that YY1 PcG function is required for Igκ chain rearrangement in early B cell development, however, it is not required for YY1 functions in promoting HSC self-renewal and maintaining HSC quiescence. Many questions remain unanswered regarding how cell- and tissue-specificity is achieved by PcG proteins. Herein, we utilized a YY1 REPO domain mutant (YY1ΔREPO). The small 25 amino acid REPO domain is necessary and sufficient for recruiting other PcG proteins to YY1-bound chromatin sites in Drosophila. While YY1ΔREPO is competent for DNA binding, transcriptional activation, transient transcriptional repression, and interaction with transcriptional coregulators such as HDACs, it is defective in all YY1 PcG functions and unable to recruit other PcG proteins to DNA. This mutant is therefore a powerful tool for dissecting mechanisms governing YY1 PcG-dependent versus -independent functions. Bone marrow cells from Yy1f/f Mx1-Cre mice were transduced retrovirally with MigR1-FlagYY1, MigR1-FlagYY1ΔREPO or MigR1 vector and transplanted into lethally irradiated CD45.1+ mice. In addition, Mx1-Cre bone marrow cells infected with MigR1 vector were used as the wild-type control and transplanted into CD45.1+ mice. While YY1 is required for DN1 to DN2 transition, YY1 PcG function/REPO domain is not required for DN1 transition. Instead, in mice lack of YY1 PcG function/REPO domain, early T cells had increased cell apoptosis and failed to survive. Interestingly, although YY1 PcG function/REPO domain is critical for early T cell survival, it is not required for YY1 regulation of Notch1 expression. We concluded that YY1 is a critical regulator for early T cell development and Notch signaling. There is a lineage-specific requirement for the YY1 PcG function/REPO domain for early T cell development. While YY1 PcG function is required for early T cell survival, it is not required for YY1 regulation of Notch1 expression. YY1 PcG and non-PcG functions promotes T cell development by unique mechanisms of promoting cell survival and Notch1 expression respectively. Disclosures No relevant conflicts of interest to declare.

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Vieira, Hélder, Nelson Costa, Joaquim Fernando Almeida Alves, and Luís Pinto Coelho. "Simulation of Abnormal Physiological Signals in a Phantom for Bioengineering Education." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 14 (November 30, 2020): 107. http://dx.doi.org/10.3991/ijoe.v16i14.16941.

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In clinical practice and in particular in the diagnostic process, the assessment of cardiac and respiratory functions is supported by electrocardiogram and auscultation. These exams are non-invasive, quick and inexpensive to perform and easy to interpret. For these reasons, this type of assessment is a constant in the daily life of a clinician and the information obtained is central to the decision making process. Therefore, it is essential that during their training, students of health-related subjects acquire skills in the acquisition and evaluation of the referred physiological signals. Simulation, considering the technological possibilities of today, is an excellent preparation tool since it exposes trainees to near real contexts but without the associated risks. Hence, the simulation of physiological signals plays an important role in the education of healthcare professionals, bioengineering professionals and also in the development and calibration of medical devices. This paper describes a project to develop synchronized electrocardiogram (ECG), phonocardiogram (PCG) and breathing sounds simulators that aims to improve an existing phantom simulator. The developed system allows, in an integrated way, to generate normal and pathological signals, being contemplated several distinct pathologies. For engineering education, it is also possible to simulate the introduction of signal disturbances or hardware malfunctions.

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Rouhani, M., and R. Abdoli. "A comparison of different feature extraction methods for diagnosis of valvular heart diseases using PCG signals." Journal of Medical Engineering & Technology 36, no. 1 (December 10, 2011): 42–49. http://dx.doi.org/10.3109/03091902.2011.634946.

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Ghosh, Samit Kumar, R. N. Ponnalagu, R. K. Tripathy, and U. Rajendra Acharya. "Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG signals." Computers in Biology and Medicine 118 (March 2020): 103632. http://dx.doi.org/10.1016/j.compbiomed.2020.103632.

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Tuncer, Turker, Sengul Dogan, Ru-San Tan, and U. Rajendra Acharya. "Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals." Information Sciences 565 (July 2021): 91–104. http://dx.doi.org/10.1016/j.ins.2021.01.088.

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Bourouhou, Abdelhamid, Abdelilah Jilbab, Chafik Nacir, and Ahmed Hammouch. "Heart Sound Signals Segmentation and Multiclass Classification." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 15 (December 15, 2020): 64. http://dx.doi.org/10.3991/ijoe.v16i15.16817.

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Анотація:

<em>The heart is the organ that pumps blood with oxygen and nutrients into all body organs by a rhythmic cycle overlapping between contraction and dilatation. This is done by producing an audible sound which we can hear using a stethoscope. Many are the causes affecting the normal function of this most vital organ. In this respect, the heart sound classification has become one of the diagnostic tools that allow the discrimination between patients and healthy people; this diagnosis is less painful, less costly and less time consuming. In this paper, we present a classification algorithm based on the extraction of 20 features from segmented phonocardiogram “PCG” signals. We applied four types of machine learning classifiers that are k- Near Neighbor “KNN”, Support Vector Machine “SVM”, Tree, and Naïve Bayes “NB” so as to train old features and predict the new entry. To make our results measurable, we have chosen the PASCAL Classifying Heart Sounds challenge, which is a rich database and is conducive to classifying the PCGs into four classes for dataset A and three classes for dataset B. The main finding is about 3.06 total precision of the dataset A and 2.37 of the dataset B.</em>

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Mubarak, Qurat-ul-Ain, Muhammad Usman Akram, Arslan Shaukat, Farhan Hussain, Sajid Gul Khawaja, and Wasi Haider Butt. "Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds." Computer Methods and Programs in Biomedicine 164 (October 2018): 143–57. http://dx.doi.org/10.1016/j.cmpb.2018.07.006.

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Karhade, Jay, Shaswati Dash, Samit Kumar Ghosh, Dinesh Kumar Dash, and Rajesh Kumar Tripathy. "Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–11. http://dx.doi.org/10.1109/tim.2022.3163156.

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