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Decomposition of an aortic stenosis PCG signal. (a) An aortic stenosis PCG signal. (b)-(e) The components are plotted in their joint time-amplitude, time-frequency, time-phase and time-time-support planes.
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Background:
A phonocardiogram (PCG) signal can be recorded for long-term heart monitoring. A huge amount of data is produced if the time of a recording is as long as days or weeks. It is necessary to compress the PCG signal to reduce storage space in a record and play system. In another situation, the PCG signal is transmitted to a remote health c...
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Citations
... By attaching skin electrodes outside the eye near the lateral and medial canthus, the potential can be measured by having the patient move the eyes horizontally a set distance [38] Eyes EMG Needle electrodes or surface electrodes coupled to a chart recorder or a computer [39] EMG is a technique for recording biomedical electrical signals obtained from the neuromuscular activities [39] Muscles EEG Neurosky Mind-Wave, InteraXon Muse, Emotiv Epoc, Emotiv Insight, and OpenBCI [40] EEG is an electrophysiological technique for the recording of electrical activity arising from the human brain. EEG devices record voltage differences between different points by comparing different electrical signals comming from a pair of electrodes [41] Brain ECoG Electrodes coupled to a subcutaneous amplifier and transmitter unit (with leads connected to the electrode array) and a a processing computer for realtime analysis of the signal [42] ECoG is a technique for recording brain signals placing electrodes subdurally on the arachnoidal surface of the brain [42] Brain Bioacoustic PCG Phonocardiograph, smart stethoscopes PCG consists on recording all sounds made by heart during a cardiac cycle [43] Heart Speech Voice Recorders, microphones, sound recorders, etc [44]. ...
... Second, the methods can be divided into two categories depending on whether they utilize the inter-period redundancy in addition to the intra-period one. Among the methods in Table 1, only the method in [24] has tried to eliminate inter-period redundancy by representation of the PCG signal based on some basis signals acquired by the Self-Organizing Map (SOM) clustering. The representation error (i.e., the residual) signal is encoded using the vector quantization. ...
... The compression efficiency of the proposed PM algorithm is higher for the murmur class since the corresponding signals showed more order and uniformity in behavior compared to the normal class signals; this can be guessed from Fig. 3 too. The compression method in [24] although implemented on two databases with a relatively low sampling rate of 4000 Hz, but it is included in Table 4 because it is based on the adaptation of an offline PM technique. Content courtesy of Springer Nature, terms of use apply. ...
Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated has increased and therefore, the storage and transmission of these signals has become a challenge. This, in turn, demonstrates the importance and necessity of using efficient methods for compression of these signals. These methods should have a high compression ratio and, at the same time, preserve important clinical information as much as possible. In this paper, a lossy compression method is proposed for phonocardiography (PCG) signals recorded at a relatively high sampling rate so that it can control the quality of the compressed signal. This method is based on two techniques: "two-stage downsampling" and "pattern matching (PM)". The proposed two-stage downsampling technique increases the amount of compression ratio and at the same time, reduces the computational complexity. The PM technique is able to reduce the inter-period redundancy and therefore, further increase the compression ratio. The simulation results of the proposed method on two databases of the University of Michigan and the University of Washington showed that the two-stage downsampling and PM techniques have a large contribution in increasing the compression ratio. The performance of the proposed method was evaluated according to the PRD and CR criteria and compared with that of some existing methods. In this evaluation, for the PRD range of ≤5%, the CR value was between 2500 and 3900 for the University of Michigan database and between 2500 and 4125 for the University of Washington database. Also, the results of applying the proposed method on the PASCAL database showed that the efficiency of the proposed method depends to a large extent, on the quality and regularity of the PCG signal.
... The basic principle behind the fHS processing is that the heart's mechanical activity is accompanied by the generation of various characteristic sounds. These sounds are associated with changes in the speed of blood flow, as well as with the opening and closing of heart valves [13]. Dia et al. estimated adult heart rate from phonocardiograph (PCG) signals [14]. ...
Objective: Beat-to-beat tele-fetal monitoring and comparison with clinical data are studied with a wavelet transformation approach. Tele-fetal monitoring is a big progress toward a wearable medical device for pregnant women capable of obtaining prenatal care at home. Study Design: We apply a wavelet transformation algorithm for fetal cardiac monitoring using a portable fetal Doppler medical device. After an investigation of 85 different mother wavelets, a bio-orthogonal 2.2 mother wavelet in level 4 of decomposition is chosen. The efficiency of the proposed method is evaluated using two data sets including public and clinical. Results: From publicly available data on PhysioBank, and simultaneous clinical measurement, we prove that the comparison between obtained fetal heart rate by the algorithm and the baselines yields a promising accuracy beyond 95%. Conclusion: Finally, we conclude that the proposed algorithm would be a robust technique for any similar tele-fetal monitoring approach.
... This method is applied to the bitstream produced by Set Partitioning [11]. Hong Tang et al. developed a novel PCG signals compression technique using sound repetition and vector quantization [51]. In a recent paper, Ying-Ren Chien et al. propose a deep convolutional autoencoder for PCG compression. ...
Remote Healthcare Monitoring Systems (RHMs) that employ fetal phonocardiography (fPCG) signals are highly efficient technologies for monitoring continuous and long-term fetal heart rate. Wearable devices used in RHMs still face a challenge that decreases their efficacy in terms of energy consumption because these devices have limited storage and are powered by batteries. This paper proposes an effective fPCG compression algorithm to reduce RHM energy consumption. In the proposed algorithm, the Discrete Orthogonal Charlier Moment (DOCMs) is used to extract features of the signal. The householder orthonormalization method (HOM) is used with the Charlier Moment to overcome the propagation of numerical errors that occur when computing high-order Charlier polynomials. The proposed algorithm’s performance is evaluated in terms of CR, PRD, SNR, PSNR, and QS and provides the average values 18.33, 0.21, 48.85, 68.86, and 90.88, respectively. The results of the comparison demonstrate the proposed compression algorithm’s superiority over other algorithms. It also tested in terms of compression speed and computational efficiency. The results indicate that the proposed algorithm has a high Compression speed (218.672 bps) and high computational efficiency (21.33). Additionally, the results reveal that the proposed algorithm decreases the energy consumption of a wearable device due to the transmission time decreasing for data by 3.68 s.
... CVD diagnosis can be done by using the widely known auscultation methods based on stethoscope, phonocardiogram, or echocardiogram. A cardiologist expert could use phonocardiogram (or PCG) to visualize the recorded heart sound during a cardiac cycle based on a phonocardiograph device [7,8]. Also, they can use an echocardiogram (average cost of 1500 as per current cost [9]) to visualize the heart beating and blood pumping. ...
Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.
... In literature, several algorithms for heart sound compression have been proposed with the aim of reducing the number of transmitted and stored samples. For example, the algorithm proposed in [16] is based on two steps. In the first one, a dictionary is constructed according to a time-frequency decomposition of the heart sound signal. ...
This paper proposes the use of the Deterministic Binary Block Diagonal (DBBD) matrix as sensing matrix for compressed sensing of heart sound signals. The use of a deterministic matrix has the advantage of not requiring the generation of random numbers in the acquisition node. Moreover, the DBBD matrix has a very low computational complexity at the compression side, as it only requires a sum of the samples. In the paper, the DBBD sensing matrix is used in combination with the Discrete Cosine Transform and the Mexican Hat wavelet to compress and reconstruct heart sound signal obtained from the PhysioNet database. The results show a lower value of the Percent Root Mean Square Difference compared to that obtained by the random sensing matrix previously used in the literature for heart sound signals.
... They utilized the attributes of Hilbert transform (HT) to detect the moment segmentation and peak points of heart sounds by using zero-crossing methods. In 2016, Tang et al. [9] introduced clustering to compress of the PCG signals which reduces storage capacity in the recording system, the study acquired varying compression ratios from 20 to 149 times. Dominguez et al. [10], used the a neuromorphic auditory sensor to analyze audio information in frequency boundaries in real time. ...
... Higher-order spectra [9,16,17] were also extracted for every heartbeat: mean and standard deviation of kurtosis in the S1 interval, the standard deviation of skewness in S1 interval, mean and standard deviation of kurtosis in systole interval, mean values of kurtosis and skewness in the S2 interval and mean and standard deviation of kurtosis and skewness in the diastole interval. Kurtosis and Skewness refer to 'tailedness' and asymmetry in the probability distribution of a variable around its mean, respectively. ...
... In our method, all of the coefficients in the third level of the tree were used, resulting in 7 features. Hurst's Exponent was chosen based on its superior discrimination rate compared to frequently used features such as Sample entropy, log energy, and Shannon's entropy [9,17,25]. Furthermore, Mel frequency Cepstral coefficients (MFCC) were extracted for each heartbeat in each subject. MFCCs are also very popular in heart sound processing [9,11,16,[25][26][27]. ...
Cardiovascular is arguably the most dominant death cause in the world. Heart functionality can be measured in various ways. Heart sounds are usually inspected in these experiments as they can unveil a variety of heart related diseases. This study tackles the lack of reliable models and high training times on a publicly available dataset. The heart sound set is provided by Physionet consisting of 3153 recordings, from which five seconds were fixed to evaluate to the developed method. In this work, we propose a novel method based on feature reduction combination, using Genetic Algorithm (GA) and Principal Component Analysis (PCA). The authors present eight dominant features in heart sound classification: mean duration of systole interval, the standard deviation of diastole interval, the absolute amplitude ratio of diastole to S2, S1 to systole and S1 to diastole, zero crossings, Centroid to Centroid distance (CCdis) and mean power in the 95–295 Hz range. These reduced features are then optimized respectively with two straightforward classification algorithms weighted k-NN with a lower-dimensional feature space and Linear SVM that uses a linear combination of all features to create a robust model, acquiring up to 98.15% accuracy, holding the best stats in the heart sound classification on a largely used dataset. According to the experiments done in this study, the developed method can be further explored for real world heart sound assessments.
... Beritelli et al. proposed a selection algorithm in 2009 to determine the best subsequence from a signal based on cepstral distance measurement [8]. Another best subsequence selection algorithm was proposed by Li et al. based on the degree of heart sound periodicity [9][10][11][12]. Abdollahpur et al. proposed a cycle quality assessment method to select those cycles with little noise or spikes [13]. ...
... A heart sound signal is safely believed to be quasiperiodic [9][10][11]35], and an indicator to evaluate quantitively the degree of periodicity has been proposed in [9][10][11] in the cycle fre-quency domain. If the cycle duration of a heart sound signal is T, the time-varying autocorrelation is ...
... A heart sound signal is safely believed to be quasiperiodic [9][10][11]35], and an indicator to evaluate quantitively the degree of periodicity has been proposed in [9][10][11] in the cycle fre-quency domain. If the cycle duration of a heart sound signal is T, the time-varying autocorrelation is ...
Automated heart sound signal quality assessment is a necessary step for reliable analysis of heart sound signal. An unavoidable processing step for this objective is the heart sound segmentation, which is still a challenging task from a technical viewpoint. In this study, ten features are defined to evaluate the quality of heart sound signal without segmentation. The ten features come from kurtosis, energy ratio, frequency-smoothed envelope, and degree of sound periodicity, where five of them are novel in signal quality assessment. We have collected a total of 7893 recordings from open public heart sound databases and performed manual annotation for each recording as gold standard quality label. The signal quality is classified based on two schemes: binary classification (“unacceptable” and “acceptable”) and triple classification (“unacceptable”, “good,” and “excellent”). Sequential forward feature selection shows that the feature “the degree of periodicity” gives an accuracy rate of 73.1% in binary SVM classification. The top five features dominate the classification performance and give an accuracy rate of 92%. The binary classifier has excellent generalization ability since the accuracy rate reaches to (90.4±0.5) % even if 10% of the data is used to train the classifier. The rate increases to (94.3±0.7) % in 10-fold validation. The triple classification has an accuracy rate of (85.7±0.6) % in 10-fold validation. The results verify the effectiveness of the signal quality assessment, which could serve as a potential candidate as a preprocessing in future automatic heart sound analysis in clinical application.
... For example, the value of CR for free lossless audio codec (FLAC) compression is only about 1.94 [14]; and, the value of CR for lossless ECG compression is 2.56 [15]. Additionally, it has been reported that a medical professional felt the necessity of a high CR and can tolerate a PRD as high as 5% [16]. Thus, our design aims to attain a high CR at the values of PRD are less than 5%. ...
... The optimal value of Q can be found by using the genetic algorithm. In [16], the authors exploited the repetition patterns that were embedded in the PCG signals in order to eliminate their redundancy. After decomposing the PCG signals into the time-frequency domain, the authors proposed clustering the decomposed data to build a dictionary during the training phase. ...
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%.
... Vector quantisation (VQ) is a lossy compression method that is extensively used due to its better recovery of image quality, higher compression ratio and simpler implementation (Chang & Wu, 2007;Cockshott et al., 2008;Cohen, 1988;Gray, 1984;Tang et al., 2016). Linde et al. (1980) first introduced the well-known Linde-Buzo-Gray (LBG) algorithm for generating codebooks in VQ compression. ...
In the information age, a digital image is an important media for people’s daily interactions. Looking to maintain the quality of the restored image, how to maximise the compression of images has become a challenging topic. Vector quantisation (VQ) compression is an easy-operating image compression method that can compress images to 1/16th of the original size. Based on VQ compresses, a novel image compression method is proposed in this paper. The proposed scheme compresses the image depending on the result of linear regression prediction which can significantly increase the compression ratio.
