Computer method for perinatal screening of cardiac murmur using fetal phonocardiography

Sensing Devices for Detecting and Processing Acoustic Signals in Healthcare

Acoustic signals are important markers to monitor physiological and pathological conditions, e.g., heart and respiratory sounds. The employment of traditional devices, such as stethoscopes, has been progressively superseded by new miniaturized devices, usually identified as microelectromechanical systems (MEMS). These tools are able to better detect the vibrational content of acoustic signals in order to provide a more reliable description of their features (e.g., amplitude, frequency bandwidth). Starting from the description of the structure and working principles of MEMS, we provide a review of their emerging applications in the healthcare field, discussing the advantages and limitations of each framework. Finally, we deliver a discussion on the lessons learned from the literature, and the open questions and challenges in the field that the scientific community must address in the near future.

A comparative study of single-channel signal processing methods in fetal phonocardiography

Fetal phonocardiography is a non-invasive, completely passive and low-cost method based on sensing acoustic signals from the maternal abdomen. However, different types of interference are sensed along with the desired fetal phonocardiography. This study focuses on the comparison of fetal phonocardiography filtering using eight algorithms: Savitzky-Golay filter, finite impulse response filter, adaptive wavelet transform, maximal overlap discrete wavelet transform, variational mode decomposition, empirical mode decomposition, ensemble empirical mode decomposition, and complete ensemble empirical mode decomposition with adaptive noise. The effectiveness of those methods was tested on four types of interference (maternal sounds, movement artifacts, Gaussian noise, and ambient noise) and eleven combinations of these disturbances. The dataset was created using two synthetic records r01 and r02, where the record r02 was loaded with higher levels of interference than the record r01. The evaluation was performed using the objective parameters such as accuracy of the detection of S1 and S2 sounds, signal-to-noise ratio improvement, and mean error of heart interval measurement. According to all parameters, the best results were achieved using the complete ensemble empirical mode decomposition with adaptive noise method with average values of accuracy = 91.53% in the detection of S1 and accuracy = 68.89% in the detection of S2. The average value of signal-to-noise ratio improvement achieved by complete ensemble empirical mode decomposition with adaptive noise method was 9.75 dB and the average value of the mean error of heart interval measurement was 3.27 ms.

Can empirical mode decomposition improve heartbeat detection in fetal phonocardiography signals?

BACKGROUND AND OBJECTIVE

A fetal phonocardiography signal can be hard to interpret and classify due to various sources of additive noise in the womb, spanning from fetal movement to maternal heart sounds. Nevertheless, the non-invasive nature of the method makes it potentially suitable for long-term monitoring of fetal health, especially since it can be implemented on ubiquitous devices such as smartphones. We have employed empirical mode decomposition for the extraction of intrinsic mode functions that would enable the utilization of additional characteristics from the signal.

METHODS

Fetal heart recordings from 7 pregnant women in the 3rd trimester or pregnancy were taken in parallel with a measurement microphone and a portable Doppler device. Signal peaks positions from the Doppler were taken as the locations of S1 heart sounds and subsequently used as classification labels for the microphone signal. After employing a moving window approach for segmentation, more than 7600 observations were stored in the final dataset. The 135 extracted features consisted of typical audio temporal and spectral characteristics, each taken from separate sets of audio signals and intrinsic mode functions. We have used a number of metrics and methods to validate the usability of features, including univariate analysis of feature ranking and importance. Furthermore, we have used machine learning to train a number of classifiers to validate the usability of features based on intrinsic mode functions, taking prediction accuracy as the comparison metric.

RESULTS

Features extracted from intrinsic mode functions combined with audio features significantly improve accuracy in comparison to using only audio features. The improvements of detection accuracy obtained with a selected set of combined features spanned from 3.8% to even 10.3% based on the employed classifier.

CONCLUSIONS

We have utilized empirical mode decomposition as a method of extracting features relevant for fetal heartbeat classification. The results show consistent improvements in detection accuracy when these characteristics are added to a set of conventional audio features. This implies substantial benefits of applying empirical mode decomposition and lays the groundwork for future research on fetal heartbeat detection.

Analysis of PCG signals using quality assessment and homomorphic filters for localization and classification of heart sounds

BACKGROUND AND OBJECTIVE

Accurate localization of heart beats in phonocardiogram (PCG) signal is very crucial for correct segmentation and classification of heart sounds into S1 and S2. This task becomes challenging due to inclusion of noise in acquisition process owing to number of different factors. In this paper we propose a system for heart sound localization and classification into S1 and S2. The proposed system introduces the concept of quality assessment before localization, feature extraction and classification of heart sounds.

METHODS

The signal quality is assessed by predefined criteria based upon number of peaks and zero crossing of PCG signal. Once quality assessment is performed, then heart beats within PCG signal are localized, which is done by envelope extraction using homomorphic envelogram and finding prominent peaks. In order to classify localized peaks into S1 and S2, temporal and time-frequency based statistical features have been used. Support Vector Machine using radial basis function kernel is used for classification of heart beats into S1 and S2 based upon extracted features. The performance of the proposed system is evaluated using Accuracy, Sensitivity, Specificity, F-measure and Total Error. The dataset provided by PASCAL classifying heart sound challenge is used for testing.

RESULTS

Performance of system is significantly improved by quality assessment. Results shows that proposed Localization algorithm achieves accuracy up to 97% and generates smallest total average error among top 3 challenge participants. The classification algorithm achieves accuracy up to 91%.

CONCLUSION

The system provides firm foundation for the detection of normal and abnormal heart sounds for cardiovascular disease detection.

Prenatal identification of CHD murmur using four segment phonocardiographic signal analysis

Congenital heart defects (CHD) are one of the utmost birth defects present in the neonatal after birth and a big challenge for the researchers to identify the structural abnormality during the antepartum period. An algorithm is presented here to identify the presence of CHD through foetal phonocardiographic (fPCG) signals. The recorded fPCG is decomposed using Daubechies4 wavelet with sub-level threshold to remove the noise in the signal. The Shannon energy is used to identify the different peaks of signals and then S1 and S2 according to the intervals between adjacent peaks. The signal is segmented into four important parts: S1, S1S2, S2 and S2S1. The FFT is used to identify the frequency component present in four segments which in turn indicates the presence of pathological murmur that may turn into CHD. The algorithm is tested on 25 samples with accuracy rate 88% in identifying the presence of a murmur.

Fetal Heart Rate Monitoring from Phonocardiograph Signal Using Repetition Frequency of Heart Sounds

As a passive, harmless, and low-cost diagnosis tool, fetal heart rate (FHR) monitoring based on fetal phonocardiography (fPCG) signal is alternative to ultrasonographic cardiotocography. Previous fPCG-based methods commonly relied on the time difference of detected heart sound bursts. However, the performance is unavoidable to degrade due to missed heart sounds in very low signal-to-noise ratio environments. This paper proposes a FHR monitoring method using repetition frequency of heart sounds. The proposed method can track time-varying heart rate without both heart sound burst identification and denoising. The average accuracy rate comparison to benchmark is 88.3% as the SNR ranges from −4.4 dB to −26.7 dB.

Physiological acoustic sensing based on accelerometers: a survey for mobile healthcare

This paper reviews the applications of accelerometers on the detection of physiological acoustic signals such as heart sounds, respiratory sounds, and gastrointestinal sounds. These acoustic signals contain a rich reservoir of vital physiological and pathological information. Accelerometer-based systems enable continuous, mobile, low-cost, and unobtrusive monitoring of physiological acoustic signals and thus can play significant roles in the emerging mobile healthcare. In this review, we first briefly explain the operation principle of accelerometers and specifications that are important for mobile healthcare. Applications of accelerometer-based monitoring systems are then presented. Next, we review a variety of accelerometers which have been reported in literatures for physiological acoustic sensing, including both commercial products and research prototypes. Finally, we discuss some challenges and our vision for future development.

Screening for congenital heart diseases by murmurs using telemedical phonocardiography

A large proportion of congenital heart diseases (CHD) remain undetected during pregnancy or even after birth. Many of them generate turbulent blood flow, resulting heart murmur. Doppler ultrasound cardiotocography (CTG) is suitable for the assessment of the fetal heart rate and some derived parameters, but it is inadequate for detecting heart murmurs. Although comprehensive examination can be carried out with echocardiography, it is expensive and requires expertise; therefore, it is not applicable for widespread screening. This paper presents a new possibility for screening for some CHDs using phonocardiography, which can be combined with Doppler ultrasound CTG as an extension of it. Furthermore it can be carried out at home allowing repeated measurements, which increases also the reliability of filtering out innocent murmurs. The diagnostic capability of this screening method is supported by a large number of evaluated fetal heart sound records. Moreover, according to experiences pregnant women prefer this reliable, easy to use method, which facilitates their examination.

Fetal phonocardiography--past and future possibilities

The paper presents an overview of the 15 year long development of fetal phonocardiography including the works on the applied signal processing methods for identification of sound components. Based on the improvements achieved on this field, the paper shows that beyond the traditional CTG test the phonocardiography may be successfully applied for long-term fetal measurements and home monitoring. In addition, by indication of heart murmurs based on a comprehensive analysis of the recorded heart sound congenital heart defects can also be detected together with additional features in the third trimester. This makes an early widespread screening possible combined with the prescribed CTG test even at home using a telemedicine system.

A new, phonocardiography-based telemetric fetal home monitoring system

The purpose of this article is to describe a new, phonocardiography-based fetal telemonitoring system, which, due to its passive nature, allows long-term measurements even at the home of the pregnant woman. The input element of the system was the home monitor with two sensors for recording the trans-abdominal fetal heart signal and the uterine contractions. The recorded signal was transmitted by mobile network and Internet to an Evaluation Center, where it was analyzed in detail to obtain information about possible dysfunction of the fetal heart. The investigations on this system made clear that by advanced processing of the recorded signal the system captured many additional cardiac features compared with the traditional ultrasound-based cardiotocographic procedure.