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

An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

BACKGROUND AND OBJECTIVES

Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate.

METHODS

To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms.

MAIN RESULTS

Among all classifiers, ViT provides the best performance by achieving mean average accuracy A_cc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models.

CONCLUSIONS

The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.

Novel Method for Fetal and Maternal Heart Rate Measurements Using 2-D Ultrasound Color Doppler Flow Images

Fetal heart rate (FHR) and maternal heart rate (MHR) are important indicators of fetal well-being during pregnancy. A common method in clinical examination is to estimate the FHR using the Doppler shift of echoes from umbilical artery blood flow based on an ultrasound pulsed-wave (PW) Doppler technique. Similarly, a sampling gate can be located at the maternal blood flow to measure MHR using PW Doppler. Ultrasound color Doppler flow imaging (CDFI) is one of the most commonly used imaging modes for clinical fetal examinations. Color coding is employed to display the blood flow velocity and direction in color grades according to the Doppler shift. Continuous CDF images contain dynamic changes characteristics of the blood flow. The periodic characteristics can be used to obtain heart rate information. Therefore, here we propose a novel method to measure FHR and MHR simultaneously using CDF images. The proposed method calculates the histogram of color similarity of CDF images to initially extract the periodic characteristics of the CDF image sequence. The histogram of color similarity function is then processed by a bandpass filter and autocorrelation operation to reduce noise and enhance periodicity. Finally, peak detection is performed on the processed signal to obtain the period and estimate the heart rate. The proposed method can measure the FHR and MHR in parallel after selecting two regions containing the umbilical artery and maternal blood flow, respectively. Thus, the method has high computational efficiency. The proposed method was evaluated on a Doppler flow phantom and clinical CDF images and then compared with the PW Doppler method. The correlation analysis and Bland-Altman plots reveal that the proposed method agrees well with the PW Doppler. It is a sanity check method for real-time clinical FHR and MHR measurements.

Passive Fetal Movement Recognition Approaches Using Hyperparameter Tuned LightGBM Model and Bayesian Optimization

Fetal movement is an important clinical indicator to assess fetus growth and development status in the uterus. In recent years, a noninvasive intelligent sensing fetal movement detection system that can monitor high-risk pregnancies at home has received a lot of attention in the field of wearable health monitoring. However, recovering fetal movement signals from a continuous low-amplitude background that is heavily contaminated with noise and recognizing real fetal movements is a challenging task. In this paper, fetal movement can be efficiently recognized by combining the strength of Kalman filtering, time and frequency domain and wavelet domain feature extraction, and hyperparameter tuned Light Gradient Boosting Machine (LightGBM) model. Firstly, the Kalman filtering (KF) algorithm is used to recover the fetal movement signal in a continuous low-amplitude background contaminated by noise. Secondly, the time domain, frequency domain, and wavelet domain (TFWD) features of the preprocessed fetal movement signal are extracted. Finally, the Bayesian Optimization algorithm (BOA) is used to optimize the LightGBM model to obtain the optimal hyperparameters. Through this, the accurate prediction and recognition of fetal movement are successfully achieved. In the performance analysis of the Zenodo fetal movement dataset, the proposed KF + TFWD + BOA-LGBM approach's recognition accuracy and F1-Score reached 94.06% and 96.85%, respectively. Compared with 8 existing advanced methods for fetal movement signal recognition, the proposed method has better accuracy and robustness, indicating its potential medical application in wearable smart sensing systems for fetal prenatal health monitoring.