Extracting fetal heart beats from maternal abdominal recordings: Selection of the optimal principal components

This study presents a systematic comparison of different approaches to the automated selection of the principal components (PC) which optimise the detection of maternal and fetal heart beats from non-invasive maternal abdominal recordings. A public database of 75 4-channel non-invasive maternal abdominal recordings was used for training the algorithm. Four methods were developed and assessed to determine the optimal PC: (1) power spectral distribution, (2) root mean square, (3) sample entropy, and (4) QRS template. The sensitivity of the performance of the algorithm to large-amplitude noise removal (by wavelet de-noising) and maternal beat cancellation methods were also assessed. The accuracy of maternal and fetal beat detection was assessed against reference annotations and quantified using the detection accuracy score F1 [2*PPV*Se / (PPV + Se)], sensitivity (Se), and positive predictive value (PPV). The best performing implementation was assessed on a test dataset of 100 recordings and the agreement between the computed and the reference fetal heart rate (fHR) and fetal RR (fRR) time series quantified. The best performance for detecting maternal beats (F1 99.3%, Se 99.0%, PPV 99.7%) was obtained when using the QRS template method to select the optimal maternal PC and applying wavelet de-noising. The best performance for detecting fetal beats (F1 89.8%, Se 89.3%, PPV 90.5%) was obtained when the optimal fetal PC was selected using the sample entropy method and utilising a fixed-length time window for the cancellation of the maternal beats. The performance on the test dataset was 142.7 beats2/min2 for fHR and 19.9 ms for fRR, ranking respectively 14 and 17 (out of 29) when compared to the other algorithms presented at the Physionet Challenge 2013

Comparison of stethoscope bell and diaphragm, and of stethoscope tube length, for clinical blood pressure measurement

OBJECTIVE: This study investigated the effect of stethoscope side and tube length on auscultatory blood pressure (BP) measurement. METHODS: Thirty-two healthy participants were studied. For each participant, four measurements with different combinations of stethoscope characteristics (bell or diaphragm side, standard or short tube length) were each recorded at two repeat sessions, and eight Korotkoff sound recordings were played twice on separate days to one experienced listener to determine the systolic and diastolic BPs (SBP and DBP). Analysis of variance was carried out to study the measurement repeatability between the two repeat sessions and between the two BP determinations on separate days, as well as the effects of stethoscope side and tube length. RESULTS: There was no significant paired difference between the repeat sessions and between the repeat determinations for both SBP and DBP (all P-values>0.10, except the repeat session for SBP using short tube and diaphragm). The key result was that there was a small but significantly higher DBP on using the bell in comparison with the diaphragm (0.66 mmHg, P=0.007), and a significantly higher SBP on using the short tube in comparison with the standard length (0.77 mmHg, P=0.008). CONCLUSION: This study shows that stethoscope characteristics have only a small, although statistically significant, influence on clinical BP measurement. Although this helps understand the measurement technique and resolves questions in the published literature, the influence is not clinically significant

Conversation Disentanglement with Bi-Level Contrastive Learning

Conversation disentanglement aims to group utterances into detached sessions, which is a fundamental task in processing multi-party conversations. Existing methods have two main drawbacks. First, they overemphasize pairwise utterance relations but pay inadequate attention to the utterance-to-context relation modeling. Second, huge amount of human annotated data is required for training, which is expensive to obtain in practice. To address these issues, we propose a general disentangle model based on bi-level contrastive learning. It brings closer utterances in the same session while encourages each utterance to be near its clustered session prototypes in the representation space. Unlike existing approaches, our disentangle model works in both supervised setting with labeled data and unsupervised setting when no such data is available. The proposed method achieves new state-of-the-art performance on both settings across several public datasets

Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively

Interactive Speech and Noise Modeling for Speech Enhancement

Speech enhancement is challenging because of the diversity of background noise types. Most of the existing methods are focused on modelling the speech rather than the noise. In this paper, we propose a novel idea to model speech and noise simultaneously in a two-branch convolutional neural network, namely SN-Net. In SN-Net, the two branches predict speech and noise, respectively. Instead of information fusion only at the final output layer, interaction modules are introduced at several intermediate feature domains between the two branches to benefit each other. Such an interaction can leverage features learned from one branch to counteract the undesired part and restore the missing component of the other and thus enhance their discrimination capabilities. We also design a feature extraction module, namely residual-convolution-and-attention (RA), to capture the correlations along temporal and frequency dimensions for both the speech and the noises. Evaluations on public datasets show that the interaction module plays a key role in simultaneous modeling and the SN-Net outperforms the state-of-the-art by a large margin on various evaluation metrics. The proposed SN-Net also shows superior performance for speaker separation.Comment: AAAI 2021 (Accepted

Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets

Variation of Korotkoff stethoscope sounds during blood pressure measurement: Analysis using a convolutional neural network

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heart beats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as ‘Korotkoff’. A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10-fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analysed separately, allowing consistency to be evaluated between groups. Next, within-subject variation of the Korotkoff sounds analysed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analysed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P < 0.001). Results reached stability for SBP (97.8%, at 6th beats below SBP) and DBP (98.1%, at 6th beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at high cuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P < 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject

Effect of multiple clinical factors on recurrent angina after percutaneous coronary intervention: A retrospective study from 398 ST-segment elevation myocardial infarction patients

Recurrent angina (RA) has an important influence on health status of patients after percutaneous coronary intervention (PCI). This study aimed to retrospectively investigate the effect of multiple clinical factors on both short-term and long-term development of RA.A total of 398 ST-segment elevation myocardial infarction (STEMI) patients were studied for up to 12 months. The primary clinical outcome, RA, was assessed at 1-month and 12-month. In multivariate analyses, the effect of clinical factors, including baseline demographics, medical history, infarction-related arteries, procedural characteristics of PCI, and the use of medicines, was investigated in patients with and without RA.The Logistic regression analysis showed that the patients with treatment through radial approach PCI (odds ratio [OR]: 0.42, 95% confidence interval [CI]: 0.18-0.96, P < 0.05) were less likely to have RA during 1-month assessment. During 12 months after PCI, male patients (OR: 0.53, 95% CI: 0.29-0.96, P < 0.05), and/or those treated with radial approach PCI (OR: 0.45, 95% CI: 0.21-0.97, P < 0.05) were less likely to have RA, whereas the patients with infarction related artery (IRA) in left anterior descending (LAD) (OR: 2.41, 95% CI: 1.20-4.84, P < 0.01) were more likely to have RA at follow-up. The Cox regression analysis further revealed that the patients with infarction of the LAD artery (hazard ratio [HR]: 2.08, 95% CI: 1.10-3.92, P < 0.05), but not with treatment through radial artery during PCI (HR: 0.42, 95% CI: 0.18-0.96, P < 0.05) had higher potential of development of RA during 12 months after PCI.We studied the effects of multiple clinical factors on the development of RA after PCI. Our findings suggest that patients with infarction of the LAD artery, and/or treatment not through radial artery during PCI were associated with higher risk of RA and may require close follow-up

MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection

Event detection (ED) is aimed to identify the key trigger words in unstructured text and predict the event types accordingly. Traditional ED models are too data-hungry to accommodate real applications with scarce labeled data. Besides, typical ED models are facing the context-bypassing and disabled generalization issues caused by the trigger bias stemming from ED datasets. Therefore, we focus on the true few-shot paradigm to satisfy the low-resource scenarios. In particular, we propose a multi-step prompt learning model (MsPrompt) for debiasing few-shot event detection, that consists of the following three components: an under-sampling module targeting to construct a novel training set that accommodates the true few-shot setting, a multi-step prompt module equipped with a knowledge-enhanced ontology to leverage the event semantics and latent prior knowledge in the PLMs sufficiently for tackling the context-bypassing problem, and a prototypical module compensating for the weakness of classifying events with sparse data and boost the generalization performance. Experiments on two public datasets ACE-2005 and FewEvent show that MsPrompt can outperform the state-of-the-art models, especially in the strict low-resource scenarios reporting 11.43% improvement in terms of weighted F1-score against the best-performing baseline and achieving an outstanding debiasing performance