Wearable Electrocardiogram Quality Assessment Using Wavelet Scattering and LSTM.

As the fast development of wearable devices and Internet of things technologies, real-time monitoring of ECG signals is quite critical for cardiovascular diseases. However, dynamic ECG signals recorded in free-living conditions suffered from extremely serious noise pollution. Presently, most algorithms for ECG signal evaluation were designed to divide signals into acceptable and unacceptable. Such classifications were not enough for real-time cardiovascular disease monitoring. In the study, a wearable ECG quality database with 50,085 recordings was built, including A/B/C (or high quality/medium quality/low quality) three quality grades (A: high quality signals can be used for CVD detection; B: slight contaminated signals can be used for heart rate extracting; C: heavily polluted signals need to be abandoned). A new SQA classification method based on a three-layer wavelet scattering network and transfer learning LSTM was proposed in this study, which can extract more systematic and comprehensive characteristics by analyzing the signals thoroughly and deeply. Experimental results ( mACC = 98.56%, mF 1 = 98.55%, Se A = 97.90%, Se B = 98.16%, Se C = 99.60%, + P A = 98.52%, + P B = 97.60%, + P C = 99.54%, F 1A = 98.20%, F 1B = 97.90%, F 1C = 99.60%) and real data validations proved that this proposed method showed the high accuracy, robustness, and computationally efficiency. It has the ability to evaluate the long-term dynamic ECG signal quality. It is advantageous to promoting cardiovascular disease monitoring by removing contaminating signals and selecting high-quality signal segments for further analysis.

Disparities in Hepatocellular Carcinoma Survival by Insurance Status: A Population-Based Study in China.

Objective: Health disparities related to basic medical insurance in China have not been sufficiently examined, particularly among patients with hepatocellular carcinoma (HCC). This study aims to investigate the disparities in HCC survival by insurance status in Tianjin, China. Methods: This retrospective analysis used data from the Tianjin Basic Medical Insurance claims database, which consists of enrollees covered by Urban Employee Basic Medical Insurance (UEBMI) and Urban and Rural Resident Basic Medical Insurance (URRBMI). Adult patients newly diagnosed with HCC between 2011 and 2016 were identified and followed until death from any cause, withdrawal from UEBMI or URRBMI, or the latest data in the dataset (censoring as of December 31st 2017), whichever occurred first. Patients' overall survival during the follow-up was assessed using Kaplan-Meier and extrapolated by six parametric models. The hazard ratio (HR) and 95% confidence intervals (CI) were calculated with the adjusted Cox proportional hazards model including age at diagnosis, sex, baseline comorbidities and complications, baseline healthcare resources utilization and medical costs, tumor metastasis at diagnosis, the initial treatment after diagnosis and antiviral therapy during the follow-up. Results: Two thousand sixty eight patients covered by UEBMI (N = 1,468) and URRBMI (N = 570) were included (mean age: 60.6 vs. 60.9, p = 0.667; female: 31.8 vs. 27.7%, p = 0.074). The median survival time for patients within the UEBMI and URRBMI were 37.8 and 12.2 months, and the 1-, 3-, 5-, 10-year overall survival rates were 63.8, 50.2, 51.0, 33.4, and 44.4, 22.8, 31.5, 13.1%, respectively. Compared with UEBMI, patients covered by URRBMI had 72% (HR: 1.72; 95% CI: 1.47-2.00) higher risk of death after adjustments for measured confounders above. The survival difference was still statistically significant (HR: 1.49; 95% CI: 1.21-1.83) in sensitivity analysis based on propensity score matching. Conclusions: This study reveals that HCC patients covered by URRBMI may have worse survival than patients covered by UEBMI. Further efforts are warranted to understand healthcare disparities for patients covered by different basic medical insurance in China.

Assessment of Systemic Inflammation and Nutritional Indicators in Predicting Recurrence-Free Survival After Surgical Resection of Gastrointestinal Stromal Tumors.

BACKGROUND: Recent studies have shown that the systemic inflammation and nutritional indicators are prognostic for a variety of malignancies. However, only limited data have so far demonstrated their usefulness in gastrointestinal mesenchymal tumors (GIST). METHODS: We retrospectively analyzed the data of GIST patients who underwent radical surgery in Beijing hospital from October 2004 to July 2018. The area under the receiver operating characteristic curve (AUC) was used to compare several commonly used inflammatory and nutritional indicators. The indicators with largest AUC were further analysis. Optimal cut-off values of those indicators in predicting recurrence-free survival (RFS) were determined. Kaplan-Meier curve and the time-dependent receiver operating characteristic (ROC) curve were used to assess the prognostic values. We then used univariate and multivariate Cox regression analyses to identify prognostic factors that were associated with RFS. RESULTS: In total, 160 patients who underwent surgery for GIST were included in the study. The median survival time was 34.5 months, with 1-, 3-, and 5-year RFS rates of 96.1%, 84.7%, and 80.8%, respectively. The inflammatory and nutritional indicators with largest AUC were Systemic immunoinflammatory Index (SII) and Geriatric Nutrition Risk Index (GNRI), reached 0.650 and 0.713, respectively. The optimal cutoff of GNRI and SII were 98.3, and 820.0, respectively. Univariate analysis showed that GNRI, SII, KI67, surgery method, tumor location, tumor size, and mitotic index were all significant prognostic indicators of RFS. After multivariate Cox analysis, independent prognostic factors for RFS in GIST included tumor location, mitotic index, tumor size, and GNRI (HR=2.802,95% CI: 1.045 to 7.515, p = 0.041). Besides, SII also tended to be associated with RFS (HR = 2.970, 95% CI: 0.946 to 9.326, p = 0.062). CONCLUSIONS: High GNRI is an independent prognostic factor for RFS in GIST, while SII can be considered as a prognostic factor. GNRI and SII can be used as tools to evaluate the prognosis of patients before surgery, helping doctors to better treat high-risk patients.

Remote health diagnosis and monitoring in the time of COVID-19.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.

Performance of an open-source heart sound segmentation algorithm on eight independent databases.

OBJECTIVE: Heart sound segmentation is a prerequisite step for the automatic analysis of heart sound signals, facilitating the subsequent identification and classification of pathological events. Recently, hidden Markov model-based algorithms have received increased interest due to their robustness in processing noisy recordings. In this study we aim to evaluate the performance of the recently published logistic regression based hidden semi-Markov model (HSMM) heart sound segmentation method, by using a wider variety of independently acquired data of varying quality. APPROACH: Firstly, we constructed a systematic evaluation scheme based on a new collection of heart sound databases, which we assembled for the PhysioNet/CinC Challenge 2016. This collection includes a total of more than 120 000 s of heart sounds recorded from 1297 subjects (including both healthy subjects and cardiovascular patients) and comprises eight independent heart sound databases sourced from multiple independent research groups around the world. Then, the HSMM-based segmentation method was evaluated using the assembled eight databases. The common evaluation metrics of sensitivity, specificity, accuracy, as well as the [Formula: see text] measure were used. In addition, the effect of varying the tolerance window for determining a correct segmentation was evaluated. MAIN RESULTS: The results confirm the high accuracy of the HSMM-based algorithm on a separate test dataset comprised of 102 306 heart sounds. An average [Formula: see text] score of 98.5% for segmenting S1 and systole intervals and 97.2% for segmenting S2 and diastole intervals were observed. The [Formula: see text] score was shown to increases with an increases in the tolerance window size, as expected. SIGNIFICANCE: The high segmentation accuracy of the HSMM-based algorithm on a large database confirmed the algorithm's effectiveness. The described evaluation framework, combined with the largest collection of open access heart sound data, provides essential resources for evaluators who need to test their algorithms with realistic data and share reproducible results.

Continuous assessment of schizophrenia using heart rate and accelerometer data.

OBJECTIVE: Schizophrenia has been associated with changes in heart rate (HR) and physical activity measures. However, the relationship between analysis window length and classifier accuracy using these features has yet to be quantified. APPROACH: Here we used objective HR and activity data to classify contiguous days of data as belonging to a schizophrenia patient or a healthy control. HR and physical activity recordings were made on 12 medicated subjects with schizophrenia and 12 healthy controls. Features derived from these data included classical statistical characteristics, rest-activity metrics, transfer entropy, and multiscale fuzzy entropy. We varied the analysis window length from two to eight days, and selected features via minimal-redundancy-maximal-relevance. A support vector machine was trained to classify schizophrenia from control windows on a daily basis. Model performance was assessed via subject-wise leave-one-out-crossfold-validation. MAIN RESULTS: An analysis window length of eight days resulted in an area under a receiver operating characteristic curve (AUC) of 0.96. Reducing the analysis window length to two days only lowered the AUC to 0.91. The type of most predictive features varied with analysis window length. SIGNIFICANCE: Our results suggest continuous tracking of subjects with schizophrenia over short time scales may be sufficient to estimate illness severity on a daily basis.

Functional assessment of CTCF sites at cytokine-sensing mammary enhancers using CRISPR/Cas9 gene editing in mice.

The zinc finger protein CTCF has been invoked in establishing boundaries between genes, thereby controlling spatial and temporal enhancer activities. However, there is limited genetic evidence to support the concept that these boundaries restrict the search space of enhancers. We have addressed this question in the casein locus containing five mammary and two non-mammary genes under the control of at least seven putative enhancers. We have identified two CTCF binding sites flanking the locus and two associated with a super-enhancer. Individual deletion of these sites from the mouse genome did not alter expression of any of the genes. However, deletion of the border CTCF site separating the Csn1s1 mammary enhancer from neighboring genes resulted in the activation of Sult1d1 at a distance of more than 95 kb but not the more proximal and silent Sult1e1 gene. Loss of this CTCF site led to de novo interactions between the Sult1d1 promoter and several enhancers in the casein locus. Our study demonstrates that only one out of the four CTCF sites in the casein locus had a measurable in vivo activity. Studies on additional loci are needed to determine the biological role of CTCF sites associated with enhancers.

A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal QRS complexes from abdominal ECG recordings.

Non-invasive monitoring of fetal electrocardiogram (fECG) plays an important role in detecting and diagnosing fetal diseases. This study aimed to develop a multi-step method for locating both maternal and fetal QRS complexes from abdominal ECG (aECG) recordings. The proposed method included four major steps: abdominal ECG pre-processing, maternal QRS complex locating, maternal ECG cancellation and fetal QRS complex locating. Signal quality assessment (SQA) and fine-tuning for maternal ECG (FTM) were implemented in the first and third steps, respectively. The method was then evaluated using 75 non-invasive 4-channel aECG recordings provided by the PhysioNet/Computing in Cardiology Challenge 2013. The F1 measure, which is a new index introduced by Behar et al (2013 Proc. Comput. Cardiol. 40 297-300), was used to assess the locating accuracy. The other two indices, mean squared error of heart rate (MSE_HR) between the fetal HR signals estimated from the reference and our method (MSE_HR in bpm(2)) and root mean squared difference between the corresponding fetal RR intervals (MSE_RR in ms) were also used to assess the locating accuracy. Overall, for the maternal QRS complex, the F1 measure was 98.4% from the method without the implementation of SQA, and it was improved to 99.8% with SQA. For the fetal QRS complex, the F1 measure, MSE_HR and MSE_RR were 84.9%, 185.6 bpm(2) and 19.4 ms for the method without both SQA and FTM procedures. They were improved to 93.9%, 47.5 bpm(2) and 7.6 ms with both SQA and FTM procedures. These improvements were observed from each individual subject. It can be concluded that implementing both SQA and FTM procedures could achieve better performance for locating both maternal and fetal QRS complexes.