Short-segment heart sound classification using an ensemble of deep convolutional neural networks

This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.Comment: 8 pages, 1 figure, conferenc

Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models

Recent studies on analyzing dynamic brain connectivity rely on sliding-window analysis or time-varying coefficient models which are unable to capture both smooth and abrupt changes simultaneously. Emerging evidence suggests state-related changes in brain connectivity where dependence structure alternates between a finite number of latent states or regimes. Another challenge is inference of full-brain networks with large number of nodes. We employ a Markov-switching dynamic factor model in which the state-driven time-varying connectivity regimes of high-dimensional fMRI data are characterized by lower-dimensional common latent factors, following a regime-switching process. It enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We consider the switching VAR to quantity the dynamic effective connectivity. We propose a three-step estimation procedure: (1) extracting the factors using principal component analysis (PCA) and (2) identifying dynamic connectivity states using the factor-based switching vector autoregressive (VAR) models in a state-space formulation using Kalman filter and expectation-maximization (EM) algorithm, and (3) constructing the high-dimensional connectivity metrics for each state based on subspace estimates. Simulation results show that our proposed estimator outperforms the K-means clustering of time-windowed coefficients, providing more accurate estimation of regime dynamics and connectivity metrics in high-dimensional settings. Applications to analyzing resting-state fMRI data identify dynamic changes in brain states during rest, and reveal distinct directed connectivity patterns and modular organization in resting-state networks across different states.Comment: 21 page

Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

AT excursion: a new approach to predict replication origins in viral genomes by locating AT-rich regions

<p>Abstract</p> <p>Background</p> <p>Replication origins are considered important sites for understanding the molecular mechanisms involved in DNA replication. Many computational methods have been developed for predicting their locations in archaeal, bacterial and eukaryotic genomes. However, a prediction method designed for a particular kind of genomes might not work well for another. In this paper, we propose the AT excursion method, which is a score-based approach, to quantify local AT abundance in genomic sequences and use the identified high scoring segments for predicting replication origins. This method has the advantages of requiring no preset window size and having rigorous criteria to evaluate statistical significance of high scoring segments.</p> <p>Results</p> <p>We have evaluated the AT excursion method by checking its predictions against known replication origins in herpesviruses and comparing its performance with an existing base weighted score method (BWS₁). Out of 43 known origins, 39 are predicted by either one or the other method and 26 origins are predicted by both. The excursion method identifies six origins not predicted by BWS₁, showing that the AT excursion method is a valuable complement to BWS₁. We have also applied the AT excursion method to two other families of double stranded DNA viruses, the poxviruses and iridoviruses, of which very few replication origins are documented in the public domain. The prediction results are made available as supplementary materials at <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>. Preliminary investigation shows that the proposed method works well on some larger genomes too.</p> <p>Conclusion</p> <p>The AT excursion method will be a useful computational tool for identifying replication origins in a variety of genomic sequences.</p

Outcomes in Neonates with Pulmonary Atresia and Intact Ventricular Septum Underwent Pulmonary Valvulotomy and Valvuloplasty Using a Flexible 2-French Radiofrequency Catheter

Purpose : Outcomes in 6 neonates with pulmonary atresia and intact ventricular septum (PAIVS) Undergoing radiofrequency pulmonary valvulotomy and valvuloplasty (RPVV) were reported to identify the factors favorable for RPVV as the treatment of choice. Materials and Methods: From May 2000 to January 2008, 6 patients with PAWS were included in this retrospective study. They were aged I day to 90 days old. Study modalities included review of recordings of presentations and profiles of chest radiography, electrocardiography, echocardiography, and cardiac catheterization with angiography. Hemodynamic profiles from the echocardiography and the cardiac catheterization were analyzed. Results: Echocardiography showed severe tricuspid regurgitation, membranous atresia of the pulmonary valve, intact ventricular septum, patent ductus arteriosus, and hypoplastic right ventricle in 6 patients. The pulmonary valve annulus were 4.2 to 6.9 mm in diameters, and those of the tricuspid valve were 7.1 to 10.1 mm. Elevated serum level of cardiac enzymes were found in 1 patient with ventriculocoronary communication (VCC). At cardiac catheterization, the ratio of systolic pressure of the right ventricle to that of the left ventricle ranged from 1.43 to 2.33 before RPVV, and from 0.54 to 1.15 after RPVV ((p=0. 027). The pressure gradients ranged from 76 to 136 mmHg before RPVV, and from 15 to 39 mmHg after RPVV (p=0.028). The echocardiographic gradients ranged from 16 to 32 mmHg within 24 hours after RPVV, and from 15 to 50 mmHg at the follow-ups. Conclusion: RPVV can be a treatment of choice for neonates with PAIVS, if there is patent infundibulum, no right-ventricular dependent coronary circulation, and adequate tricuspid valve and pulmonary valve

Heart sound monitoring sys

Cardiovascular disease (CVD) is among the leading life threatening ailments [1] [2].Under normal circumstances, a cardiac examination utilizing electrocardiogram appliances or tools is proposed for a person stricken with a heart disorder. The logging of irregular heart behaviour and morphology is frequently achieved through an electrocardiogram (ECG) produced by an electrocardiographic appliance for tracing cardiac activity. For the most part, gauging of this activity is achieved through a non-invasive procedure i.e. through skin electrodes. Taking into consideration the ECG and heart sound together with clinical indications, the cardiologist arrives at a diagnosis on the condition of the patient's heart. This paper focuses on the concerns stated above and utilizes the signal processing theory to pave the way for better heart auscultation performance by GPs. The objective is to take note of heart sounds in correspondence to the valves as these sounds are a source of critical information. Comparative investigations regarding MFCC features with varying numbers of HMM states and varying numbers of Gaussian mixtures were carried out for the purpose of determining the impact of these features on the classification implementation at the sites of heart sound auscultation. We employ new strategy to evaluate and denoise the heart and ecg signal with a specific end goal to address specific issues