In the past few decades, analysis of heart sound signals (i.e. the phonocardiogram or PCG), especially for automated heart sound segmentation and classification, has been widely studied and has been reported to have the potential value to detect pathology accurately in clinical applications. However, comparative analyses of algorithms in the literature have been hindered by the lack of high-quality, rigorously validated, and standardized open databases of heart sound recordings. This paper describes a public heart sound database, assembled for an international competition, the PhysioNet/Computing in Cardiology (CinC) Challenge 2016. The archive comprises nine different heart sound databases sourced from multiple research groups around the world. It includes 2435 heart sound recordings in total collected from 1297 healthy subjects and patients with a variety of conditions, including heart valve disease and coronary artery disease. The recordings were collected from a variety of clinical or nonclinical (such as in-home visits) environments and equipment. The length of recording varied from several seconds to several minutes. This article reports detailed information about the subjects/patients including demographics (number, age, gender), recordings (number, location, state and time length), associated synchronously recorded signals, sampling frequency and sensor type used. We also provide a brief summary of the commonly used heart sound segmentation and classification methods, including open source code provided concurrently for the Challenge. A description of the PhysioNet/CinC Challenge 2016, including the main aims, the training and test sets, the hand corrected annotations for different heart sound states, the scoring mechanism, and associated open source code are provided. In addition, several potential benefits from the public heart sound database are discussed.

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Skilled cardiologists perform cardiac auscultation, acquiring and interpreting heart sounds, by implicitly carrying out a sequence of steps. These include discarding clinically irrelevant beats, selectively tuning in to particular frequencies and aggregating information across time to make a diagnosis. In this paper, we formalize a series of analytical stages for processing heart sounds, propose algorithms to enable computers to approximate these steps, and investigate the effectiveness of each step in extracting relevant information from actual patient data. Through such reasoning, we provide insight into the relative difficulty of the various tasks involved in the accurate interpretation of heart sounds. We also evaluate the contribution of each analytical stage in the overall assessment of patients. We expect our framework and associated software to be useful to educators wanting to teach cardiac auscultation, and to primary care physicians, who can benefit from presentation tools for computer-assisted diagnosis of cardiac disorders. Researchers may also employ the comprehensive processing provided by our framework to develop more powerful, fully automated auscultation applications.

MOUCHAWAR, G., ET AL.: ICD Waveform Optimization: A Randomized, Prospective, Pair-Sampled Mul ticenter Study. The theoretical tissue model-based estimates of phase 1 and phase 2 duration of biphasic waveforms are considerably shorter than the pulse widths currently used in ICDs with standard tilt. This study used a tissue resistance/capacitance (RC) model to identify optimal biphasic pulse widths. By paired step-down defibrillation threshold (DFT) testing, the efficacy of standard versus "tuned" biphasic waveforms was evaluated in 91 patients. Standard waveforms consisted of a phase 1 set to 65% tilt and phase 2 = phase 1. The tuned waveform was based on an RC model of membrane characteristics with a time constant of 3.5 ms. The optimal phase 1 truncation point is at the peak of membrane response. The optimal phase 2 duration ends with a membrane response near or just below 0. In paired analysis, no sig nificant differences were found in DFT or impedance between standard and tuned waveforms. In patients with DFTs > 400 V, the tuned waveform lowered the DFT by an average of 38 V (P < 0.05). Multivariate analyses showed a significant inverse relationship between DFT and impedance (P < 0.001). As impedance increased, the tuned waveform was associated with DFTs comparable to the standard wave form with shorter pulse duration and lower delivered energy. No single tilt value allowing an easy calcu lation of delivered energy was related to ICD waveform efficacy. The use of ICDs with tuned optimal pulse durations offer a greater flexibility of choice for patients with high DFTs. (PACE 2000; 23:[Pt. II]:1992-1995

This paper describes novel fully automated techniques for analyzing large amounts of cardiovascular data. In contrast to traditional medical expert systems our techniques incorporate no a priori knowledge about disease states. This facilitates the discovery of unexpected events. We start by transforming continuous waveform signals into symbolic strings derived directly from the data. Morphological features are used to partition heart beats into clusters by maximizing the dynamic time-warped sequence-aligned separation of clusters. Each cluster is assigned a symbol, and the original signal is replaced by the corresponding sequence of symbols. The symbolization process allows us to shift from the analysis of raw signals to the analysis of sequences of symbols. This discrete representation reduces the amount of data by several orders of magnitude, making the search space for discovering interesting activity more manageable. We describe techniques that operate in this symbolic domain to discover rhythms, transient patterns, abnormal changes in entropy, and clinically significant relationships among multiple streams of physiological data. We tested our techniques on cardiologist-annotated ECG data from forty-eight patients. Our process for labeling heart beats produced results that were consistent with the cardiologist supplied labels 98.6% of the time, and often provided relevant finer-grained distinctions. Our higher level analysis techniques proved effective at identifying clinically relevant activity not only from symbolized ECG streams, but also from multimodal data obtained by symbolizing ECG and other physiological data streams. Using no prior knowledge, our analysis techniques uncovered examples of ventricular bigeminy and trigeminy, ectopic atrial rhythms with aberrant ventricular conduction, paroxysmal atrial tachyarrhythmias, atrial fibrillation, and pulsus paradoxus.

Introduction Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD. Methods We used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2-regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set. Results We identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total-tau (t-tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration ( P < .001). On the test set, the predictive model achieved an area under the receiver operating characteristic curve of 0.85. Discussion Progression to AD, in terms of biomarker trajectories, can be predicted based on participant-specific factors. Such disease staging tools could help in targeting high-risk patients for therapeutic intervention trials. As longitudinal data with richer features are collected, such models will help increase our understanding of the factors that drive the different trajectories of AD.

BackgroundIdentification of patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) remains a major challenge in clinical cardiology. We hypothesized that quantifying variability in electrocardiogram (ECG) morphology may improve risk stratification post‐ACS.Methods and ResultsWe developed a new metric to quantify beat‐to‐beat morphologic changes in the ECG: morphologic variability in beat space (MVB), and compared our metric to published ECG metrics (heart rate variability [HRV], deceleration capacity [DC], T‐wave alternans, heart rate turbulence, and severe autonomic failure). We tested the ability of these metrics to identify patients at high risk of cardiovascular death (CVD) using 1082 patients (1‐year CVD rate, 4.5%) from the MERLIN‐TIMI 36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non‐ST‐Elevation Acute Coronary Syndrome—Thrombolysis in Myocardial Infarction 36) clinical trial. DC, HRV/low frequency–high frequency, and MVB were all associated with CVD (hazard ratios [HRs] from 2.1 to 2.3 [P<0.05 for all] after adjusting for the TIMI risk score [TRS], left ventricular ejection fraction [LVEF], and B‐type natriuretic peptide [BNP]). In a cohort with low‐to‐moderate TRS (N=864; 1‐year CVD rate, 2.7%), only MVB was significantly associated with CVD (HR, 3.0; P=0.01, after adjusting for LVEF and BNP).ConclusionsECG morphological variability in beat space contains prognostic information complementary to the clinical variables, LVEF and BNP, in patients with low‐to‐moderate TRS. ECG metrics could help to risk stratify patients who might not otherwise be considered at high risk of CVD post‐ACS.

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