MIMIC-IV, a freely accessible electronic health record dataset.

Digital data collection during routine clinical practice is now ubiquitous within hospitals. The data contains valuable information on the care of patients and their response to treatments, offering exciting opportunities for research. Typically, data are stored within archival systems that are not intended to support research.

A contrastive learning approach for ICU false arrhythmia alarm reduction.

The high rate of false arrhythmia alarms in Intensive Care Units (ICUs) can lead to disruption of care, negatively impacting patients' health through noise disturbances, and slow staff response time due to alarm fatigue. Prior false-alarm reduction approaches are often rule-based and require hand-crafted features from physiological waveforms as inputs to machine learning classifiers. Despite considerable prior efforts to address the problem, false alarms are a continuing problem in the ICUs.

Post-extrasystolic characteristics in the arterial blood pressure waveform are associated with right ventricular dysfunction in intensive care patients.

Right ventricular dysfunction (RVD) is associated with end-organ dysfunction and mortality, but has been an overlooked condition in the ICU. We hypothesized that analysis of the arterial waveform in the presence of ventricular extrasystoles could differentiate patients with RVD from patients with a normally functioning right ventricle, because the 2nd and 3rd post-ectopic beat could reflect right ventricular state (pulmonary transit time) during the preceding ectopy. We retrospectively identified patients with echocardiographic evidence of moderate-to-severe RVD and patients with a normal functioning right ventricle (control) from the MIMIC database.

You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018.

The PhysioNet/Computing in Cardiology Challenge 2018 focused on the use of various physiological signals (EEG, EOG, EMG, ECG, SaO) collected during polysomnographic sleep studies to detect sources of arousal (non-apnea) during sleep. A total of 1,983 polysomnographic recordings were made available to the entrants. The arousal labels for 994 of the recordings were made available in a public training set while 989 labels were retained in a hidden test set.

AF Classification from a Short Single Lead ECG Recording: the PhysioNet/Computing in Cardiology Challenge 2017.

The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels.

Recent advances in heart sound analysis.

Heart sounds have been widely studied and have been demonstrated to have value for detecting pathologies in clinical applications. Over the last few decades, the use of heart sound signals has become increasingly uncommon and its practice in modern medicine somewhat diminished, although research into automated analysis has continued. Unfortunately, a 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.

An open access database for the evaluation of heart sound algorithms.

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.

False alarm reduction in critical care.

High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm.

The PhysioNet/Computing in Cardiology Challenge 2015: Reducing False Arrhythmia Alarms in the ICU.

High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 Physio-Net/Computing in Cardiology Challenge provides a set of 1,250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm.

MIMIC-III, a freely accessible critical care database.

MIMIC-III ('Medical Information Mart for Intensive Care') is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework.

Robust detection of heart beats in multimodal data.

This editorial reviews the background issues, the design, the key achievements, and the follow-up research generated as a result of the PhysioNet/Computing in Cardiology (CinC) Challenge 2014, published in the concurrent focus issue of Physiological Measurement. Our major focus was to accelerate the development and facilitate the comparison of robust methods for locating heart beats in long-term multi-channel recordings. A public (training) database consisting of 151 032 annotated beats was compiled from records that contained ECGs as well as pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats.

Acute lower urinary tract dysfunction (LUTD) following traumatic brain injury (TBI) in rats.

Aims: The aim of this study was to assess experimental traumatic brain injury (TBI)-induced lower urinary tract dysfunction (LUTD) by monitoring systemic and urodynamic parameters using an implantable telemetry system.

Methods: A single lateral fluid percussion TBI (FP-TBI; 3.4 atm) was administered to 10 female rats.

Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

Objective: We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms.

Design: Data collection and retrospective analysis.

In situ monitoring of adipogenesis with human-adipose-derived stem cells using surface-enhanced Raman spectroscopy.

Methods capable of nondestructively collecting high-quality, real-time chemical information from living human stem cells are of increasing importance given the escalating relevance of stem cells in therapeutic and regenerative medicines. Raman spectroscopy is one such technique that can nondestructively collect real-time chemical information. Living cells uptake gold nanoparticles and transport these particles through an endosomal pathway.

Using surface-enhanced Raman spectroscopy to probe for genetic markers on single-stranded DNA.

Methods capable of quickly and inexpensively collecting genetic information are of increasing importance. We report a method of using surface-enhanced Raman spectroscopy to probe single-stranded DNA for genetic markers. This unique approach is used to analyze unmodified genes of moderate length for genetic markers by hybridizing native test oligonucleotides into a surface-enhanced Raman complex, vastly increasing detection sensitivity as compared to traditional Raman spectroscopy.

Enhancing Electrochemical Detection by Scaling Solid State Nanogaps.

The ability to quickly and inexpensively fabricate planar solid state nanogaps has enabled research to be effectively performed on devices down to just a few nanometers. Here, nanofabricated electrode pairs with electrode-to-electrode spacings of <4, 6 and 20 nm are utilized for monitoring an electroactive molecules, dopamine, in ionic solution. The results show a several order of magnitude enhancement of the electrochemical signal, collected current, for the solid state nanogaps with 6 nm electrode-electrode spacings as compared to traditional microelectrodes.

Specific oxygen-containing functional groups on the carbon surface underlie an enhanced sensitivity to dopamine at electrochemically pretreated carbon fiber microelectrodes.

The in vivo use of carbon-fiber microelectrodes for neurochemical investigation has proven to be selective and sensitive when coupled with background-subtracted fast-scan cyclic voltammetry (FSCV). Various electrochemical pretreatments have been established to enhance the sensitivity of these sensors; however, the fundamental chemical mechanisms underlying these enhancement strategies remain poorly understood. We have investigated an electrochemical pretreatment in which an extended triangular waveform from -0.

Simultaneous decoupled detection of dopamine and oxygen using pyrolyzed carbon microarrays and fast-scan cyclic voltammetry.

Microfabricated structures utilizing pyrolyzed photoresist have been shown to be useful for monitoring electrochemical processes. These previous studies, however, were limited to constant-potential measurements and slow-scan voltammetry. The work described in this paper utilizes microfabrication processes to produce devices that enable multiple fast-scan cyclic voltammetry (FSCV) waveforms to be applied to different electrodes on a single substrate.

Solid state nanogaps for differential measurements of molecular properties.

This paper demonstrates the production and probing of solid state nanogaps. These nanogaps can be inexpensively and controllably produced using a combination of molecular and standard photolithography. These nanogaps are implemented for chemical monitoring by using surface enhanced Raman spectroscopy to collect molecular information at the nanogap and current-voltage traces to probe the charge transport of the nanogap.

Statistically significant Raman detection of midsequence single nucleotide polymorphisms.

This report highlights methodologies that enable statistically significant data to be collected for single nucleotide polymorphisms using surface enhanced Raman spectroscopy. Single-stranded oligonucleotides functionalized with 40 nm gold nanoparticles are hybridized with oligonucleotides adsorbed to a photolithographically defined gold surface thus creating a surface enhanced Raman environment around the DNA duplex. With this design characteristic Raman spectra have been collected and explored for differences between DNA duplexes formed from complementary oligonucleotides, completely mismatched oligonucleotides, and those formed from oligonucleotides that have a midsequence single nucleotide mismatch.