Sample entropy correlates with intraventricular hemorrhage and mortality in premature infants early in life.

Background: Mortality and intraventricular hemorrhage (IVH) are common adverse outcomes in preterm infants and are challenging to predict clinically. Sample entropy (SE), a measure of heart rate variability (HRV), has shown predictive power for sepsis and other morbidities in neonates. We evaluated associations between SE and mortality and IVH in the first week of life.

Impact of surgical complications on hospital costs and revenues: retrospective database study of Medicare claims.

To compare the length of stay, hospital costs and hospital revenues for Medicare patients with and without a subset of potentially preventable postoperative complications after major noncardiac surgery. Retrospective data analysis using the Medicare Standard Analytical Files, Limited Data Set, 5% inpatient claims files for years 2016-2020. In 74,103 claims selected for analysis, 71,467 claims had no complications and 2636 had one or more complications of interest.

Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.

Study Objective: Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis.

Improving reliability for measuring ST deviation in suspected acute coronary syndrome.

Background: Standard 12‑lead ECG is used for diagnosis and risk stratification in suspected acute coronary syndrome (ACS) patients. Artifacts have significant impact on the measuring quality, which consequently affect the diagnostic decision. We used a signal quality indicator (SQI) to identify the ECG segments with lower artifact levels which we hypothesized would improve ST measurements.

Big data reveals insights for lead importance in ECG interpretation.

Background: Not every lead contributes equally in the interpretation of an ECG. There are some abnormalities in which the lead importance is not clear either from cardiac electrophysiology or experience. Therefore, it is beneficial to develop an algorithm to quantify the lead importance in the reading of ECGs, namely to determine how much to weigh the evidence from each individual lead when interpreting ECG.

Diagnostic performance of a new ECG algorithm for reducing false positive cases in patients suspected acute coronary syndrome.

Background: Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation.

ECG reading differences demonstrated on two databases.

Unlabelled: Many studies that rely on manual ECG interpretation as a reference use multiple ECG expert interpreters and a method to resolve differences between interpreters, reflecting the fact that experts sometimes use different criteria. The aim of this study was to show the effect of manual ECG interpretation style on training automated ECG interpretation.

Methods: The effect of ECG interpretation style or differing ECG criteria on algorithm training was shown in this study by careful analysis of the changes in algorithm performance when the algorithm was trained on one database and tested on a different database.

Synthesis of standard 12‑lead electrocardiograms using two-dimensional generative adversarial networks.

This paper proposes a two-dimensional (2D) bidirectional long short-term memory generative adversarial network (GAN) to produce synthetic standard 12-lead ECGs corresponding to four types of signals-left ventricular hypertrophy (LVH), left branch bundle block (LBBB), acute myocardial infarction (ACUTMI), and Normal. It uses a fully automatic end-to-end process to generate and verify the synthetic ECGs that does not require any visual inspection. The proposed model is able to produce synthetic standard 12-lead ECG signals with success rates of 98% for LVH, 93% for LBBB, 79% for ACUTMI, and 59% for Normal.

Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.

Background: Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion.

Methods: This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US.

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS).

Detection of strict left bundle branch block by neural network and a method to test detection consistency.

Objective: To develop an automatic algorithm to detect strict left bundle branch block (LBBB) on electrocardiograms (ECG) and propose a procedure to test the consistency of neural network detections.

Approach: The database for the classification of strict LBBB was provided by Telemetric and Holter ECG Warehouse. It contained 10 s ECGs taken from the MADIT-CRT clinical trial.

12-Lead ECG interpretation by database comparison.

Background: Automated ECG interpretation is most often a rule-based expert system, though experts may disagree on the exact ECG criteria. One method to automate ECG analysis while indirectly using varied sets of expert rules is to base the automated interpretation on similar ECGs that already have a physician interpretation. The aim of this study is to develop and test an ECG interpretation algorithm based on such similar ECGs.

Cardiac arrhythmia detection using deep learning: A review.

Due to its simplicity and low cost, analyzing an electrocardiogram (ECG) is the most common technique for detecting cardiac arrhythmia. The massive amount of ECG data collected every day, in home and hospital, may preclude data review by human operators/technicians. Therefore, several methods are proposed for either fully automatic arrhythmia detection or event selection for further verification by human experts.

Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings.

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 s). For this purpose, we combined a signal quality index (SQI) algorithm, to assess noisy instances, and trained densely connected convolutional neural networks to classify ECG recordings.

The algorithmic performance of J-Tpeak for drug safety clinical trial.

Introduction: The interval from J-point to T-wave peak (JTp) in ECG is a new biomarker able to identify drugs that prolong the QT interval but have different ion channel effects. If JTp is not prolonged, the prolonged QT may be associated with multi ion channel block that may have low torsade de pointes risk. From the automatic ECG measurement perspective, accurate and repeatable measurement of JTp involves different challenges than QT.

Efficient noise-tolerant estimation of heart rate variability using single-channel photoplethysmography.

Background: The feasibility of using photoplethysmography (PPG) for estimating heart rate variability (HRV) has been the subject of many recent studies with contradicting results. Accurate measurement of cardiac cycles is more challenging in PPG than ECG due to its inherent characteristics.

Methods: We developed a PPG-only algorithm by computing a robust set of medians of the interbeat intervals between adjacent peaks, upslopes, and troughs.

Intelligent use of advanced capabilities of diagnostic ECG algorithms in a monitoring environment.

A large number of ST-elevation notifications are generated by cardiac monitoring systems, but only a fraction of them is related to the critical condition known as ST-segment elevation myocardial infarction (STEMI) in which the blockage of coronary artery causes ST-segment elevation. Confounders such as acute pericarditis and benign early repolarization create electrocardiographic patterns mimicking STEMI but usually do not benefit from a real-time notification. A STEMI screening algorithm able to recognize those confounders utilizing capabilities of diagnostic ECG algorithms in variation analysis of ST segments helps to avoid triggering a non-actionable ST-elevation notification.

Electrocardiographic diagnosis of ST segment elevation myocardial infarction: An evaluation of three automated interpretation algorithms.

Objective: To assess the validity of three different computerized electrocardiogram (ECG) interpretation algorithms in correctly identifying STEMI patients in the prehospital environment who require emergent cardiac intervention.

Methods: This retrospective study validated three diagnostic algorithms (AG) against the presence of a culprit coronary artery upon cardiac catheterization. Two patient groups were enrolled in this study: those with verified prehospital ST-elevation myocardial infarction (STEMI) activation (cases) and those with a prehospital impression of chest pain due to ACS (controls).

Identification of exercise-induced ischemia using QRS slopes.

In this work we studied a computer-aided approach using QRS slopes as unconventional ECG features to identify the exercise-induced ischemia during exercise stress testing and demonstrated that the performance is comparable to the experts' manual analysis using standard criteria involving ST-segment depression. We evaluated the performance of our algorithm using a database including 927 patients undergoing exercise stress tests and simultaneously collecting the ECG recordings and SPECT results. High resolution 12-lead ECG recordings were collected continuously throughout the rest, exercise, and recovery phases.

Automated detection of ventricular pre-excitation in pediatric 12-lead ECG.

Background: With increased interest in screening of young people for potential causes of sudden death, accurate automated detection of ventricular pre-excitation (VPE) or Wolff-Parkinson-White syndrome (WPW) in the pediatric resting ECG is important. Several recent studies have shown interobserver variability when reading screening ECGs and thus an accurate automated reading for this potential cause of sudden death is critical. We designed and tested an automated algorithm to detect pediatric VPE optimized for low prevalence.

Quantitative relationship between end-tidal carbon dioxide and CPR quality during both in-hospital and out-of-hospital cardiac arrest.

Objective: Cardiopulmonary resuscitation (CPR) guidelines recommend the administration of chest compressions (CC) at a standardized rate and depth without guidance from patient physiologic output. The relationship between CC performance and actual CPR-generated blood flow is poorly understood, limiting the ability to define "optimal" CPR delivery. End-tidal carbon dioxide (ETCO2) has been proposed as a surrogate measure of blood flow during CPR, and has been suggested as a tool to guide CPR despite a paucity of clinical data.

Agreement between cardiologists and fellows in interpretation of ischemic electrocardiographic changes in acute myocardial infarction.

Background: Time from symptom onset may not be the best indicator for choosing reperfusion therapy for patients presenting with acute ST-elevation myocardial infarction (STEMI); consequently ECG-based methods have been developed.

Methods: This study evaluated the inter-observer agreement between experienced cardiologists and junior doctors in identifying the ECG findings of the pre-infarction syndrome (PIS) and evolving myocardial infarction (EMI). The ECGs of 353 STEMI patients were independently analyzed by two cardiologists, one fellow in cardiology, one fellow in internal medicine and a medical student.

Automatic detection of ECG cable interchange by analyzing both morphology and interlead relations.

Background: ECG cable interchange can generate erroneous diagnoses. For algorithms detecting ECG cable interchange, high specificity is required to maintain a low total false positive rate because the prevalence of interchange is low. In this study, we propose and evaluate an improved algorithm for automatic detection and classification of ECG cable interchange.

Development of three methods for extracting respiration from the surface ECG: a review.

Background: Respiration rate (RR) is a critical vital sign that can be monitored to detect acute changes in patient condition (e.g., apnea) and potentially provide an early warning of impending life-threatening deterioration.