Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022.

Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B.

Comparative associations of oximetry patterns in Obstructive Sleep Apnea with incident cardiovascular disease.

Study Objectives: Intermittent hypoxia is a key mechanism linking Obstructive Sleep Apnea (OSA) to cardiovascular disease (CVD). Oximetry analysis could enhance understanding of which OSA phenotypes are associated with CVD risk. The aim of this study was to compare associations of different oximetry patterns with incident CVD in men and women with OSA.

Age, sex and race bias in automated arrhythmia detectors.

Despite the recent explosion of machine learning applied to medical data, very few studies have examined algorithmic bias in any meaningful manner, comparing across algorithms, databases, and assessment metrics. In this study, we compared the biases in sex, age, and race of 56 algorithms on over 130,000 electrocardiograms (ECGs) using several metrics and propose a machine learning model design to reduce bias. Participants of the 2021 PhysioNet Challenge designed and implemented working, open-source algorithms to identify clinical diagnosis from 2- lead ECG recordings.

Issues in the automated classification of multilead ecgs using heterogeneous labels and populations.

The standard twelve-lead electrocardiogram (ECG) is a widely used tool for monitoring cardiac function and diagnosing cardiac disorders. The development of smaller, lower-cost, and easier-to-use ECG devices may improve access to cardiac care in lower-resource environments, but the diagnostic potential of these devices is unclear. This work explores these issues through a public competition: the 2021 PhysioNet Challenge.

Predicting Cardiovascular Outcomes Using the Respiratory Event Desaturation Transient Area Derived from Overnight Sleep Studies.

A new method for calculation of an overnight oximetry signal metric which is predictive of cardiovascular disease (CVD) outcomes in individuals undergoing an overnight sleep test is presented. The metric - the respiratory event desaturation transient area (REDTA) - quantifies the desaturation associated with respiratory events. Data from the Sleep Heart Health Study, which includes overnight oximetry signals and long-term CVD outcomes, was used to develop and test the parameter.

Automated Annotation of Polysomnogram Epochs for Apnoea and Non-apnoea Arousals.

A system for automated annotation of selected signals from the polysomnogram (PSG) for the presence of apnoea and non-apnoea arousals is presented. Fifty nine time- and frequency-domain features were derived from the PSG for each 15 second epoch and after combining features from adjacent epochs, the feature information was processed with a bank of feed-forward neural networks that provided a probability estimate that each epoch was associated with an apnoea or non-apnoea arousal, or no-arousal. Data from the Physionet Computing in Cardiology Challenge 2018 was used to develop and test the system.

Is Cumulative Time of Oxygen Desaturation a Better Predictor of Cardiovascular Mortality than Apnoea Hypopnoea Index?

In this paper, we explored the link between sleep apnoea and cardiovascular disease (CVD) using a time-series statistical measure of sleep apnoea-related oxygen desaturation. We compared the performance of a hypoxic measure derived from the polysomnogram with the Apnoea Hypopnoea Index (AHI) in predicting CVD mortality in patients of the Sleep Heart Health Study.We estimated the relative cumulative time of SpO below 90% (T90) using pulse oximetry signals from polysomnogram recordings as the hypoxic measure of desaturation patterns.

An Investigation of the Relationship between the Apnoea Hypopnoea Index and Apnoea Hypopnoea Duration per Hour.

A number of automated apnoea hypopnoea index (AHI) prediction algorithms first divide the signal(s) of interest into epochs, make a prediction for each epoch, determine the number of epoch predictions per hour and map this to an AHI value. An underlying assumption of this approach is a smooth relationship between the apnoea plus hypopnoea duration and the AHI value. In this study we investigate this relationship to establish if this assumption impacts the final system.

Comparing Different Methods of Hand-crafted HRV, EDR and CPC Features for Sleep Apnoea Detection.

In this paper, we extracted hand crafted features from the ECG signals and evaluated the performance of different combination of features for sleep apnoea detection. We calculated the ECG derived respiratory (EDR) signal using three methods (QRS area, amplitude demodulation and fast PCA methods) and then calculated the cardiopulmonary coupling (CPC) spectrum using each EDR method. We then extracted features from the CPC spectrums and the time and frequency representations of the heart rate variability (HRV) and EDR signals Then, we compared the performance results of different combinations of the features used for automated sleep apnoea detection.

Non-invasive Diagnosis of Sleep Apnoea Using ECG and Respiratory Bands.

In this paper, we used ECG signals and repiratory inductance plethysmography (RIP) or respiratory bands. We evaluated the performance of the signals individually as well as different combinations of features and signals for sleep apnoea detection. We implemented two methods (QRS area, and fast principal component analysis (PCA) methods) for estimating the ECG derived respiratory (EDR) signal and the cardiopulmonary coupling (CPC) spectrum.

Automatic scoring of non-apnoea arousals using hand-crafted features from the polysomnogram.

Objective: We present a system for automated annotation of non-apnoea arousals using twelve signals from the polysomnogram (PSG) including airflow, six signals of electroencephalogram, the electrooculogram, chin electromyogram, oximetry signal, and chest and abdominal respiratory effort signals.

Approach: Fifty-nine time- and frequency-domain features were extracted from the twelve signals using 15 s epochs. Features from an epoch were combined with features from adjacent epochs and then processed with a bank of feed-forward networks that provided a probability estimate of the occurrence of a non-apnoea arousal event in every epoch.

Optimising the Apnoea Classification Performance of a Neural Network Classifier Processing ECG-Oximetry Signals.

In this paper we investigate using principal components analysis to optimize the performance of a neural network system processing simultaneously acquired electrocardiogram (ECG) and oximetry signals. The algorithm identifies epochs of normal breathing, central apnoea (CA), and obstructive apnoea (OA) by processing a pooled feature set containing information capturing the desaturations from the oximeter sensor as well as time and spectral features from the ECG. Training and testing of the system was facilitated with a dataset of 125 scored polysomnogram recordings with accompanying respiratory event annotations.

A Fast Principal Component Analysis Method For Calculating The ECG Derived Respiration.

In this paper, we present a principal component analysis (PCA) method for estimating the respiration from overnight ECG recording. In comparison to other published methods, our method is very fast to compute and has low memory requirements, which makes it suitable for processing long duration ECG recordings. We used our method to derive respiratory features for the ECG which were then used to identify epochs of sleep apnoea from the ECG.

A low-complexity algorithm for detection of atrial fibrillation using an ECG.

Objectives: We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals.

Sleep apnoea diagnosis using respiratory effort-based signals - a comparative study.

A measure of the respiratory effort during a sleep study is an important contributor to the diagnosis of sleep apnoea. A common way of measuring respiratory effort is with bands with stretch sensors placed around the chest and/or abdomen. An alternative, and more convenient method from the patient's perspective, is via the ECG derived respiration (EDR) signal which provides an estimate of the respiratory effort at each heartbeat.

A fast approximation method for principal component analysis applied to ECG derived respiration for OSA detection.

In this paper, we present an approximation method for principal component analysis (PCA) and apply it to estimating the respiration from the overnight ECG signal. The approximation method is computationally fast with low memory requirements. We compare it to a full PCA method which is applied to segments of the ECG.

Sleep apnoea classification using heart rate variability, ECG derived respiration and cardiopulmonary coupling parameters.

We investigated using heart rate variability (HRV), ECG derived respiration and cardiopulmonary coupling features (CPC) calculated from night-time single lead ECG signals to classify one-minute epochs for the presence or absence of sleep apnoea. We used the 35 training recordings of the M.I.

Reducing false arrhythmia alarms in the ICU using multimodal signals and robust QRS detection.

This study developed algorithms to decrease the arrhythmia false alarms in the ICU by processing multimodal signals of photoplethysmography (PPG), arterial blood pressure (ABP), and two ECG signals. The goal was to detect the five critical arrhythmias comprising asystole (ASY), extreme bradycardia (EBR), extreme tachycardia (ETC), ventricular tachycardia (VTA), and ventricular flutter or fibrillation (VFB). The different characteristics of the arrhythmias suggested the application of individual signal processing for each alarm and the combination of the algorithms to enhance false alarm detection.

Sleep apnoea episodes recognition by a committee of ELM classifiers from ECG signal.

This paper describes a system for the recognition of sleep apnoea episodes from ECG signals using a committee of extreme learning machine (ELM) classifiers. RR-interval parameters (heart rate variability) have been used as the identifying features as they are directly affected by sleep apnoea. The MIT PhysioNet Apnea-ECG database was used.

An ECG oximetry system for identifying obstructive and central apnoea events.

An automatic algorithm for processing simultaneously acquired electrocardiogram (ECG) and oximetry signals that identifies epochs of pure central apnoea, epochs containing obstructive apnoea and epochs of normal breathing is presented. The algorithm uses time and spectral features from the ECG derived heart-rate and respiration information, as well as features capturing desaturations from the oximeter sensor. Evaluation of performance of the system was achieved by using leave-one-record-out cross validation on the St.