Segments Of ECG Signals Research Articles

Revisiting QRS Detection Methodologies for Portable, Wearable, Battery-Operated, and Wireless ECG Systems

Cardiovascular diseases are the number one cause of death worldwide. Currently, portable battery-operated systems such as mobile phones with wireless ECG sensors have the potential to be used in continuous cardiac function assessment that can be easily integrated into daily life. These portable point-of-care diagnostic systems can therefore help unveil and treat cardiovascular diseases. The basis for ECG analysis is a robust detection of the prominent QRS complex, as well as other ECG signal characteristics. However, it is not clear from the literature which ECG analysis algorithms are suited for an implementation on a mobile device. We investigate current QRS detection algorithms based on three assessment criteria: 1) robustness to noise, 2) parameter choice, and 3) numerical efficiency, in order to target a universal fast-robust detector. Furthermore, existing QRS detection algorithms may provide an acceptable solution only on small segments of ECG signals, within a certain amplitude range, or amid particular types of arrhythmia and/or noise. These issues are discussed in the context of a comparison with the most conventional algorithms, followed by future recommendations for developing reliable QRS detection schemes suitable for implementation on battery-operated mobile devices.

Removal of Cardiopulmonary Resuscitation Artifacts with an Enhanced Adaptive Filtering Method: An Experimental Trial

Current automated external defibrillators mandate interruptions of chest compression to avoid the effect of artifacts produced by CPR for reliable rhythm analyses. But even seconds of interruption of chest compression during CPR adversely affects the rate of restoration of spontaneous circulation and survival. Numerous digital signal processing techniques have been developed to remove the artifacts or interpret the corrupted ECG with promising result, but the performance is still inadequate, especially for nonshockable rhythms. In the present study, we suppressed the CPR artifacts with an enhanced adaptive filtering method. The performance of the method was evaluated by comparing the sensitivity and specificity for shockable rhythm detection before and after filtering the CPR corrupted ECG signals. The dataset comprised 283 segments of shockable and 280 segments of nonshockable ECG signals during CPR recorded from 22 adult pigs that experienced prolonged cardiac arrest. For the unfiltered signals, the sensitivity and specificity were 99.3% and 46.8%, respectively. After filtering, a sensitivity of 93.3% and a specificity of 96.0% were achieved. This animal trial demonstrated that the enhanced adaptive filtering method could significantly improve the detection of nonshockable rhythms without compromising the ability to detect a shockable rhythm during uninterrupted CPR.

A classification scheme for ventricular arrhythmias using wavelets analysis

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7% and then the separation between OVF and DVF with an accuracy of 80.0%. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.

Intraoperative MRI electrical noise and monitor ECG filters affect arrhythmia detection and identification

Most electrical equipment in the modern operating room (OR) radiates electrical noise (EN) that can interfere with patient monitors. We have described the EN that an intraoperative magnetic resonance imaging (iMRI) system emits and have shown that this high-energy EN diminishes the quality of the ECG waveform during iMRI scans in our neurosurgical OR. We have also shown that the ECG signal filters in our iMRI-compatible patient monitor reduce this interference but, in the process, disturb the true morphology of the displayed waveform. This simulation study evaluates how iMRI-generated EN affects the ability of the anesthetist to detect and identify ECG arrhythmias and whether the patient monitor's ECG signal filters can improve arrhythmia recognition. Using an ECG simulator, we generated Lead II and V5 ECG signal segments that contained either no arrhythmia or one of four common cardiac arrhythmias. We filtered the ECG segments with four filters available on our iMRI-compatible monitor (Veris MR, MEDRAD Inc., Indianola, PA USA). We then digitized the segments and mixed simulated iMRI EN into the resultant tracings. With institutional approval and written informed consent, board-certified anesthesiologists reviewed the tracings, determined if an arrhythmia was present and identified the arrhythmia. We conducted the study anonymously. We reported the data as percent correct arrhythmia detection and correct arrhythmia identification. Thirty-one anesthesiologists completed the study. Overall, the participants correctly detected 79.5% (95% CI: 77.2, 81.7%) of the arrhythmias and correctly identified 62.5% (95% CI: 59.8, 65.3%) of the arrhythmias, regardless of EN presence. Although the proportions among monitor noise filters studied were not significant, the manufacturer-designated MR5 Veris MR filter optimized arrhythmia detection and arrhythmia identification for our participants, regardless if EN was present in the ECG tracings. In the neurosurgical OR, the anesthetist must be able to effectively monitor a patient in the presence of iMRI-generated EN. Depending on the OR design, the patient may be out of the anesthetist's direct view during a scan procedure. The anesthetist must rely on monitored physiologic parameters to assess patient status during this time. He/she should be familiar with his/her monitor's filtering capabilities and routinely adjust the ECG filters to achieve the best compromise between minimized EN effects and maximized displayed ECG signal quality.

Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals

Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5s period. Eight level wavelet packet analysis was performed on each 5s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing. The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis. The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.

Automatic segmentation of long-term ECG signals corrupted with broadband noise based on sample entropy

Biomedical signals are nonstationary in nature, namely, their statistical properties are time-dependent. Such changes in the underlying statistical properties of the signal and the effects of external noise often affect the performance and applicability of automatic signal processing methods that require stationarity. A number of methods have been proposed to address the problem of finding stationary signal segments within larger nonstationary signals. In this framework, processing and analysis are applied to each resulting locally stationary segment separately. The method proposed in this paper addresses the problem of finding locally quasi-stationary signal segments. Particularly, our proposed algorithm is designed to solve the specific problem of segmenting semiperiodic biomedical signals corrupted with broadband noise according to the various degrees of external noise power. It is based on the sample entropy and the relative sensitivity of this signal regularity metric to changes in the underlying signal properties and broadband noise levels. The assessment of the method was carried out by means of experiments on ECG signals drawn from the MIT-BIH arrhythmia database. The results were measured in terms of false alarms based on the changepoint detection bias. In summary, the results achieved were a sensitivity of 97%, and an error of 16% for records corrupted with muscle artifacts.

Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings

Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG signals (sampling rate = 250 Hz) and estimate the surrogate apnea index (AI)/hypopnea index (HI) (AHI). Total 82,535 ECG epochs (each of 5-s duration) from normal breathing during sleep, 1638 ECG epochs from 689 hypopnea events, and 3151 ECG epochs from 1862 apnea events were collected from 17 patients in the training set. Two-staged feedforward neural network model was trained using features from ECG signals with leave-one-patient-out cross-validation technique. At the first stage of classification, events (apnea and hypopnea) were classified from normal breathing events, and at the second stage, hypopneas were identified from apnea. Independent test was performed on 16 subjects' ECGs containing 483 hypopnea and 1352 apnea events. The cross-validation and independent test accuracies of apnea and hypopnea detection were found to be 94.84% and 76.82%, respectively, for training set, and 94.72% and 79.77%, respectively, for test set. The Bland-Altman plots showed unbiased estimations with standard deviations of +/- 2.19, +/- 2.16, and +/- 3.64 events/h for AI, HI, and AHI, respectively. Results indicate the possibility of recognizing apnea/hypopnea events based on shorter segments of ECG signals.