ECG Waveform Research Articles

Vital sign monitoring during out-of-hospital pediatric advanced airway management.

ObjectiveTo evaluate physiologic monitoring in pediatric patients undergoing out‐of‐hospital advanced airway management.MethodsRetrospective case series of pediatric patients (<18 years) with advanced airways placed in the out‐of‐hospital setting. Patients given cardiopulmonary resuscitation (CPR) or defibrillation before the first advanced airway attempt were excluded. Reviewers abstracted physiologic data from the patient monitor files and patient care reports. The primary outcome was the proportion of time pulse oximetry was in place during airway management. Other outcomes included the proportion of time ECG monitoring and waveform end‐tidal capnography were in place as well as the incidence of oxygen desaturation events.ResultsWe evaluated 23 pediatric patients with a mean age of 10.7 years (SD 6.5). Eleven of 18 (61%) children with medication‐facilitated intubation had pulse oximetry in place when the first medication was documented as given. Eight of 18 (44%) had ECG monitoring, 12 of 18 (66%) had waveform capnography, and 5 of 18 (28%) had a blood pressure check within the 3 minutes before receiving the first medication. In the 3‐minute preoxygenation phase, pulse oximetry was in place for an average of 1.4 minutes (47%, SD 0.37) and a visible photoplethysmogram (PPG) waveform obtained from the pulse oximeter was present for 0.6 minutes (20%, SD 0.34). During airway device placement, pulse oximetry was in place 73% (SD 0.39) of the time and 30% (SD 0.41) of the time there was a visible PPG waveform.ConclusionsPediatric patients had critical deficits in physiologic monitoring during advanced airway management.

Fully Analog ECG Baseline Wander Tracking and Removal Circuitry using HPF Based R-peak Detection and Quadratic Interpolation

This work presents a fully analog baseline wander tracking and removal circuitry using high-pass filter (HPF) based R-peak detection and quadratic interpolation that does not require digital post processing, thus suitable for compact and low power long-term ECG monitoring devices. The proposed method can effectively track and remove baseline wander in ECG waveforms corrupted by various motion artifacts, whereas minimizing the loss of essential features including the QRS-Complex. The key component for tracking the baseline wander is down sampling the moving average of the corrupted ECG waveform followed by quadratic interpolation, where the R-peak samples that distort the baseline tracking are excluded from the moving average by using a HPF based approach. The proposed circuit is designed using CMOS 0.18-μm technology (1.8V supply) with power consumption of 19.1 μW and estimated area of 15.5 mm² using a 4^th order HPF and quadratic interpolation. Results show SNR improvement of 10 dB after removing the baseline wander from the corrupted ECG waveform.

Development of a Selective Aortic Arch Perfusion System in a Porcine Model of Exsanguination Cardiac Arrest.

Hemorrhage constitutes the majority of potentially preventable deaths from trauma. There is growing interest in endovascular resuscitation techniques such as selective aortic arch perfusion (SAAP) for patients in cardiac arrest. This involves active perfusion of the coronary circulation via a thoracic aortic balloon catheter and is approaching clinical application. However, the technique is complex and requires refinement in animal models before human use can be considered. This paper describes a large animal model of exsanguination cardiac arrest treated with a bespoke SAAP system. Swine were anesthetized, instrumented and a splenectomy was performed before a controlled, logarithmic exsanguination was initiated. Animals were heparinized and the shed blood collected in a reservoir. Once cardiac arrest was observed, the blood was pumped through an extra-corporeal circuit into an oxygenator and then delivered through a 10 Fr balloon catheter placed in the thoracic aorta. This resulted in the return of a spontaneous circulation (ROSC) as demonstrated by ECG and aortic root pressure waveform. This model and accompanying SAAP system allow for standardized and reproducible recovery from exsanguination cardiac arrest.

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram.

The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

Virtual Instrument Based Real Time ECG Monitoring Device

This study presents a real-time ECG monitoring system based on a virtual instrument. The device was designed using surface electrode, lead wire, instrumentation amplifier (IN114), and passive low pass filter with cut-off frequency (fc, 180 Hz). Thereafter, the ECG signal was transferred via DAQ card by initializing the analog input and sampling rate to the Labview for further analysis. Further, digital notch filter (fc, 47 to 53 Hz), bandpass filter (fc, 0.05 to 20 Hz), and FIR high pass filter using Kaiser window (order-56, and fc – 3.5 Hz) was employed in order to remove the power line interference, detect fiducial point from ECG, and eliminate the baseline wondering. In addition, we examined the various wavelet to choose the best to use wavelet denoise based on signal-to-noise ratio (SNR). Finding suggests the SNR (58.75 dB) of sym8 wavelet was higher comparing with another wavelet. Hence, the wavelet denoising was implemented into the developed device to remove the distortion and to detect the better peak in real time. Further, multiresolution analysis with Haar wavelet with the decomposing level of 1 was incorporated into the developed ECG monitoring device to detect the R-R peak, followed by automatic heart rate detection. Thus, this finding suggests the promising result that has the potential to assess the cardiovascular conditions. In future, the developed device will be tested with healthy subjects in order to standardize the functionality and significant features will be extracted from the morphology of ECG waveform for the analysis of cardiovascular diseases.

Automated detection of myocardial infarction in ECG using modified Stockwell transform and phase distribution pattern from time-frequency analysis

Myocardial infarction (MI), usually referred as heart attack, takes place when blood circulation stops to specific portion of the heart resulting permanent damage to the heart muscles. It is an important task to identify the occurrence of MI from the ECG recordings efficiently. Most of the detection procedures include advanced signal processing methods, more ECG features and composite classifiers, making the overall procedure complex. This paper aims at automated identification of MI using modified Stockwell transform (MST) based time-frequency analysis and a phase information distribution pattern method. The morphological, pathological and temporal alterations in ECG waveforms resulting from the onset of MI are noticed in the phase distribution pattern of the ECG signal. Two discriminating features, utterly reflecting these alterations, are recognized for 12 leads of the MI affected ECG signal. Prior informations regarding the pathological characteristics of the specific disease are required for the correct detection of MI using few numbers of ECG leads. Thus, in this paper 12 lead ECG signals have been considered for identification of MI. The two-class classification problem with MI class and healthy individual class is performed using the threshold based classification regulation. Both healthy control and MI affected ECG signals are collected from the PTB diagnostic ECG database. The accuracy, sensitivity and specificity are found to be 99.93%, 99.97% and 99.30% for detection of MI. The proposed method has got the superiority in terms of simplicity of features, small feature dimension and simpler classification rule ensuring faster, accurate and easier MI detection.

The Development of Heart Rate Detection Using Arduino Microcontroller and Android

Nowadays heart attack is becoming a very deadly disease of human. This attack cannot be predicted, but it can be known from the human heartbeat which is a very vital health parameter of the human cardiovascular system. Heart rate reflects the health conditions of the human cardiovascular system that determined by such as stress at work, before or after sports and the psychology factor. Unfortunately, some people do not know their heart rate before or after doing an activity. This paper proposes of measuring technique of heart rate by using pulse sensor, Arduino microcontroller, and Android Smartphone. It is based on the principle of measuring the variation of blood volume in our body using a light source and detector. We also measure the heart rate by using the ECG or EKG (electrocardiogram) waveform as the comparison result of the pulse sensor with ECG waveform. The sensor consists of an infrared light-emitting-diode (LED) and a photodiode. The LED transmits an infrared light into the fingertip which is reflected back from the blood inside of finger arteries. The results show that this tool can detect the value of the heart rate and is displayed on the screen of an Android. The sensor takes under 10 seconds to detect the value of the heart rate. Notification via SMS will be sent the heart rate such as under normal conditions (BPM, Beat Per Minute <50, normal (BPM = 60 to 100) or above normal (BPM> 100).

Processing of ECG Waveforms to Increase the Diagnostic Accuracy of the ECG Stress Test for Transient Myocardial Ischemia

An approach to increasing the diagnostic accuracy of the ECG stress test for transient myocardial ischemia is considered. It is proposed to modify the decision rule by including an additional procedure for assessing the temporal dynamics of the equivalent cardiac generator in the ST-T segment. The results of evaluating the sensitivity and specificity of diagnosis in a group of subjects are presented.

Heart Irregularities Detection Based ECG Signals

ECG waveform plays vital role in providing the most information of the heart activity. This paper presentsthe design and implementation of an expert system for diagnosis of the heart abnormal activity. The proposedsystem consists of three parts; the first part includes the ECG waveform picking up and conditioning circuit.The second part performs waveform configuration, while the third part represents the core of the proposeddiagnosis system which contains the algorithm that compares the acquired waveform with standard one. Anydeviation in duration or amplitude from the standard will be detected by this algorithm and indicate heartabnormality. Experimental test has been performed for different waveforms and show promising resultstoward precise diagnosis of heart irregular activities.

An Efficient Non-Invasive Method for Fetal Ecg Extraction from Abdominal Signals

The principle target of this paper is to decide the over the top attributes of a baby during pregnancy by examining a fetal ECG waveform. In obtrusive system of FECG estimation, terminals are embedded inside the body this may cause the burst of films, which is perilous to both the fetal's and mother's lives. It is important to go for non-invasive strategy, right now readings are taken from the mid-region of pregnant ladies which is protected procedure for both mother and fetal. The fetal ECG waveform can be separated by smothering maternal ECG sign and clamor defilements present in the ECG input signal. By breaking down the fetal ECG waveform we can decide the irregularity of baby heart by estimating the fetal pulse and contrasting it and maternal pulse .The variation from the norm found in fetal during pregnancy can be valuable to treat the hatchling against heart related illnesses.

Design and Implementation of Vlsi Architecture for Arrhythmia Detection

Arrhythmia is one in all the foremost well-liked heart diseases that might result in serious consequences. In case of arrhythmia, the heart rate may be either too fast or slow. When a person suffers from arrhythmic the heart may not pump sufficient blood to all body parts that is necessary for circulation. some of the symptoms of arrhythmia includes faintness ,fluttering your chest, a light headedness or dizziness, fainting or near fainting and on the worst it may turn out to be deadly causing ventricular fibrillation. Due to this it is very crutial to detect conditions of arrhythmia. It is very difficult to identify the symptoms of arrhythmia from a long ECG record. This projects presents a VLSI based design of high speed and minimum area for arrhythmia detection .It uses arithmetic distribution discrete wavelet transform for arrhythmia detection of QRS wave and is implemented using CADENCE. The purpose of distributive arithmetic discrete wavelet change is to compress the ECG signal. ECG signals are generated via MATLAB. The resultant of these coefficients are given to the LUT, which comprises of MIT-BIH databases. Our aim is to detect the QRS complex in the ECG signal and to identify the time and frequency variations. By comparing these variations with that of the reference variations produced in the normal ECG waveform it is easy to identify whether the patient is suffering from arrhythmia or not. The coding was written in verilog and stimulated in modelsim software and implemented using CADENCE tool.

PPG Sensor Contact Pressure Should Be Taken Into Account for Cuff-Less Blood Pressure Measurement.

Photo-plethysmography (PPG) sensors are often used to detect pulse transit time (PTT) for potential cuff-less blood pressure (BP) measurement. It is known that the contact pressure (CP) of the PPG sensor markedly alters the PPG waveform amplitude. The objective was to test the hypothesis that PTT detected via PPG sensors is likewise impacted by CP. A device was built to measure the time delay between ECG and finger PPG waveforms (i.e., pulse arrival time (PAT) - a popular surrogate of PTT) and the PPG sensor CP at different CP levels. These measurements and finger cuff BP were recorded while the CP was slowly varied in 17 healthy subjects. Over a physiologic range of CP, the maximum deviations of PAT detected at the PPG foot and peak were 22±2 and 40±7 ms (p<0.05), which translate to ∼11 and ∼20mmHg BP error based on the literature. The curve relating PAT detected at the PPG foot to CP was U-shaped with minimum near finger diastolic BP. A conceptual model accounting for finger artery viscoelasticity and nonlinearity explained this curve. Since the regulatory bias error for BP measurement is limited to 5mmHg, PPG sensor CP should be taken into account for cuff-less BP measurement via PTT. This study suggests that widely pursued PPG-based BP measurement devices including those that detect PTT should maintain the CP or include a CP measurement in the calibration equation for deriving BP.

Establishment of Application Database for Electrocardiograph in Different Scenes

It is significant to establish scene ECG database which improves the automatic diagnostic function of electrocardiograph under different application scenarios. We built the ECG database in different scene according to the hospital level (grade 3, grade 2, grade 1) and clinical environment (intensive care and acute wards, outpatient clinics and general wards). Sample size was obtained according to the incidence of various ECG diagnoses. The database covers ECG signal, ECG waveform, ECG characteristic values, ECG diagnostic results by experts and clinical information of patients etc. It not only provides important reference for electrocardiograph manufacturers to evaluate and test the parameters of automatic diagnosis under different clinical scene, but also provides valuable scientific research and teaching resources for medical workers.

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions.

Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

LabVIEW Based Determination of Oxygen Saturation from ECG Signals

This research paper demonstrates the measurement of oxygen saturation (SAO2) from the ECG signal using LabVIEW 11.0 .SAO2 is the percentage of hemoglobin molecules that contain oxygen. The optimum level of SAO2 in a human being should be 92%.When it goes below 90% it causes hypoxemia (anemia). When SAO2 reduces below 65%, it will cause damage in the functioning of mental activity .When it reduces below 55% it causes unconsciousness. Hence sustaining the optimum value of SAO2 is crucial. Hence maintaining optimum level of SAO2 is important. The latest technology which utilizes pulse oximeter has its own drawbacks. To overcome these disadvantages, a new methodology by which SAO2 can be measured from ECG signals is proposed. The ECG signal from the patient is acquired by NI USB 6211 DAQ module. By determining the pulse numbers from the ECG waveform, the SAO2 level can be determined by suitable mathematical computations. Diagnostics and implementing control action can be done with the support of LabVIEW software.

Analysis of Heart Rate Variability in a Patient with Takotsubo Cardiomyopathy Syndrome on the Actual Onset Day

A healthy 73-year-old woman unpredictably developed Takotsubo cardiomyopathy syndrome (TTS) during Holter-electrocardiogram (ECG) recording. Thus, the complete chronological ECG data on the actual onset day of TTS were obtained. Many heart rate variability (HRV) parameters, including the low-frequency components (LF) and the high-frequency components (HF), on the actual onset day and in the healing phase were calculated. The interesting facts on the actual onset day were that the suppression of both LF and HF appeared earlier than the changes of the ECG waveform; and the LF/HF ratio remained within the normal range, although both LF and HF were markedly suppressed. The abnormality on the actual onset day was clear compared with the healing phase. It is noteworthy to obtain the chronological ECG data on the actual onset day of TTS in a healthy patient. The present data are unique in terms of being analyzed on the actual onset day. Although the HRV parameters, including LF and HF, were obviously abnormal, there is some skepticism about using HRV parameters as indices of cardiac autonomic activity. In the present case, it was concluded that the abnormality of cardiac autonomic activity contributed to the onset of TTS. These data are unlikely to ever be replicated, and we hope that this report helps elucidate the TTS mechanism.

Abstract 27: Machine Learning Models for Outcome Prediction of Out-Of-Hospital Cardiac Arrest of Presumed Cardiac Cause Using the All-Japan Utstein Registry

Background: Outcome prediction for patients with out-of-hospital cardiac arrest (OHCA) using prehospital information has been one of the major challenges in resuscitation medicine. Recently, machine learning techniques have been shown to be highly effective in predicting outcomes using clinical registries. In this study, we aimed to establish a prediction model for outcomes of OHCA of presumed cardiac cause using machine learning techniques. Methods: We analyzed data from the All-Japan Utstein Registry of the Fire and Disaster Management Agency between 2005 and 2016. Of 1,423,338 cases, data of OHCA patients aged ≥18 years with presumed cardiac etiology were retrieved and divided into two groups: training set, n = 584,748 (between 2005 and 2013) and test set, n = 223,314 (between 2014 and 2016). The endpoints were neurologic outcome at 1-month and survival at 1-month. Of 47 variables evaluated during the prehospital course, 19 variables (e.g.,sex, age, ECG waveform, and practice of bystander CPR) were used for outcome prediction. Performances of logistic regression, random forests, and deep neural network were examined in this study. Results: For prediction of neurologic outcomes (cerebral performance category 1 or 2) using the test set, the generated models showed area under the receiver operating characteristic curve (AUROC) values of 0.942 (95% confidence interval [CI] 0.941-0.943), 0.947 (95% CI 0.946-0.948), and 0.948 (95% CI 0.948-0.950) in logistic regression, random forest, and deep neural network, respectively. For survival prediction, the generated models showed AUROC values of 0.901 (95% CI 0.900-0.902), 0.913 (95% CI 0.912-0.914), and 0.912 (95% CI 0.911-0.913) in logistic regression, random forest, and deep neural network, respectively. Conclusions: Machine learning techniques using prehospital variables showed favorable prediction capability for 1-month neurologic outcome and survival in OHCA of presumed cardiac cause.

Abstract 125: Automated External Defibrillator Shock Advisement Performance Among Multiple Human Electrocardiographic Rhythms and Devices

Background: Automated external defibrillators (AEDs) perform rhythm analysis in order to facilitate defibrillation. The effectiveness of AEDs is dependent on the accuracy of their rhythm classification, which includes differentiation of shockable rhythms from non-shockable rhythms Independent (i.e. non-industry) evaluation of the performance of AEDs against real-world ECG could lead to improvements in their performance. Objective: To evaluate the sensitivity and specificity characteristics of commercial AEDs with respect to quantitative properties of the ECG waveform in several rhythm presentations using real world ECG data. Methods: We conducted a prospective simulation study evaluating three commercially available AEDs from Defibtech, Phillips, and Zoll on the determination of ECG rhythm shockability. Performance was evaluated for 181 human ECG recordings (101 ventricular fibrillation-VF, 55 PEA, and 25 asystole) ranging widely in signal characteristics, obtained from the Pittsburgh site of the Resuscitation Outcomes Consortium. We used a commercially available digital-to-analog converter (National Instruments USB-6001) to inject the recordings into each AED through a direct lead-wire interface, recording shock advisement decisions in a best-out-of-three approach for each device/rhythm pairing. We calculated the sensitivity and specificity for discriminating VF and non-VF rhythms for each device and overall. VF signal characteristics were calculated, including peak frequency, median amplitude, and peak amplitude, and the VF quantitative waveform measures AMSA and median slope. Results: The 101 VF trials featured signals with mean peak frequency 10.02 Hz(IQR 4.80 Hz), mean AMSA 9.13(IQR 7.29), mean median slope 6.72 (IQR 3.66). The sensitivities were: Defibtech 99.0%; Philips 97.0%; Zoll 98.0%. The specificities were: Defibtech 98.7%; Philips 96.2%; Zoll 97.4%. Defibtech recorded 5 discordant advisements and Philips and Zoll recorded eight each. The overall sensitivity was 98.0%, and the specificity 97.4%. Conclusion: Evaluated against a wide variety of real-world signal presentations, commercial AEDs demonstrated a high degree of sensitivity and specificity for shockable ECG rhythms.

Phase Space Reconstruction Based CVD Classifier Using Localized Features

This paper proposes a generalized Phase Space Reconstruction (PSR) based Cardiovascular Diseases (CVD) classification methodology by exploiting the localized features of the ECG. The proposed methodology first extracts the ECG localized features including PR interval, QRS complex, and QT interval from the continuous ECG waveform using features extraction logic, then the PSR technique is applied to get the phase portraits of all the localized features. Based on the cleanliness and contour of the phase portraits CVD classification will be done. This is first of its kind approach where the localized features of ECG are being taken into considerations unlike the state-of-art approaches, where the entire ECG beats have been considered. The proposed methodology is generic and can be extended to most of the CVD cases. It is verified on the PTBDB and IAFDB databases by taking the CVD including Atrial Fibrillation, Myocardial Infarction, Bundle Branch Block, Cardiomyopathy, Dysrhythmia, and Hypertrophy. The methodology has been tested on 65 patients’ data for the classification of abnormalities in PR interval, QRS complex, and QT interval. Based on the obtained statistical results, to detect the abnormality in PR interval, QRS complex and QT interval the Coefficient Variation (CV) should be greater than or equal to 0.1012, 0.083, 0.082 respectively with individual accuracy levels of 95.3%, 96.9%, and 98.5% respectively. To justify the clinical significance of the proposed methodology, the Confidence Interval (CI), the p-value using ANOVA have been computed. The p-value obtained is less than 0.05, and greater F-statistic values reveal the robust classification of CVD using localized features.

Cardiovascular autonomic responses in patients with Parkinson disease to pedunculopontine deep brain stimulation.

Dysautonomia can be a debilitating feature of Parkinson disease (PD). Pedunculopontine nucleus (PPN) stimulation may improve gait disorders in PD, and may also result in changes in autonomic performance. To determine whether pedunculopontine nucleus stimulation improves cardiovascular responses to autonomic challenges of postural tilt and Valsalva manoeuver, eight patients with pedunculopontine nucleus deep brain stimulation were recruited to the study; two were excluded for technical reasons during testing. Participants underwent head up tilt and Valsalva manoeuver with stimulation turned ON and OFF. Continuous blood pressure and ECG waveforms were recorded during these tests. In a single patient, local field potential activity was recorded from the implanted electrode during tilt. The fall in systolic blood pressure after tilt was significantly smaller with stimulation ON (mean - 8.3% versus - 17.2%, p = 0.044). Valsalva ratio increased with stimulation from median 1.15 OFF to 1.20 ON (p = 0.028). Baroreflex sensitivity increased during Valsalva compared to rest with stimulation ON versus OFF (p = 0.028). The increase in baroreflex sensitivity correlated significantly with the mean depth of PPN stimulating electrode contacts. This accounted for 89% of its variance (r = 0.943, p = 0.005). PPN stimulation can modulate the cardiovascular system in patients with PD. In this study, it reduced the postural fall in systolic blood pressure during head-up tilt and improved the cardiovascular response during Valsalva, presumably by altering the neural control of baroreflex activation.