Behavioral and electrophysiological study in Colossoma macropomum treated with different concentrations of Nepeta cataria oil in an immersion bath revealed a therapeutic window for anesthesia

The purpose of this study was to characterize the activity of essential oils from Nepeta Cataria (EON) at concentrations of 125 μ L  L-1, 150 μ L  L-1, 175 μ L  L-1, and 200 μ L  L-1 on the behavior of loss of the posture reflex and recovery of the posture reflex and electrocardiographic activity and recording of the opercular beat of Colossoma macropomum during immersion bathing for a period of 5 min, in order to obtain a window for safe use during anesthesia. The fish (23.38 ± 3.5 g) were assigned to the following experiments: experiment 1 (latency to loss and recovery of the posture reflex): (a) 125 μ L  L-1, (b) 150 μ L  L-1, (c) 175 μ L  L-1, and (d) 200 μ L  L-1 (n = 9) per group. Experiment 2 (electrocardiographic and heartbeat recordings): (a) control group; (b) vehicle control group (2 ml of alcohol per liter of water), (c) 125 μ L  L-1, (d) 150 μ L  L-1, (e) 175 μ L  L-1, and (f) 200 μ L  L-1 (n = 9), per group. All the concentrations used showed efficacy in inducing loss of the posture reflex and reversibility with recovery of the posture reflex, but the electrocardiographic recordings indicated morphographic changes such as bradycardia during induction and p wave apiculation during recovery at the highest concentrations tested. In this way, we suggest a safe use window for short-term anesthesia with EON in the concentration range of 125 to 150 μ L  L-1 for juvenile Colossoma macropomum.

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

A dynamic learning-based ECG feature extraction method for myocardial infarction detection

Objective.Myocardial infarction (MI) is one of the leading causes of human mortality in all cardiovascular diseases globally. Currently, the 12-lead electrocardiogram (ECG) is widely used as a first-line diagnostic tool for MI. However, visual inspection of pathological ECG variations induced by MI remains a great challenge for cardiologists, since pathological changes are usually complex and slight.Approach.To have an accuracy of the MI detection, the prominent features extracted from in-depth mining of ECG signals need to be explored. In this study, a dynamic learning algorithm is applied to discover prominent features for identifying MI patients via mining the hidden inherent dynamics in ECG signals. Firstly, the distinctive dynamic features extracted from the multi-scale decomposition of dynamic modeling of the ECG signals effectively and comprehensibly represent the pathological ECG changes. Secondly, a few most important dynamic features are filtered through a hybrid feature selection algorithm based on filter and wrapper to form a representative reduced feature set. Finally, different classifiers based on the reduced feature set are trained and tested on the public PTB dataset and an independent clinical data set.Main results.Our proposed method achieves a significant improvement in detecting MI patients under the inter-patient paradigm, with an accuracy of 94.75%, sensitivity of 94.18%, and specificity of 96.33% on the PTB dataset. Furthermore, classifiers trained on PTB are verified on the test data set collected from 200 patients, yielding a maximum accuracy of 84.96%, sensitivity of 85.04%, and specificity of 84.80%.Significance.The experimental results demonstrate that our method performs distinctive dynamic feature extraction and may be used as an effective auxiliary tool to diagnose MI patients.

Early detection of myocardial ischemia in 12-lead ECG using deterministic learning and ensemble learning

BACKGROUND AND OBJECTIVE

Early detection of myocardial ischemia is a necessary but difficult problem in cardiovascular diseases. Approaches that exclusively rely on classical ST and T wave changes on the standard 12-lead electrocardiogram (ECG) lack sufficient accuracy in detecting myocardial ischemia. This study aims to construct generalizable models for the detection of myocardial ischemia in patients with subtle ECG waveform changes (namely non-diagnostic ECG) using ensemble learning to integrate ECG dynamic features acquired via deterministic learning.

METHODS

First, cardiodynamicsgram (CDG), a noninvasive spatiotemporal electrocardiographic method, is generated through dynamic modeling of ECG signals using the deterministic learning algorithm. Then, the spectral fitting exponent, Lyapunov exponent, and Lempel-Ziv complexity are extracted from CDG. Subsequently, the bagging-based heterogeneous ensemble algorithm is applied on CDG features to generate diverse base classifiers and aggregate them with weighted voting to obtain an ensemble model for myocardial ischemia detection. Finally, we train and test the proposed heterogeneous ensemble model on a real-world clinical dataset. This dataset consists of 499 non-diagnostic 12-lead ECG records from 499 patients collected from three independent medical centers, including 383 patients with myocardial ischemia and 116 patients without ischemia.

RESULTS

With 10-times 5-fold cross-validation technology, our proposed method achieves an average accuracy of 89.10%, sensitivity of 91.72%, and specificity of 82.69% using the heterogeneous ensemble algorithm on the real-world clinical dataset. On three independent medical centers, our ensemble model also achieves accuracy performance over 82% for patients with non-diagnostic ECG. Furthermore, our ensemble model trained with real-world clinical data yields promising results of 91.11% accuracy, 90.49% sensitivity, and 92.88% specificity on the external test set of the public PTB dataset.

CONCLUSION

The experimental results demonstrate that the proposed model combining ensemble learning and deterministic learning presents excellent diagnostic accuracy and generalization in clinical practice, and could be implemented as a complement to the standard ECG in the clinical diagnosis of myocardial ischemia.

Monitoring of Serum Potassium and Calcium Levels in End-Stage Renal Disease Patients by ECG Depolarization Morphology Analysis

Objective: Non-invasive estimation of serum potassium, [K+], and calcium, [Ca2+], can help to prevent life-threatening ventricular arrhythmias in patients with advanced renal disease, but current methods for estimation of electrolyte levels have limitations. We aimed to develop new markers based on the morphology of the QRS complex of the electrocardiogram (ECG). Methods: ECG recordings from 29 patients undergoing hemodialysis (HD) were processed. Mean warped QRS complexes were computed in two-minute windows at the start of an HD session, at the end of each HD hour and 48 h after it. We quantified QRS width, amplitude and the proposed QRS morphology-based markers that were computed by warping techniques. Reference [K+] and [Ca2+] were determined from blood samples acquired at the time points where the markers were estimated. Linear regression models were used to estimate electrolyte levels from the QRS markers individually and in combination with T wave morphology markers. Leave-one-out cross-validation was used to assess the performance of the estimators. Results: All markers, except for QRS width, strongly correlated with [K+] (median Pearson correlation coefficients, r, ranging from 0.81 to 0.87) and with [Ca2+] (r ranging from 0.61 to 0.76). QRS morphology markers showed very low sensitivity to heart rate (HR). Actual and estimated serum electrolyte levels differed, on average, by less than 0.035 mM (relative error of 0.018) for [K+] and 0.010 mM (relative error of 0.004) for [Ca2+] when patient-specific multivariable estimators combining QRS and T wave markers were used. Conclusion: QRS morphological markers allow non-invasive estimation of [K+] and [Ca2+] with low sensitivity to HR. The estimation performance is improved when multivariable models, including T wave markers, are considered. Significance: Markers based on the QRS complex of the ECG could contribute to non-invasive monitoring of serum electrolyte levels and arrhythmia risk prediction in patients with renal disease.

Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability

OBJECTIVES

an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed.

METHODS

ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response.

RESULTS

PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive.

CONCLUSIONS

PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.

An integrated framework for evaluation on typical ECG-derived respiration waveform extraction and respiration

OBJECTIVE

ECG-derived respiration (EDR) methods have been developed during the past decades to obtain respiration-relevant information. However, it is still necessary to compare the performance of these methods under uniform conditions for reasonable application.

APPROACH

In this paper, the performance of 10 feature-based EDR methods was evaluated comprehensively on three aspects: sampling rate, noise, and window length. The Fantasia database was used in this study, as it contained ECG signals and simultaneously measured respiration signals. The performance was quantified by two parameters: waveform correlation and breathing rate (BR) errors.

MAIN RESULTS

The BR errors of AM_area, AM_QR, AM_R were all below 2 beats per minute (bpm) when the sampling rate was above 150 Hz, while they decreased sharply by about 60% when the sampling rate was below 150 Hz. FM_RR presented stable performance with an error below 2 bpm at different sampling rates. The effect of noise was obviously found in amplitude-based EDR methods, with the maximum decreased by about 40% in waveform correlation. For all EDR methods, significant increase of BR errors occurred with the window shorting from 32 s to 16 s in the frequency-based technique. In addition, about 30%-40% of the window cannot obtain the BR error, calculated based on the time-based technique, within an 8 s window.

SIGNIFICANCE

We proposed a comprehensive and integrated evaluation on typical ECG-derived respiration waveform extraction and respiration rate calculation, providing references for algorithm selection based on different requirements.

R-Wave Singularity: A New Morphological Approach to the Analysis of Cardiac Electrical Dyssynchrony

R-wave singularity (RWS) measures the intermittence or discontinuousness of R waves. It has been broadly used in QRS (QRS complex of electrocardiogram) detection, electrocardiogram (ECG) beats classification, etc. In this article, we novelly developed RWS to the analysis of QRS morphology as the measurement of ventricular dyssynchrony and tested the hypothesis that RWS could enhance the discrimination between control and acute myocardial infarction (AMI) patients. Holter ECG recordings were obtained from the Telemetric and Holter ECG Warehouse database, among which database Normal was extracted as normal controls (n = 202) and database AMI (n = 93) as typical subjects of autonomic nervous system dysfunction and cardiac electrical dyssynchrony with high risk for cardiac arrhythmias and sudden cardiac death. Experimental results demonstrate that RWS measured by Lipschitz exponent calculated from 5-min Holter recordings was significantly less negative in early AMI and late AMI than that in Normal subjects for overall, elderly, and elderly male groups, which suggested the heterogeneous depolarization of the ventricular myocardium during AMI. Receiver operating characteristic curve analyses show that combined with heart rate variability parameters, Lipschitz exponent provides higher accuracy in distinguishing between the patients with AMI and healthy control subjects for overall, elderly, elderly male, and elderly female groups. In summary, our study demonstrates the significance of using RWS to probe the cardiac electrical dyssynchrony for AMI. Lipschitz exponent may be valuable and complementary for existing cardiac resynchronization therapy and autonomic nervous system assessment.

High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence

Acute myocardial ischemia is a precursor of sudden arrhythmic death. Variability in its manifestation hampers understanding of arrhythmia mechanisms and challenges risk stratification. Our aim is to unravel the mechanisms underlying how size, transmural extent and location of ischemia determine arrhythmia vulnerability and ECG alterations. High performance computing simulations using a human torso/biventricular biophysically-detailed model were conducted to quantify the impact of varying ischemic region properties, including location (LAD/LCX occlusion), transmural/subendocardial ischemia, size, and normal/slow myocardial propagation. ECG biomarkers and vulnerability window for reentry were computed in over 400 simulations for 18 cases evaluated. Two distinct mechanisms explained larger vulnerability to reentry in transmural versus subendocardial ischemia. Macro-reentry around the ischemic region was the primary mechanism increasing arrhythmic risk in transmural versus subendocardial ischemia, for both LAD and LCX occlusion. Transmural micro-reentry at the ischemic border zone explained arrhythmic vulnerability in subendocardial ischemia, especially in LAD occlusion, as reentries were favoured by the ischemic region intersecting the septo-apical region. ST elevation reflected ischemic extent in transmural ischemia for LCX and LAD occlusion but not in subendocardial ischemia (associated with mild ST depression). The technology and results presented can inform safety and efficacy evaluation of anti-arrhythmic therapy in acute myocardial ischemia.

Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low-Stress States: Attention Test

Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should first be able to identify different states based on physiological signals. So, the first aim of this paper is to identify the most appropriate features to detect a subject's high performance state. For that, a database of electrocardiographic (ECG) and photoplethysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals, respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, 26 features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive than the classical sympathetic markers ([Formula: see text] and [Formula: see text]) for identifying this attentional state. State classification accuracy reaches a mean of [Formula: see text], a maximum of [Formula: see text], and a minimum of 85%, in the 100 classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulsewidth, pulse downward slope, and mean pulse rate). These results suggest that attentional states could be identified by PPG.

Autonomic Nervous System Measurement in Hyperbaric Environments Using ECG and PPG Signals

The main aim of this paper was to characterize the Autonomic Nervous System response in hyperbaric environments using electrocardiogram (ECG) and pulse-photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyze Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related dependency, with the heart rate and sympathetic markers (normalized power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependence between the atmospheric pressure and the parasympathetic response, as reflected in the high-frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage; thus, highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed; therefore, we propose the use of this signal for future studies in hyperbaric environments.

Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related "Big Data" Using Body Sensors information and Communication Technology

The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered 'big data'. To our knowledge, no study has highlighted the link between 'big data' characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six 'Vs': volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.

A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records

There is a growing body of research focusing on automatic detection of ischemia and myocardial infarction (MI) using computer algorithms. In clinical settings, ischemia and MI are diagnosed using electrocardiogram (ECG) recordings as well as medical context including patient symptoms, medical history, and risk factors-information that is often stored in the electronic health records. The ECG signal is inspected to identify changes in the morphology such as ST-segment deviation and T-wave changes. Some of the proposed methods compute similar features automatically while others use nonconventional features such as wavelet coefficients. This review provides an overview of the methods that have been proposed in this area, focusing on their historical evolution, the publicly available datasets that they have used to evaluate their performance, and the details of their algorithms for ECG and EHR analysis. The validation strategies that have been used to evaluate the performance of the proposed methods are also presented. Finally, the paper provides recommendations for future research to address the shortcomings of the currently existing methods and practical considerations to make the proposed technical solutions applicable in clinical practice.

An Implantable Extracardiac Soft Robotic Device for the Failing Heart: Mechanical Coupling and Synchronization

Soft robotic devices have significant potential for medical device applications that warrant safe synergistic interaction with humans. This article describes the optimization of an implantable soft robotic system for heart failure whereby soft actuators wrapped around the ventricles are programmed to contract and relax in synchrony with the beating heart. Elastic elements integrated into the soft actuators provide recoiling function so as to aid refilling during the diastolic phase of the cardiac cycle. Improved synchronization with the biological system is achieved by incorporating the native ventricular pressure into the control system to trigger assistance and synchronize the device with the heart. A three-state electro-pneumatic valve configuration allows the actuators to contract at different rates to vary contraction patterns. An in vivo study was performed to test three hypotheses relating to mechanical coupling and temporal synchronization of the actuators and heart. First, that adhesion of the actuators to the ventricles improves cardiac output. Second, that there is a contraction-relaxation ratio of the actuators which generates optimal cardiac output. Third, that the rate of actuator contraction is a factor in cardiac output.

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. Patients in the database were classified into three categories of moderate/severe ischemia, mild ischemia, and normal according to the differences in sum of the individual segment scores for the rest and stress SPECT images. Philips DXL 16-lead diagnostic algorithm was run on all 10-s segments of 12-lead ECG recordings for each patient to acquire the representative beats, ECG fiducial points from the representative beats, and other ECG parameters. The QRS slopes were extracted for each lead from the averaged representative beats and the leads with highest classification power were selected. We employed linear discriminant analysis and measured the performance using 10-fold cross-validation. Comparable performance of this method to the conventional ST-segment analysis exhibits the classification power of QRS slopes as unconventional ECG parameters contributing to improved identification of exercise-induced ischemia.

Implementation of a portable device for real-time ECG signal analysis

Background

Cardiac disease is one of the main causes of catastrophic mortality. Therefore, detecting the symptoms of cardiac disease as early as possible is important for increasing the patient’s survival. In this study, a compact and effective architecture for detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We developed a portable device using this architecture, which allows real-time electrocardiogram (ECG) signal acquisition and analysis for cardiac diseases.

Methods

A noisy ECG signal was preprocessed by an analog front-end consisting of analog filters and amplifiers before it was converted into digital data. The analog front-end was minimized to reduce the size of the device and power consumption by implementing some of its functions with digital filters realized in software. With the ECG data, we detected QRS complexes based on wavelet analysis and feature extraction for morphological shape and regularity using an ARM processor. A classifier for cardiac disease was constructed based on features extracted from a training dataset using support vector machines. The classifier then categorized the ECG data into normal beats, AFib, and myocardial ischemia.

Results

A portable ECG device was implemented, and successfully acquired and processed ECG signals. The performance of this device was also verified by comparing the processed ECG data with high-quality ECG data from a public cardiac database. Because of reduced computational complexity, the ARM processor was able to process up to a thousand samples per second, and this allowed real-time acquisition and diagnosis of heart disease. Experimental results for detection of heart disease showed that the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity of 95.9%.

Conclusions

Current home care and telemedicine systems have a separate device and diagnostic service system, which results in additional time and cost. Our proposed portable ECG device provides captured ECG data and suspected waveform to identify sporadic and chronic events of heart diseases. This device has been built and evaluated for high quality of signals, low computational complexity, and accurate detection.

A novel low-complexity post-processing algorithm for precise QRS localization

Precise localization of QRS complexes is an essential step in the analysis of small transient changes in instant heart rate and before signal averaging in QRS morphological analysis. Most localization algorithms reported in literature are either not robust to artifacts, depend on the sampling rate of the ECG recordings or are too computationally expensive for real-time applications, especially in low-power embedded devices. This paper proposes a localization algorithm based on the intersection of tangents fitted to the slopes of R waves detected by any QRS detector. Despite having a lower complexity, this algorithm achieves comparable trigger jitter to more complex localization methods without requiring the data to first be upsampled. It also achieves high localization precision regardless of which QRS detector is used as input. It is robust to clipping artifacts and to noise, achieving an average localization error below 2 ms and a trigger jitter below 1 ms on recordings where no additional artifacts were added, and below 8 ms for recordings where the signal was severely degraded. Finally, it increases the accuracy of template-based false positive rejection, allowing nearly all mock false positives added to a set of QRS detections to be removed at the cost of a very small decrease in sensitivity. The localization algorithm proposed is particularly well-suited for implementation in embedded, low-power devices for real-time applications.

Electrocardiogram derived respiration from QRS slopes

A method for estimation of respiratory rate from electrocardiogram (ECG) signals, based on variations in slopes of QRS complexes, is presented. 12 standard leads, 3 leads from vectorcardiogram (VCG), and 2 additional non-standard leads derived from VCG loops were analysed. A total of 34 slope series were studied, 2 for each analysed lead: slopes between the peak of Q and R waves, and between the peak of R and S waves. Information of QRS slopes series was combined in order to increase the robustness of estimation. Evaluation is performed over a database containing ECG and respiratory signals simultaneously recorded in 17 subjects spontaneously breathing during a tilt table test. Respiratory rate estimation is performed with information of 4 different combinations of QRS slope series. The best results in respiratory rate estimation error terms are 0.72 ± 4.34%(0.46 ± 7.59 mHz). These results outperform those obtained with other known methods, motivating the use of QRS slopes to obtain reliable respiratory rate estimates.

Very low frequency modulation in QRS slopes and its relation with respiration and heart rate variability during hemodialysis

In this work, we study the very low frequency (VLF) modulation (range 0.01-0.03 Hz) in QRS slopes, heart rate variability (HRV) and ECG-derived respiration in hemodialysis patients. First, the relation between QRS slopes and HRV in the VLF band is measured using ordinary coherence. Then, partial coherence is used to measure the former relationship once the effect related to respiration is removed. Ordinary coherence values above a statistical threshold revealed linear relationship between VLF modulation in QRS slopes and HRV in about 10% of analyzed segments, with mean ± SD values of 0.79 ± 0.07 for upward slope and 0.77 ± 0.06 for downward slope. For these segments, partial coherence values drop below the threshold for 64% of the cases for upward slope and 76% for downward slope, suggesting that the origin of the VLF modulation in QRS slopes is mainly driven by respiration or linearly related to it. In the rest of the cases, partial coherence values dropped with respect to ordinary coherence from 0.89 to 0.77 for upward slope and from 0.86 to 0.75 for downward slope, suggesting that other ANS effects non-linearly related to respiration also contribute to the VLF modulation in QRS slopes.

RELATIONSHIP AMONG QRS COMPLEX CHARACTERS IN ELECTROCARDIOGRAM AND ITS APPLICATION TO MYOCARDIAL ISCHEMIA

Electrocardiogram (ECG) is a noninvasive, economic, and convenient detecting tool in myocardial ischemia (MI), and its clinical appearance is mainly exhibited by ST-T complex changes. Recently, QRS complex characters in detecting MI were proposed by an increasing number of researchers. In this paper, various QRS complex characters were extracted in ECG, and their relationship was analyzed systematically. As a result, these characters were divided into two groups, and there was good correlation among them in each group, while the correlation between the groups was poor. Finally, these QRS complex characters were applied to myocardial ischemia, and five characters had significant differences after 59 normal ECG recordings verification, which were: QRS upward and downward slopes, transient heart rate, angle R and angle Q in a triangle QRS. Experimental results showed it was apparent that the trend changes of these five characters when MI events occurred were consistent with their relationship. The conduction velocity of action potentials in ventricular depolarization is slower in MI states than in normal states.

Novel set of vectorcardiographic parameters for the identification of ischemic patients

New signal processing techniques have enabled the use of the vectorcardiogram (VCG) for the detection of cardiac ischemia. Thus, we studied this signal during ventricular depolarization in 80 ischemic patients, before undergoing angioplasty, and 52 healthy subjects with the objective of evaluating the vectorcardiographic difference between both groups so leading to their subsequent classification. For that matter, seven QRS-loop parameters were analyzed, i.e.: (a) Maximum Vector Magnitude; (b) Volume; (c) Planar Area; (d) Maximum Distance between Centroid and Loop; (e) Angle between XY and Optimum Plane; (f) Perimeter and, (g) Area-Perimeter Ratio. For comparison, the conventional ST-Vector Magnitude (ST(VM)) was also calculated. Results indicate that several vectorcardiographic parameters show significant differences between healthy and ischemic subjects. The identification of ischemic patients via discriminant analysis using ST(VM) produced 73.2% Sensitivity (Sens) and 73.9% Specificity (Spec). In our study, the QRS-loop parameter with the best global performance was Volume, which achieved Sens=64.5% and Spec=74.6%. However, when all QRS-loop parameters and ST(VM) were combined, we obtained Sens=88.5% and Spec=92.1%. In conclusion, QRS loop parameters can be accepted as a complement to conventional ST(VM) analysis in the identification of ischemic patients.

Effect of acute myocardial ischemia on different high-frequency bandwidths and temporal regions of the QRS

The purpose of this study was to describe the changes in high-frequency QRS (HF-QRS) components due to myocardial ischemia provoked by prolonged artery occlusion during percutaneous coronary interventions (PCI). Signal-averaged ECGs from 69 patients were obtained during PCI procedures and comparison of high-frequency components of the QRS at different temporal regions and frequency bandwidth were performed. Continuous wavelet transform was applied to estimate the energy contents over the studied time-frequency regions. Seven frequency bands from 50 to 300 Hz, with bandwidth = 100 Hz were considered. The sum of all 12 leads energy decreased significantly (p < 0.001) from pre-PCI to PCI during both second half and total QRS complex in all frequency bands, but the main effect was found in the 200-300 Hz band. The energy changes were more marked toward higher frequency bands. The second half of QRS complex was more sensitive to changes due to myocardial ischemia.

Myocardial ischemia analysis based on electrocardiogram QRS complex

Electrocardiogram (ECG) is an economic, convenient, and non-invasive detecting tool in myocardial ischemia (MI), and its clinical appearance is mainly exhibited by the changes in ST-T complex. Recently, QRS complex characters were proposed to analyze MI by more and more researchers. In this paper, various QRS complex characters were extracted in ECG signals, and their relationship was analyzed systematically. As a result, these characters were divided into two groups, and there existed good relationship among them for each group, while the poor relationship between the groups. Then these QRS complex characters were applied for statistical analysis on MI, and five characters had significant differences after ECG recording verification, which were: QRS upward and downward slopes, transient heart rate, angle R and angle Q. On the other hand, these QRS complex characters were analyzed in frequency domain. Experimental results showed that the frequency features of RR interval series (Heart Rate Variability, HRV), and QRS barycenter sequence had significant differences between MI states and normal states. Moreover, QRS barycenter sequence performed better.

"ECG variability contour" method reveals amplitude changes in both ischemic patients and normal subjects during Dipyridamole stress: a preliminary report

To detect and quantify consistent ECG amplitude changes, the "ECG variability contour" (EVC) method was proposed. Using this method we investigated amplitude changes in subjects undergoing myocardial perfusion imaging (MPI) with Dipyridamole (Dp). Fifty-three patients having reversible perfusion defects and 19 normal subjects (NS) who were free of: perfusion defects on their MPI, standard ST-T changes during Dp stress, and a negative clinical follow up. Mean ∏¹(<∏¹>) was similar for the NS and patient group (6.2 ± 6.1 vs. 6.3 ± 6.2, P = 0.95). <∏¹> was 4.6 ± 3.0 in patients not having ST-T changes during Dp stress (n = 42), whereas in patients having ST-T changes (n = 11) it was 13.1 ± 10.2 (P < 0.001). For both groups <∏(QRS)> was smaller than <∏(ST)>, which in turn was smaller than <∏(T)>. The values of <∏(QRS)>, <∏(ST)>, and <∏(T)> for the NS, patients without and with ST-T changes were: 26.8 ± 28.6, 42.6 ± 41.8, 44.9 ± 36.5; 19.6 ± 20.8, 26.4 ± 31.4, 38.7 ± 27.3; 51.0 ± 30.0, 71.0 ± 36.8, 75.1 ± 20.9, respectively (P < 0.05 for all comparisons of patients with versus without ST-T changes). This study showed that Dp stress, with or without hypoperfusion, had a clear effect on myocyte electrophysiology, expressed by consistent ECG amplitude changes, detected by the EVC method. The EVC method did not distinguish between NS and patients in this clinical setting.

Changes in the initial slope of the QRS in ischemic patients and normal subjects undergoing scintigraphy with dipyridamole

The effect of dipyridamole (Dp) on the linear portion of the first major deflection of the QRS in human electrocardiograms (which we refer to as Fslope) was investigated in the context of myocardial perfusion imaging (MPI), in normal subjects (NS, n=19) and in patients with cardiovascular ischemia (as manifested by a positive MPI scan, n=53). In the majority of participants, Fslope decreased (84% of 19 NS and 77% of 53 patients, p=0.69). The decrease in Fslope was more pronounced in patients with ST-T deviation as compared to those without (32.6±24.5 vs. 22.4±23.1, p=0.24), and in patients vs. NS (24.9±23.5 vs. 19.6±23.4, p=0.44). A change in Fslope is a sensitive (but not specific) measure for detecting the effect of Dp on the myocardium in the setting of scintigraphy.