Low level and high frequency fragmentation of the QRS changes during acute myocardial ischemia in patients with and without prior myocardial infarction

High frequency (HF) QRS fragmentation and very-low amplitude abnormal intra-QRS potential (uAIQP) analyses have been used to track ischemic changes during coronary artery occlusions. The aim of this study was to assess the relationship between these two techniques in detecting acute myocardial ischemia and the effects of a previous myocardial infarction (MI). Fifty-six patients who underwent elective percutaneous coronary intervention (PCI) procedures were selected and classified into 2 groups according to the presence of prior healed MI (old-MI) (n=18) or not (no_MI) (n=38). Continuous ECG before and during the PCI were recorded and signal-averaged. uAIQPs were obtained using a signal modelling approach. HFQRSRMS was obtained by band pass filtering the ECGs at 150 to 250 Hz. QRS-HFpower was estimated from a modeling power spectral technique. uAIQP and HF indices were obtained from a baseline and an occlusion-PCI ECG episode. uAIQP and HF values decreased (p<0.05) for each of the 12 leads at the PCI event respect to baseline in all patients and the no-MI group. Changes in uAIQP or HF did not separate the groups. uAIQP and QRS-HFpower values at baseline were lower in all leads, except V1-V2, in the old-MI groups compared to no-MI (p<0.05). Pearson’s correlation showed moderate relationship among the indices in most of leads. High-frequency QRS fragmentation indices could add diagnostic value to ST analysis for diagnosing ischemia when a baseline ECG information is available. Patients with old-MI presented lower uAIQP amplitudes compared to no-MI, however further studies are needed to elucidate the effects of old MI on very-low level fragmentation of the QRS.Peer ReviewedPostprint (published version

High-frequency Electrocardiogram Analysis in the Ability to Predict Reversible Perfusion Defects during Adenosine Myocardial Perfusion Imaging

Background: A previous study has shown that analysis of high-frequency QRS components (HF-QRS) is highly sensitive and reasonably specific for detecting reversible perfusion defects on myocardial perfusion imaging (MPI) scans during adenosine. The purpose of the present study was to try to reproduce those findings. Methods: 12-lead high-resolution electrocardiogram recordings were obtained from 100 patients before (baseline) and during adenosine Tc-99m-tetrofosmin MPI tests. HF-QRS were analyzed regarding morphology and changes in root mean square (RMS) voltages from before the adenosine infusion to peak infusion. Results: The best area under the curve (AUC) was found in supine patients (AUC=0.736) in a combination of morphology and RMS changes. None of the measurements, however, were statistically better than tossing a coin (AUC=0.5). Conclusion: Analysis of HF-QRS was not significantly better than tossing a coin for determining reversible perfusion defects on MPI scans

High-frequency QRS analysis compared to conventional ST-segment analysis in patients with chest pain and normal ECG referred for exercise tolerance test

Background: The novel analysis of high-frequency QRS components (HFQRS-analysis) has been proposed in patients with chest pain (CP) and normal electrocardiography (ECG) referred for exercise tolerance test (ex-ECG). The aim of the study was to compare the diagnostic value of ex-ECG with ex-HFQRS-analysis. Methods: Patients with CP and normal ECG, troponin, and echocardiography were consid­ered. All patients underwent ex-ECG for conventional ST-segment-analysis and ex-HFQRS-analysis. A decrease ≥ 50% of the HFQRS signal intensity recorded in at least 2 contiguous leads was considered an index of ischemia, as ST-segment depression ≥ 2 mm or ≥ 1 mm and CP on ex-ECG. Exclusion criteria were: QRS duration ≥ 120 ms and inability to exercise. End-point: The composite of coronary stenosis ≥ 70% or acute coronary syndrome, revascu­larization, cardiovascular death at 3-month follow-up. Results: Three-hundred thirty-seven patients were enrolled (age 60 ± 15 years). The percent­age of age-adjusted maximal predicted heart rate was 89 ± 10 beat per minute and the maximal systolic blood pressure was 169 ± 23 mm Hg. Nineteen patients achieved the end-point. In multivariate analysis, both ex-ECG and ex-HFQRS were predictors of the end-point. The ex-HFQRS-analysis showed higher sensitivity (63% vs. 26%; p &lt; 0.05), lower specificity (68% vs. 95%; p &lt; 0.001), and comparable negative predictive value (97% vs. 96%; p = 0.502) when compared to ex-ECG-analysis. Receiver operator characteristics analysis showed the incremental diagnostic value of HFQRS (area: 0.655, 95% CI 0.60–0.71) over conventional ex-ECG (0.608, CI 0.55–0.66) and CP score (0.530, CI 0.48–0.59), however without statistical significance in pairwise comparison by C-statistic. Conclusions: In patients with CP submitted to ex-ECG, the novel ex-HFQRS-analysis shows a valuable incremental diagnostic value over ST-segment-analysis

Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Evaluation of depolarization changes during acute myocardial ischemia by analysis of QRS slopes.

OBJECTIVE: This study evaluates depolarization changes in acute myocardial ischemia by analysis of QRS slopes. METHODS: In 38 patients undergoing elective percutaneous coronary intervention, changes in upward slope between Q and R waves and downward slope between R and S waves (DS) were analyzed. In leads V1 to V3, upward slope of the S wave was additionally analyzed. Ischemia was quantified by myocardial scintigraphy. Also, conventional QRS and ST measures were determined. RESULTS: QRS slope changes correlated significantly with ischemia (for DS: r = 0.71, P < .0001 for extent, and r = 0.73, P < .0001 for severity). Best corresponding correlation for conventional electrocardiogram parameters was the sum of R-wave amplitude change (r = 0.63, P < .0001; r = 0.60, P < .0001) and the sum of ST-segment elevation (r = 0.67, P < .0001; r = 0.73, P < .0001). Prediction of extent and severity of ischemia increased by 12.2% and 7.1% by adding DS to ST. CONCLUSIONS: The downward slope between R and S waves correlates with ischemia and could have potential value in risk stratification in acute ischemia in addition to ST-T analysis

High-frequency ECG

The standard ECG is by convention limited to 0.05-150 Hz, but higher frequencies are also present in the ECG signal. With high-resolution technology, it is possible to record and analyze these higher frequencies. The highest amplitudes of the high-frequency components are found within the QRS complex. In past years, the term "high frequency", "high fidelity", and "wideband electrocardiography" have been used by several investigators to refer to the process of recording ECGs with an extended bandwidth of up to 1000 Hz. Several investigators have tried to analyze HF-QRS with the hope that additional features seen in the QRS complex would provide information enhancing the diagnostic value of the ECG. The development of computerized ECG-recording devices that made it possible to record ECG signals with high resolution in both time and amplitude, as well as better possibilities to store and process the signals digitally, offered new methods for analysis. Different techniques to extract the HF-QRS have been described. Several bandwidths and filter types have been applied for the extraction as well as different signal-averaging techniques for noise reduction. There is no standard method for acquiring and quantifying HF-QRS. The physiological mechanisms underlying HF-QRS are still not fully understood. One theory is that HF-QRS are related to the conduction velocity and the fragmentation of the depolarization wave in the myocardium. In a three-dimensional model of the ventricles with a fractal conduction system it was shown that high numbers of splitting branches are associated with HF-QRS. In this experiment, it was also shown that the changes seen in HF-QRS in patients with myocardial ischemia might be due to the slowing of the conduction velocity in the region of ischemia. This mechanism has been tested by Watanabe et al by infusing sodium channel blockers into the left anterior descending artery in dogs. In their study, 60 unipolar ECGs were recorded from the entire ventricular surface and were signal-averaged and filtered in the 30-250 Hz frequency range. The results showed that the decrease noted in the HF-QRS correlated linearly with the local conduction delay. The results suggest that HF-QRS is a potent indicator of disturbed local conduction. An alternative theory is that HF-QRS reflect the shape of the original electrocardiographic signal. Bennhagen et al showed that root mean square (RMS) voltage values of the depolarization signal correlate poorly with the signal amplitude but highly with the first and second derivatives, i.e. the velocity and the acceleration of the signal. It has also been suggested that the autonomic nervous system affects HF-QRS. For example, sitting up causes significant changes in HF-QRS in some leads compared to the supine position [Douglas et al., 2006]. Unpublished results indicate that familial dysautonomic patients (both vagal and sympathetic degeneration) have very little Reduced Amplitude Zones (RAZ) formation . Athletic individuals, especially elite athletes, who have vagally-mediated changes on the conventional ECG (i.e. early repolarization, bradycardia) have increased RAZ formation. Further electrophysiological studies are needed, however, to better understand the underlying mechanisms of HF-QRS. Several investigators have studied HF-QRS in different cardiac conditions, including acute myocardial ischemia and myocardial infarction (MI). However, in order for clinicians to confidently use HF-QRS as an adjunct to standard ECG, more knowledge about the characteristics of HF-QRS is needed

Identification of myocardial infarction by high-frequency serial ECG measurement

The purpose of this study is to attempt to identify acute myocardial infarction with high frequency serial electrocardiogram. High-frequency ECG and serial ECG are both unique ECG analysing techniques. The idea in this study is to combine these two and see if changes between different ECGs from the same person can provide us some information, whether it being in the high-frequency or normal frequency range of the ECG. To answer the questions, an existing database which contained multiple ECGs for each person with high sampling frequency was used. 5 different machine learning models were trained and tested with this database. The results of the machine learning methods were good, producing the mean accuracy of 91.9%, while the best model was the Extra Trees machine learning model. It produced the accuracy of 97.9% when applying cross-validation to the database. After these results, high-frequency serial ECG could be stated to be relevant. However, having ECG measured regularly can be expensive and time consuming. Therefore, the possibility of using a wearable ECG device was also studied. With a device called SAFE, developed by the University of Turku, a new high-frequency serial ECG database was gathered. The already existing machine learning model trained with the previous data was applied to this database and produced a mean accuracy of 90%. The quality of the ECGs gathered with the device were also deemed to be viable. Both high-frequency ECG and serial ECG were found to be relevant methods. A wearable device could be used for AMI detection if the ECG is sufficient enough. Future studies could include increasing the dataset size of the wearable device, investigate other myocardial diseases and exploring the possibilities of high-frequency ECG further

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network