Centenarians and their hearts: a prospective registry with comprehensive geriatric assessment, electrocardiogram, echocardiography, and follow-up

Background Data on the cardiac characteristics of centenarians are scarce. Our aim was to describe electrocardiogram (ECG) and echocardiography in a cohort of centenarians and to correlate them with clinical data. Methods We used prospective multicenter registry of 118 centenarians (28 men) with a mean age of 101.5 ± 1.7 years. Electrocardiogram was performed in 103 subjects (87.3%) and echocardiography in 100 (84.7%). All subjects underwent a follow-up for at least 6 months. Results Centenarians with abnormal ECG were less frequently females (72% vs 93%), had higher rates of previous consumption of tobacco (14% vs 0) and alcohol (24% vs 12%), and scored lower in the perception of health status (6.8 ± 2.0 vs 8.3 ± 6.8). Centenarians with significant abnormalities in echocardiography were less frequently able to walk 6 m (33% vs 54%). Atrial fibrillation/flutter was found in 27 subjects (26%). Mean left ventricular (LV) ejection fraction was 60.0 ± 10.5%. Moderate or severe aortic valve stenosis was found in 16%, mitral valve regurgitation in 15%, and aortic valve regurgitation in 13%. Diastolic dysfunction was assessed in 79 subjects and was present in 55 (69.6%). Katz index and LV dilation were independently associated with the ability to walk 6 m. Age, Charlson and Katz indexes, and the presence of significant abnormalities in echocardiography were associated with mortality. Conclusions Centenarians have frequent ECG alterations and abnormalities in echocardiography. More than one fifth has atrial fibrillation, and most have diastolic dysfunction. Left ventricular dilation was associated with the ability to walk 6 m. Significant abnormalities in echocardiography were associated with mortality.

The impedance cardiogram recorded through two electrocardiogram/defibrillator pads as a determinant of cardiac arrest during experimental studies

OBJECTIVE: Laypersons are poor at emergency pulse checks (sensitivity 84%, specificity 36%). Guidelines indicate that pulse checks should not be performed. The impedance cardiogram (dZ/dt) is used to assess stroke volume. Can a novel defibrillator-based impedance cardiogram system be used to distinguish between circulatory arrest and other collapse states?

DESIGN: Animal study.

SETTING: University research laboratory.

SUBJECTS: Twenty anesthetized, mechanically ventilated pigs, weight 50-55 kg.

INTERVENTIONS: Stroke volume was altered by right ventricular pacing (160, 210, 260, and 305 beats/min). Cardiac arrest states were then induced: ventricular fibrillation (by rapid ventricular pacing) and, after successful defibrillation, pulseless electrical activity and asystole (by high-dose intravenous pentobarbitone).

MEASUREMENTS AND MAIN RESULTS: The impedance cardiogram was recorded through electrocardiogram/defibrillator pads in standard cardiac arrest positions. Simultaneously recorded electro- and impedance cardiogram (dZ/dt) along with arterial blood pressure tracings were digitized during each pacing and cardiac arrest protocol. Five-second epochs were analyzed for sinus rhythm (20 before ventricular fibrillation, 20 after successful defibrillation), ventricular fibrillation (40), pulseless electrical activity (20), and asystole (20), in two sets of ten pigs (ten training, ten validation). Standard impedance cardiogram variables were noncontributory in cardiac arrest, so the fast Fourier transform of dZ/dt was assessed. During ventricular pacing, the peak amplitude of fast Fourier transform of dZ/dt (between 1.5 and 4.5 Hz) correlated with stroke volume (r2 = .3, p < .001). In cardiac arrest, a peak amplitude of fast Fourier transform of dZ/dt of < or = 4 dB x ohm x rms indicated no output with high sensitivity (94% training set, 86% validation set) and specificity (98% training set, 90% validation set).

CONCLUSIONS: As a powerful clinical marker of circulatory collapse, the fast Fourier transformation of dZ/dt (impedance cardiogram) has the potential to improve emergency care by laypersons using automated defibrillators.

Heterogeneous error-resilient scheme for spectral analysis in ultra-low power wearable electrocardiogram devices

Wearable devices performing advanced bio-signal analysis algorithms are aimed to foster a revolution in healthcare provision of chronic cardiac diseases. In this context, energy efficiency is of paramount importance, as long-term monitoring must be ensured while relying on a tiny power source. Operating at a scaled supply voltage, just above the threshold voltage, effectively helps in saving substantial energy, but it makes circuits, and especially memories, more prone to errors, threatening the correct execution of algorithms. The use of error detection and correction codes may help to protect the entire memory content, however it incurs in large area and energy overheads which may not be compatible with the tight energy budgets of wearable systems. To cope with this challenge, in this paper we propose to limit the overhead of traditional schemes by selectively detecting and correcting errors only in data highly impacting the end-to-end quality of service of ultra-low power wearable electrocardiogram (ECG) devices. This partition adopts the protection of either significant words or significant bits of each data element, according to the application characteristics (statistical properties of the data in the application buffers), and its impact in determining the output. The proposed heterogeneous error protection scheme in real ECG signals allows substantial energy savings (11% in wearable devices) compared to state-of-the-art approaches, like ECC, in which the whole memory is protected against errors. At the same time, it also results in negligible output quality degradation in the evaluated power spectrum analysis application of ECG signals.

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets.

Experimental envenomation with Crotalus durissus terrificus venom in dogs treated with antiophidic serum - Part II: Laboratory aspects, electrocardiogram and histopathology

The present work shows laboratory aspects, electrocardiogram and histopathology results during experimental envenomation by Crotalus durissus terrificus in dogs treated with antiophidic serum. Twenty-one dogs were divided into three groups of seven animals each. Group I received 1mg/kg venom (sc); Group II received 1mg/kg venom (sc), 50mg antiophidic serum (iv) and fluid therapy including 0.9% NaCl solution (iv); and Group III received 1mg/kg venom (sc), 50mg antiophidic serum (iv) and fluid therapy including 0.9% NaCl solution containing sodium bicarbonate diluted to the dose of 4mEq/kg. Urinalysis showed brown urine, proteinuria, occult blood and myoglobinuria. Respiratory acidosis and hypotension were also observed. At the venom inoculation site, there was discreet edema, popliteal lymph node response, musculature presenting whitish areas and necrotic myositis with myoregenerative activity. There was not evidence of electrocardiographical and biochemical alterations.

Comparison of Polar(®) RS800G3(™) heart rate monitor with Polar(®) S810i(™) and electrocardiogram to obtain the series of RR intervals and analysis of heart rate variability at rest

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Variability analysis of discrete event series and wavefront patterns in surface electrocardiogram: contributions to psychophysiological and clinical research

The surface electrocardiogram (ECG) is an established diagnostic tool for the detection of abnormalities in the electrical activity of the heart. The interest of the ECG, however, extends beyond the diagnostic purpose. In recent years, studies in cognitive psychophysiology have related heart rate variability (HRV) to memory performance and mental workload. The aim of this thesis was to analyze the variability of surface ECG derived rhythms, at two different time scales: the discrete-event time scale, typical of beat-related features (Objective I), and the “continuous” time scale of separated sources in the ECG (Objective II), in selected scenarios relevant to psychophysiological and clinical research, respectively. Objective I) Joint time-frequency and non-linear analysis of HRV was carried out, with the goal of assessing psychophysiological workload (PPW) in response to working memory engaging tasks. Results from fourteen healthy young subjects suggest the potential use of the proposed indices in discriminating PPW levels in response to varying memory-search task difficulty. Objective II) A novel source-cancellation method based on morphology clustering was proposed for the estimation of the atrial wavefront in atrial fibrillation (AF) from body surface potential maps. Strong direct correlation between spectral concentration (SC) of atrial wavefront and temporal variability of the spectral distribution was shown in persistent AF patients, suggesting that with higher SC, shorter observation time is required to collect spectral distribution, from which the fibrillatory rate is estimated. This could be time and cost effective in clinical decision-making. The results held for reduced leads sets, suggesting that a simplified setup could also be considered, further reducing the costs. In designing the methods of this thesis, an online signal processing approach was kept, with the goal of contributing to real-world applicability. An algorithm for automatic assessment of ambulatory ECG quality, and an automatic ECG delineation algorithm were designed and validated.

A Phase Space Box-counting based Method for Arrhythmia Prediction from Electrocardiogram Time Series

Arrhythmia is one kind of cardiovascular diseases that give rise to the number of deaths and potentially yields immedicable danger. Arrhythmia is a life threatening condition originating from disorganized propagation of electrical signals in heart resulting in desynchronization among different chambers of the heart. Fundamentally, the synchronization process means that the phase relationship of electrical activities between the chambers remains coherent, maintaining a constant phase difference over time. If desynchronization occurs due to arrhythmia, the coherent phase relationship breaks down resulting in chaotic rhythm affecting the regular pumping mechanism of heart. This phenomenon was explored by using the phase space reconstruction technique which is a standard analysis technique of time series data generated from nonlinear dynamical system. In this project a novel index is presented for predicting the onset of ventricular arrhythmias. Analysis of continuously captured long-term ECG data recordings was conducted up to the onset of arrhythmia by the phase space reconstruction method, obtaining 2-dimensional images, analysed by the box counting method. The method was tested using the ECG data set of three different kinds including normal (NR), Ventricular Tachycardia (VT), Ventricular Fibrillation (VF), extracted from the Physionet ECG database. Statistical measures like mean (μ), standard deviation (σ) and coefficient of variation (σ/μ) for the box-counting in phase space diagrams are derived for a sliding window of 10 beats of ECG signal. From the results of these statistical analyses, a threshold was derived as an upper bound of Coefficient of Variation (CV) for box-counting of ECG phase portraits which is capable of reliably predicting the impeding arrhythmia long before its actual occurrence. As future work of research, it was planned to validate this prediction tool over a wider population of patients affected by different kind of arrhythmia, like atrial fibrillation, bundle and brunch block, and set different thresholds for them, in order to confirm its clinical applicability.

An indicator of sudden cardiac death during brief coronary occlusion: electrocardiogram QT time and the role of collaterals

The coronary collateral circulation has a beneficial role regarding all-cause and cardiac mortality. Hitherto, the underlying mechanism has not been clarified. The aim of this prospective study was to assess the effect of the coronary collateral circulation on electrocardiogram (ECG) QTc time change during short-term myocardial ischaemia.

Effects of cardiology review of the electrocardiogram in patients with suspected acute coronary syndromes

Misclassification of the electrocardiogram (ECG) contributes to treatment errors in patients with acute coronary syndrome. We hypothesized that cardiology ECG review could reduce these errors.

Determinants of prognostically relevant intracoronary electrocardiogram ST-segment shift during coronary balloon occlusion

The prognostic relevance of quantitative an intracoronary occlusive electrocardiographic (ECG) ST-segment shift and its determinants have not been investigated in humans. In 765 patients with chronic stable coronary artery disease, the following simultaneous quantitative measurements were obtained during a 1-minute coronary balloon occlusion: intracoronary ECG ST-segment shift (recorded by angioplasty guidewire), mean aortic pressure, mean distal coronary pressure, and mean central venous pressure (CVP). Collateral flow index (CFI) was calculated as follows: (mean distal coronary pressure minus CVP)/(mean aortic pressure minus CVP). During an average follow-up duration of 50 ± 34 months, the cumulative mortality rate from all causes was significantly lower in the group with an ST-segment shift <0.1 mV (n = 89) than in the group with an ST-segment shift ≥0.1 mV (n = 676, p = 0.0211). Factors independently related to intracoronary occlusive ECG ST-segment shift <0.1 mV (r(2) = 0.189, p <0.0001) were high CFI (p <0.0001), intracoronary occlusive RR interval (p = 0.0467), right coronary artery as the ischemic region (p <0.0001), and absence of arterial hypertension (p = 0.0132). "High" CFI according to receiver operating characteristics analysis was ≥0.217 (area under receiver operating characteristics curve 0.647, p <0.0001). In conclusion, absence of ECG ST-segment shift during brief coronary occlusion in patients with chronic coronary artery disease conveys a decreased mortality and is directly influenced by a well-developed collateral supply to the right versus left coronary ischemic region and by the absence of systemic hypertension in a patient's history.

Ajmaline attenuates electrocardiogram characteristics of inferolateral early repolarization

J waves are the hallmark of both inferolateral early repolarization (ER) and Brugada syndrome. While ajmaline, a class 1a antiarrhythmic drug, accentuates the J wave in Brugada syndrome, its effect on ER is unreported.