Naming of the waves in the ECG, with a brief account of their genesis

Oxidative damage and cardiotoxicity induced by 2-aminobenzothiazole in zebrafish (Danio rerio)

There is limited information available on cardiovascular toxicity of 2-Aminobenzothiazole (NTH), a derivative of benzothiazole (BTH) commonly used in tire production, in aquatic organisms. In the present study, the zebrafish embryos were exposed to varying concentrations of NTH (0, 0.05, 0.5, and 5 mg/L) until adulthood and the potential cardiovascular toxicity was assessed. NTH exposure resulted in striking aberrations in cardiac development, including heart looping failure and interference with atrioventricular canal differentiation. RNA-sequencing analysis indicated that NTH causes oxidative damage to the heart via ferroptosis, leading to oxygen supply disruption, cardiac malformation, and ultimately, zebrafish death. Quantitative real-time polymerase chain reaction (qPCR) analysis demonstrated the dysregulation of genes associated with early heart development, contraction, and oxidative stress. Additionally, reactive oxygen species accumulation and glutathione/malondialdehyde levels changes suggested a potential link between cardiac developmental toxicity and oxidative stress. In adult zebrafish, NTH exposure led to ventricular enlargement, decreased heart rate, reduced blood flow, and prolonged RR, QRS, and QTc intervals. To the best of our knowledge, this study is the first to provide evidence of cardiac toxicity and the adverse effects of ontogenetic NTH exposure in zebrafish, revealing the underlying toxic mechanisms connected with oxidative stress damage. These findings may provide crucial insights into the environmental risks associated with NTH and other BTHs.

ECG-Based Multiclass Arrhythmia Classification Using Beat-Level Fusion Network

Cardiovascular disease (CVD) is one of the most severe diseases threatening human life. Electrocardiogram (ECG) is an effective way to detect CVD. In recent years, many methods have been proposed to detect arrhythmia using 12-lead ECG. In particular, deep learning methods have been proven to be effective and have been widely used. The attention mechanism has attracted extensive attention in many fields in a series of deep learning methods. Off-the-shelf solutions based on deep learning and attention mechanism for ECG classification mostly give weights to time points. None of the existing methods were considered using the attention mechanism dealing with ECG signals at the level of heartbeats. In this paper, we propose a beat-level fusion net (BLF-Net) for multiclass arrhythmia classification by assigning weights at the heartbeat level, according to the contribution of the heartbeat to diagnostic results. This algorithm consists of three steps: (1) segmenting the long ECG signal into short beats; (2) using a neural network to extract features from heartbeats; and (3) assigning weights to features extracted from heartbeats using an attention mechanism. We test our algorithm on the PTB-XL database and have superiority over state-of-the-art performance on six classification tasks. Besides, the principle of this architecture is clarified by visualizing the weight of the attention mechanism. The proposed BLF-Net is shown to be useful and automatically provides an effective network structure for arrhythmia classification, which is capable of aiding cardiologists in arrhythmia diagnosis.

What Else Can AI See in a Digital ECG?

The electrocardiogram (ECG), considered by some diagnosticians of cardiovascular diseases to be a slightly anachronistic tool, has acquired a completely new face and importance thanks to its three modern features: the digital form of recording, its very frequent use, and the possibility of processing thousands of records by artificial intelligence (AI). In this review of the literature on this subject from the first 3 months of 2023, the use of many types of software for extracting new information from the ECG is described. These include, among others, natural language processing, backpropagation neural network and convolutional neural network. AI tools of this type allow physicians to achieve high accuracy not only in ECG-based predictions of the patient's age or sex but also of the abnormal structure of heart valves, abnormal electrical activity of the atria, distorted immune response after transplantation, good response to resynchronization therapy and an increased risk of sudden cardiac death. The attractiveness of the presented results lies in the simplicity of the examination by the staff, relatively low costs and even the possibility of performing the examination remotely. The twelve studies presented here are just a fraction of the novelties that the current year will bring.

Recent Advances and Challenges in Textile Electrodes for Wearable Biopotential Signal Monitoring: A Comprehensive Review

The technology of wearable medical equipment has advanced to the point where it is now possible to monitor the electrocardiogram and electromyogram comfortably at home. The transition from wet Ag/AgCl electrodes to various types of gel-free dry electrodes has made it possible to continuously and accurately monitor the biopotential signals. Fabrics or textiles, which were once meant to protect the human body, have undergone significant development and are now employed as intelligent textile materials for healthcare monitoring. The conductive textile electrodes provide the benefit of being breathable and comfortable. In recent years, there has been a significant advancement in the fabrication of wearable conductive textile electrodes for monitoring biopotential signals. This review paper provides a comprehensive overview of the advances in wearable conductive textile electrodes for biopotential signal monitoring. The paper covers various aspects of the technology, including the electrode design, various manufacturing techniques utilised to fabricate wearable smart fabrics, and performance characteristics. The advantages and limitations of various types of textile electrodes are discussed, and key challenges and future research directions are identified. This will allow them to be used to their fullest potential for signal gathering during physical activities such as running, swimming, and other exercises while being linked into wireless portable health monitoring systems.

Hierarchical deep learning with Generative Adversarial Network for automatic cardiac diagnosis from ECG signals

Cardiac disease is the leading cause of death in the US. Accurate heart disease detection is critical to timely medical treatment to save patients' lives. Routine use of the electrocardiogram (ECG) is the most common method for physicians to assess the cardiac electrical activities and detect possible abnormal conditions. Fully utilizing the ECG data for reliable heart disease detection depends on developing effective analytical models. In this paper, we propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for ECG signal analysis. The first-level model is composed of a Memory-Augmented Deep AutoEncoder with GAN (MadeGAN), which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmia identification, which is achieved by integrating the transfer learning technique to transfer knowledge from the first-level learning with the multi-branching architecture to handle the data-lacking and imbalanced data issues. We evaluate the performance of the proposed framework using real-world ECG data from the MIT-BIH arrhythmia database. Experimental results show that our proposed model outperforms existing methods that are commonly used in current practice.

Biodegradable Polymer Composites for Electrophysiological Signal Sensing

Electrophysiological signals are collected to characterize human health and applied in various fields, such as medicine, engineering, and pharmaceuticals. Studies of electrophysiological signals have focused on accurate signal acquisition, real-time monitoring, and signal interpretation. Furthermore, the development of electronic devices consisting of biodegradable and biocompatible materials has been attracting attention over the last decade. In this regard, this review presents a timely overview of electrophysiological signals collected with biodegradable polymer electrodes. Candidate polymers that can constitute biodegradable polymer electrodes are systemically classified by their essential properties for collecting electrophysiological signals. Moreover, electrophysiological signals, such as electrocardiograms, electromyograms, and electroencephalograms subdivided with human organs, are discussed. In addition, the evaluation of the biodegradability of various electrodes with an electrophysiology signal collection purpose is comprehensively revisited.

Blended Polymer Dry Electrodes for Reliable Electrocardiogram and Electromyogram Measurements and Their Eco-Friendly Disposal Led by Degradability in Hot Water

To increase the human lifespan, healthcare monitoring devices that diagnose diseases and check body conditions have attracted considerable interest. Commercial AgCl-based wet electrodes with the advantages of high conductivity and strong adaptability to human skin are considered the most frequently used electrode material for healthcare monitoring. However, commercial AgCl-based wet electrodes, when exposed for a long period, cause an evaporation of organic solvents, which could reduce the signal-to-noise ratio of biosignals and stimulate human skin. In this context, we demonstrate a dry electrode for a poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)-based blended polymer electrode using a combination of PEDOT:PSS, waterborne polyurethane (WPU) and ethylene glycol (EG) that could be reused for a long period of time to detect electrocardiography (ECG) and electromyography (EMG). Both ECG and EMG are reliably detected by the wireless real-time monitoring system. In particular, the proposed dry electrode detects biosignals without deterioration for over 2 weeks. Additionally, a double layer of a polyimide (PI) substrate and fluorinated polymer CYTOP induces the strong waterproof characteristics of external liquids for the proposed dry electrodes, having a low surface energy of 14.49 mN/m. In addition, the proposed electrode has excellent degradability in water; it dissolves in hot water at 60 °C.

Relationship between Subjective and Biological Responses to Comfortable and Uncomfortable Sounds

Various kinds of biological sensors are now embedded in wearable devices and data on human biological information have recently become more widespread. Among various environmental stressors, sound has emotional and biological impacts on humans, and it is worthwhile to investigate the relationship between the subjective impressions of and biological responses to such sounds. In this study, the relationship between subjective and biological responses to acoustic stimuli with two contrasting kinds of sounds, a murmuring river sound and white noise, was investigated. The subjective and biological responses were measured during the presentation of the sounds. Compared with the murmuring river sound, the white noise had a significantly decreased EEG-related index of α-EEG and HRV-related index of SD2/SD1. The correlation between each index of subjective and biological responses indicated that α-EEG was highly correlated with the results of subjective evaluation. However, based on a more detailed analysis with clustering, some subjects showed different biological responses in each trial since they felt the sound was powerful when listening to the murmuring river sound, as well as feeling that it was beautiful. It was suggested that biological responses to sound exposure may be affected by the impression of the sound, which varies by individual.

Diagnosing Atrial Septal Defect from Electrocardiogram with Deep Learning

The heart murmur associated with atrial septal defects is often faint and can thus only be detected by chance. Although electrocardiogram examination can prompt diagnoses, identification of specific findings remains a major challenge. We demonstrate improved diagnostic accuracy realized by incorporating a proposed deep learning model, comprising a convolutional neural network (CNN) and long short-term memory (LSTM), with electrocardiograms. This retrospective observational study included 1192 electrocardiograms of 728 participants from January 1, 2000, to December 31, 2017, at Tokyo Women's Medical University Hospital. Using echocardiography, we confirmed the status of healthy subjects-no structural heart disease-and the diagnosis of atrial septal defects in patients. We used a deep learning model comprising a CNN and LTSMs. All pediatric cardiologists (n = 12) were blinded to patient groupings when analyzing them by electrocardiogram. Using electrocardiograms, the model's diagnostic ability was compared with that of pediatric cardiologists. We assessed 1192 electrocardiograms (828 normally structured hearts and 364 atrial septal defects) pertaining to 792 participants. The deep learning model results revealed that the accuracy, sensitivity, specificity, positive predictive value, and F1 score were 0.89, 0.76, 0.96, 0.88, and 0.81, respectively. The pediatric cardiologists (n = 12) achieved means of accuracy, sensitivity, specificity, positive predictive value, and F1 score of 0.58 ± 0.06, 0.53 ± 0.04, 0.67 ± 0.10, 0.69 ± 0.18, and 0.58 ± 0.06, respectively. The proposed method is a superior alternative to accurately diagnose atrial septal defects.

Epsilon Waves as an Extreme Form of Depolarization Delay: Focusing on the Arrhythmogenic Right Ventricular Cardiomyopathy/Dysplasia

Revision of the Task Force diagnostic criteria (TFC) for arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D), in 2010, has increased the sensitivity for the diagnosis of early and familial forms of the disease. Epsilon wave (EW) is a major diagnostic criterion in the context of ARVC/D, however, it remains unquantifiable and therefore, may leave room for substantial subjective interpretation, thus, explaining the existing high inter-observer variability in the assessment of EW. EW, when present, coexists with other disease characteristics, which are sufficient for ARVC/D diagnosis, making EW generally not required for ARVC/D diagnosis. Nevertheless, EW remains an important part of the electrocardiographic phenotype of ARVC/D that may be useful in planning diagnostic work-up, which needs to be recognized.

ECG Language processing (ELP): A new technique to analyze ECG signals

BACKGROUND

A language is constructed of a finite/infinite set of sentences composing of words. Similar to natural languages, the Electrocardiogram (ECG) signal, the most common noninvasive tool to study the functionality of the heart and diagnose several abnormal arrhythmias, is made up of sequences of three or four distinct waves, including the P-wave, QRS complex, T-wave, and U-wave. An ECG signal may contain several different varieties of each wave (e.g., the QRS complex can have various appearances). For this reason, the ECG signal is a sequence of heartbeats similar to sentences in natural languages) and each heartbeat is composed of a set of waves (similar to words in a sentence) of different morphologies.

METHODS

Analogous to natural language processing (NLP), which is used to help computers understand and interpret the human's natural language, it is possible to develop methods inspired by NLP to aid computers to gain a deeper understanding of Electrocardiogram signals. In this work, our goal is to propose a novel ECG analysis technique, ECG language processing (ELP), focusing on empowering computers to understand ECG signals in a way physicians do.

RESULTS

We evaluated the proposed approach on two tasks, including the classification of heartbeats and the detection of atrial fibrillation in the ECG signals. Overall, our technique resulted in better performance or comparable performance with smaller neural networks compared to other deep neural networks and existing algorithms.

CONCLUSION

Experimental results on three databases (i.e., PhysioNet's MIT-BIH, MIT-BIH AFIB, and PhysioNet Challenge 2017 AFIB Dataset databases) reveal that the proposed approach as a general idea can be applied to a variety of biomedical applications and can achieve remarkable performance.

Carbon nanotube materials for electrocardiography

Carbon nanotubes (CNTs) as 1D nanomaterials of excellent physicochemical characteristics bring hope to compete and eventually conquer traditional solutions in electrocardiography - one of the most powerful and non-invasive diagnostic tools in cardiac disorders. Our review tracks (from 2008) the development of CNTs as critical components in the systems where CNTs serve mainly as electroconductive fillers hence enable recording electrocardiographs (ECG). The characteristics of the CNT-based ECG systems - mainly to-skin electrodes and in a few cases wiring - covers their electrical and mechanical performance (adhesivity, flexibility, elasticity) and qualitative biocompatibility. By comprehensive analysis of the state-of-art in this field, we intend to indicate the most important challenges for the CNT (and other) materials to be applied in scale-up solution for electrocardiography in the near future.

A Retrospective Analysis of Hospital Electrocardiogram Auto-Populated QT Interval Calculation

Background

The current electrocardiogram (ECG) standard for rate correction of the QT interval (QTc) is a power function known as the Bazett formula (QTcB). QTc formulae are either power functions or linear functions. QTcB is known to lack reliability, as heart rate (HR) rises from or falls below 60 beats per minute (bpm). The American Heart Association (AHA), the American College of Cardiology Foundation (ACCF), and the Heart Rhythm Society (HRS) have recommended using other formulae in place of QTcB since 2009. The Epic Electronic Health Record System (Epic Systems Corporation, Verona, WI) automatically populates the Fridericia formula (QTcFri) on hospital ECG reports without any provider calculation.

Methods

We aimed to retrospectively investigate the effect of QTcFri on one year of ECGs in the Epic Electronic Health Record (EHR) at a single tertiary care center. Inclusion criteria for ECG reports specified HR 60-120 bpm without QRS duration > 120 ms. Gathered data from Epic EHR ECG reports included patient age, sex, HR, QRS duration (QRSd), QT interval, QTcB, and QTcFri. EHR documented 61,946 ECG reports for the year, with 44,566 meeting criteria for inclusion. General statistical methods included range, median, mean, and standard deviation. Confidence intervals were assessed to maintain the fidelity of analysis. The normality of data distribution was assessed with Kolmogorov-Smirnov testing. The Wilcoxon rank-sum test was then performed to confirm a statistically significant difference between the Bazett and Fridericia formulae. The ∆QTc analysis was conducted on prolonged QTc (males > 450 ms; females > 460 ms) and severely prolonged QTc > 500 ms data subsets. A value of p<0.05 was interpreted as significant. Statistical analysis was performed using SPSS statistical software (IBM Statistics, v. 26; IBM Corp, Armonk, NY).

Results

The 44,566 ECG reports demonstrated 57% female gender and a mean age of 57 ± 17.5 years. The mean HR was 83 ± 14.7 bpm and the mean ∆QTc was 23 ± 12.9 ms shorter with QTcFri. Mean data showed minimal variation between sexes: age, heart rate, uncorrected QT, QTcB, QTcFri, and ∆QTc varied by less than 2%. Mean QRS varied by 4% between sexes. The Wilcoxon rank-sum test revealed 44,127 ranks with a negative difference, 0 ranks with a positive difference, and 439 ties, p <0.001 (99% CI: 22.5 ms, 23.0 ms). QTcB identified 37.4% (16665/44566) ECGs prolonged. Using QTcFri, 21% (9371/44566) of the total ECGs corrected to normal QTc (<450 ms (men) and 460 ms (women)). QTcFri use reduced the number of ECG reports with QTc > 500 ms by 57.3%. A total of 125 ECG reports, 117 females and eight males, corrected to normal gender-specific QTc with QTcFri. The mean decrease in QTc with the Fridericia formula when QTcB > 500 ms was 31 ± 14.5 ms (99% CI: 30.4 ms, 31.7 ms).

Conclusion

Our data from the Wilcoxon rank-sum analysis indicated that the EHR QTcFri analysis yields a statistically significant difference (p < 0.001) in QTc calculation of 22 ms over 44,566 ECG reports. The data showed a 21% reduction in inaccurately documented test results. The utilization of this resource will provide the most accurate and clinically relevant data to inform clinical decision-making. Accurate QT interval calculation will better inform downstream clinical decision-making through a wider scope of therapeutic intervention. This analysis is readily available to clinicians without calculation and its awareness will benefit patient care.

Wearable Devices for Precision Medicine and Health State Monitoring

Wearable technologies will play an important role in advancing precision medicine by enabling measurement of clinically-relevant parameters describing an individual's health state. The lifestyle and fitness markets have provided the driving force for the development of a broad range of wearable technologies that can be adapted for use in healthcare. Here we review existing technologies currently used for measurement of the four primary vital signs: temperature, heart rate, respiration rate, and blood pressure, along with physical activity, sweat, and emotion. We review the relevant physiology that defines the measurement needs and evaluate the different methods of signal transduction and measurement modalities for the use of wearables in healthcare.

Heart Rhythm Analyzed via Shapelets Distinguishes Sleep From Awake

Automatically determining when a person falls asleep from easily available vital signals is important, not just for medical applications but also for practical ones, such as traffic safety or smart homes. Heart dynamics and respiration cycle couple differently during sleep and awake. Specifically, respiratory modulation of heart rhythm or respiratory sinus arrhythmia (RSA) is more prominent during sleep, as both sleep and RSA are connected to strong vagal activity. The onset of sleep can be recognized or even predicted as the increase of cardio-respiratory coupling. Here, we employ this empirical fact to design a method for detecting the change of consciousness status (sleep/awake) based only on heart rate variability (HRV) data. Our method relies on quantifying the (self)similarity among shapelets - short chunks of HRV time series - whose "shapes" are related to the respiration cycle. To test our method, we examine the HRV data of 75 healthy individuals recorded with microsecond precision. We find distinctive patterns stable across age and sex, that are not only indicative of sleep and awake, but allow to pinpoint the change from awake to sleep almost immediately. More systematic analysis along these lines could lead to a reliable prediction of sleep.

Fetal electrocardiography ST-segment analysis for intrapartum monitoring: a critical appraisal of conflicting evidence and a way forward

BACKGROUND

In the past century, some areas of obstetric including intrapartum care have been slow to benefit from the dramatic advances in technology and medical care. Although fetal heart rate monitoring (cardiotocography) became available a half century ago, its interpretation often differs between institutions and countries, its diagnostic accuracy needs improvement, and a technology to help reduce the unnecessary obstetric interventions that have accompanied the cardiotocography is urgently needed.

STUDY DESIGN

During the second half of the 20th century, key findings in animal experiments captured the close relationship between myocardial glycogenolysis, myocardial workload, and ST changes, thus demonstrating that ST waveform analysis of the fetal electrocardiogram can provide information on oxygenation of the fetal myocardium and establishing the physiological basis for the use of electrocardiogram in intrapartum fetal surveillance.

RESULTS

Six randomized controlled trials, 10 meta-analyses, and more than 20 observational studies have evaluated the technology developed based on this principle. Nonetheless, despite this intensive assessment, differences in study protocols, inclusion criteria, enrollment rates, clinical guidelines, use of fetal blood sampling, and definitions of key outcome parameters, as well as inconsistencies in randomized controlled trial data handling and statistical methodology, have made this voluminous evidence difficult to interpret. Enormous resources spent on randomized controlled trials have failed to guarantee the generalizability of their results to other settings or their ability to reflect everyday clinical practice.

CONCLUSION

The latest meta-analysis used revised data from primary randomized controlled trials and data from the largest randomized controlled trials from the United States to demonstrate a significant reduction of metabolic acidosis rates by 36% (odds ratio, 0.64; 95% confidence interval, 0.46-0.88) and operative vaginal delivery rates by 8% (relative risk, 0.92; 95% confidence interval, 0.86-0.99), compared with cardiotocography alone.

Discriminative Ratio of Spectral Power and Relative Power Features Derived via Frequency-Domain Model Ratio With Application to Seizure Prediction

The ratio of spectral power in two different bands and relative band power have been shown to be sometimes more discriminative features than the spectral power in a specific band for binary classification of a time series for seizure prediction. However, why and which ratio of spectral power and relative power features are better discriminators than a band power have not been understood. While general answers to why and which are difficult, this paper partially addresses the answer to these questions. Using auto-regressive modeling, this paper, for the first time, theoretically explains that for high signal-to-noise ratio (SNR) cases, the ratio features may sometime amplify the discriminability of one of the two states in a time series, as compared with a band power. This paper, also for the first time, introduces a novel frequency-domain model ratio (FDMR) that can be used to select the two frequency bands. The FDMR computes the ratio of the frequency responses of the two auto-regressive model filters that correspond to two different states. It is shown that the ratio implicitly cancels the effect of change of variance of the white noise that is input to the auto-regressive model in a non-stationary environment for high SNR conditions. It is also shown that under certain sufficient but not necessary conditions, the ratio of the spectral power and the relative band power, i.e., the band power divided by the total power spectral density, can be better discriminators than band power. Synthesized data and scalp EEG data from the MIT Physionet for patient-specific seizure prediction are used to explain why the ratios of spectral power obtained by a ranking algorithm in the prior literature satisfy the sufficient conditions for amplification of the ratio feature derived in this paper.

Multichannel electrocardiogram diagnostics for the diagnosis of arrhythmogenic right ventricular dysplasia

Aims

The identification of arrhythmogenic right ventricular dysplasia (ARVD) from 12-channel standard electrocardiogram (ECG) is challenging. High density ECG data may identify lead locations and criteria with a higher sensitivity.

Methods and results

Eighty-channel ECG recording from patients diagnosed with ARVD and controls were quantified by magnitude and integral measures of QRS and T waves and by a measure (the average silhouette width) of differences in the shapes of the normalized ECG cycles. The channels with the best separability between ARVD patients and controls were near the right ventricular wall, at the third intercostal space. These channels showed pronounced differences in P waves compared to controls as well as the expected differences in QRS and T waves.

Conclusion

Multichannel recordings, as in body surface mapping, add little to the reliability of diagnosing ARVD from ECGs. However, repositioning ECG electrodes to a high anterior position can improve the identification of ECG variations in ARVD. Additionally, increased P wave amplitude appears to be associated with ARVD.

Epsilon wave: A review of historical aspects

The epsilon wave of the electrocardiogram (ECG) together with fragmented QRS (fQRS), the terminal conduction delay, incomplete right bundle branch block (IRBBB) and complete/advanced RBBB (CRBBB) of peripheral origin are part of a spectrum of ventricular depolarization abnormalities of arrhythmogenic cardiomyopathy(AC). Although the epsilon wave is considered a major diagnostic criterion for AC since 2010 (AC Task Force Criteria), its diagnostic value is limited because it is a sign of the later stage of the disease. It would be more appropriate to say that the epsilon wave is a “hallmark” of AC, but is of low diagnostic sensitivity. Although the epsilon wave has high specificity for AC, it can be present in other pathological conditions. In this update we will cover the nomenclature, association with disease states and electrocardiographic aspects of the epsilon wave.

High risk of heart failure associated with desmoglein-2 mutations compared to plakophilin-2 mutations in arrhythmogenic right ventricular cardiomyopathy/dysplasia

BACKGROUND

Previous studies suggested that genetic status affects the clinical course of arrhythmogenic right ventricular cardiomyopathy/dysplasia (ARVC/D) patients. The aim of this study was to compare the outcome of desmoglein-2 (DSG2) mutation carriers to those who carry the plakophilin-2 (PKP2) mutation, the most common ARVC/D-associated gene.

METHODS AND RESULTS

Consecutive ARVC/D patients carrying a pathogenic mutation in PKP2 or DSG2 were selected from a national ARVC/D registry. The cumulative freedom from sustained ventricular arrhythmia and cardiac transplantation/death from heart failure (HF) during follow-up was assessed, compared between PKP2 and DSG2, and predictors for ventricular arrhythmia and HF events determined. Overall, 118 patients from 78 families were included: 27 (23%) carried a DSG2 mutation and 91 (77%) a PKP2 mutation. There were no significant differences between DSG2 and PKP2 mutation carriers concerning gender, proband status, age at diagnosis, T-wave inversion, or right ventricular dysfunction at baseline. DSG2 patients displayed more frequent epsilon wave (37% vs. 17%, P = 0.048) and left ventricular dysfunction at diagnosis (54% vs. 10%, P < 0.001). During a median follow-up of 5.6 years (2.5-16), DSG2 and PKP2 mutation carriers displayed a similar risk of sustained ventricular arrhythmia (log-rank P = 0.20), but DSG2 mutation carriers were at higher risk of transplantation/HF-related death (log-rank P < 0.001). The presence of a DSG2 mutation vs. PKP2 mutation was a predictor of transplantation/HF-related death in univariate Cox analysis (P = 0.0005).

CONCLUSIONS

In this multicentre cohort, DSG2 mutation carriers were found to be at high risk of end-stage HF compared to PKP2 mutation carriers, supporting careful haemodynamic monitoring of these patients. The benefit of early HF treatment needs to be assessed in DSG2 carriers.

Diagnostic and therapeutic strategies for arrhythmogenic right ventricular dysplasia/cardiomyopathy patient

Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVD/C) is a rare inherited heart muscle disease characterized by ventricular tachyarrhythmia, predominant right ventricular dysfunction, and sudden cardiac death. Its pathophysiology involves close interaction between genetic mutations and exposure to physical activity. Mutations in genes encoding desmosomal protein are the most common genetic basis. Genetic testing plays important roles in diagnosis and screening of family members. Syncope, palpitation, and lightheadedness are the most common symptoms. The 2010 Task Force Criteria is the standard for diagnosis today. Implantation of a defibrillator in high-risk patients is the only therapy that provides adequate protection against sudden death. Selection of patients who are best candidates for defibrillator implantation is challenging. Exercise restriction is critical in affected individuals and at-risk family members. Antiarrhythmic drugs and ventricular tachycardia ablation are valuable but palliative components of the management. This review focuses on the current diagnostic and therapeutic strategies in ARVD/C and outlines the future area of development in this field.

Epsilon waves: Milestones in the discovery and progress

The Epsilon wave was first identified in 1977. Four decades of progress help people to better understand its pathological electrogenesis and diagnostic value. Currently, the Epsilon wave is on the list of the 2010 Task Force recommendations for the diagnosis of arrhythmogenic right ventricular dysplasia (ARVD). In this review, we provide the history of the first recording of the Epsilon wave in coronary artery disease and Uhl's anomaly, subsequently leading to the signal averaging technique to record late potentials. Based on our experience, we discuss some existing controversies. When we look back at the decades of progress of the Epsilon wave, we conclude that the Epsilon wave is only the tip of the iceberg of ECG abnormalities in ARVD.

Value of a novel 16-lead High-Definition ECG machine to detect conduction abnormalities in structural heart disease

BACKGROUND

Depolarization abnormalities are hardly detectable by standard 12-lead electrocardiogram (ECG) in some patients.

OBJECTIVE

To evaluate the value of the 16-lead High-Definition (HD)-ECG machine to record conduction abnormalities including Epsilon waves in patients with structural heart disease.

METHODS

Tracings with 12-lead ECG, 16-lead HD-ECG, and signal-averaged ECG were studied.

RESULTS

(1) Case of severe coronary artery disease (CAD): On 16-lead HD-ECG, a tiny intra-QRS signal was noted in lead III, a prolonged P wave in lead II, and fragmentation on top of lead aVL and lead aVF. Proper automatic measurement of the prolonged P wave measuring 190 ms was noted. Signal-averaging by 16-lead HD-ECG in lead III showed the intra-QRS fragmentation and P wave prolongation of 180 ms. (2) First patient with arrhythmogenic right ventricular dysplasia (ARVD): Standard 12-lead ECG indicated Epsilon waves in lead III, V2, V3, and inverted T waves in V1-V3. 16-lead HD-ECG indicated QRS prolongation in lead II, III, aVL, aVF, V2, V3 as opposed to V6, and low amplitudes of QRS complexes in V4R and V3R as a new possible sign of ARVD. Notches in lead V2, widening of QRS complexes in all precordial leads, but shorter QRS in V8-V9 are also considered as a potential new diagnostic sign of ARVD. (3) Second ARVD patient: Notches at the end of the QRS in lead III and a negative initial deflection of the QRS in V1 and V2 were detected by standard 12-lead ECG. On 16-lead HD-ECG, a more pronounced QRS fragmentation was visible.

CONCLUSION

16-lead HD-ECG in both CAD and ARVD seems to be more sensitive than 12-lead ECG to record electrocardiographic abnormalities.

Electrocardiographic Markers of Sudden Cardiac Death (Including Left Ventricular Hypertrophy)

Although the electrocardiograph (ECG) was invented more than 100 years ago, it remains the most commonly used test in clinical medicine. It is easy to perform, relatively cheap, and results are readily available. Interpretation, however, needs expertise and knowledge. New data, phenomenon, and syndromes are continually discovered by the ECG. It is important to differentiate between normal and abnormal ECGs first and then try to correlate the findings with clinical pathologies. Furthermore, the ECG is an integral part of the screening model for a variety of conditions such as channelopathies, athletes, preoperative risk profile, and remains the cardiologist's best friend.

Individual identification via electrocardiogram analysis

BACKGROUND

During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research.

METHODS

We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents.

RESULTS

100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %.

CONCLUSIONS

Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Heart rate variability and heart rate recovery as prognostic factors

Background and aim

Heart rate (HR) can appear static and regular at rest, during exercise or recovery after exercise. However, HR is constantly adjusted due to factors such as breathing, blood pressure control, thermoregulation and the renin-angiotensin system, leading to a more dynamic response that can be quantified using HRV (heart rate variability). HRV is defined as the deviation in time between successive normal heart beat and is a noninvasive method to measure the total variation in a number of HR interval. HRV can serve as measure of autonomic activity of sino-atrial node. The aim of the study was to determine the influence of certain clinical and paraclinical parameters on heart rate recovery after exercise in patients with ischemic heart disease and the relation with HRV using 24 h Holter monitoring.

Methods

The study included 46 patients who were submitted to cardiovascular exercise stress test and also to 24 h Holter EKG monitoring. Subjects had a mean age of 56.2±11.2 years, with a minimum of 25 and a maximum of 79 years. The study included 22 (47.8%) men and 24 (52.2%) women. Statistical analysis was performed using MedCalc software version 14.8.1. Multivariate analysis consisted of the construction of several multiple linear regression models. A p value of 0.05 was considered statistically significant.

Results

The HRV values (time domain) were all lower in the IHD compared with the group without coronary heart disease, even if the difference is not statistically significant. Also rest and maximal HR values were similar but during the test varies in the sense that those with IHD had higher values of rest and maximal HR and lower HRR, but not statistically significant.

Conclusions

HRV is a very easy and safe method if there is an available device and it is used for evaluation of the autonomic nervous system in many cardiovascular diseases, but also in other pathologies.

In uncomplicated ischemic heart disease HRV is depressed, but not significant. HRR, which is also considered an indicator of the parasympathetic activity after exercise termination, is also non-significantlly decreased in ischemic patients and the correlation between them is weak. Both HRV and HRR parameters can be easily measured, but the best algorithm of this issue requires further studies, conducted in larger patient populations. Although HRR and HRV are tools to measure the autonomic nervous system activity the relation between them need more studies to be able to quantify the arrhythmogenic risk.

The electrocardiographic manifestations of arrhythmogenic right ventricular dysplasia

The ECG is abnormal in most patients with arrhythmogenic right ventricular dysplasia (ARVD). Right ventricular parietal block, reduced QRS amplitude, epsilon wave, T wave inversion in V1-3 and ventricular tachycardia in the morphology of left bundle branch block are the characteristic changes that reflect the underlying genetic predetermined pathology and pathoelectrophysiology. Recognizing the characteristic ECG changes in ARVD will be of help in making a correct diagnosis of this rare disease.

Frequency-multiplication high-output triboelectric nanogenerator for sustainably powering biomedical microsystems

An attractive method to response the current energy crisis and produce sustainable nonpolluting power source is harvesting energy from our living environment. However, the energy in our living environment always exists in low-frequency form, which is very difficult to be utilized directly. Here, we demonstrated a novel sandwich-shape triboelectric nanogenerator to convert low-frequency mechanical energy to electric energy with double frequency. An aluminum film was placed between two polydimethylsiloxane (PDMS) membranes to realize frequency multiplication by twice contact electrifications within one cycle of external force. The working mechanism was studied by finite element simulation. Additionally, the well-designed micro/nano dual-scale structures (i.e., pyramids and V-shape grooves) fabricated atop PDMS surface was employed to enhance the device performance. The output peak voltage, current density, and energy volume density achieved 465 V, 13.4 μA/cm(2), and 53.4 mW/cm(3), respectively. This novel nanogenerator was systematically investigated and also demonstrated as a reliable power source, which can be directly used to not only lighten five commercial light-emitting diodes (LEDs) but also drive an implantable 3-D microelectrode array for neural prosthesis without any energy storage unit or rectification circuit. This is the first demonstration of the nanogenerator for directly driving biomedical microsystems, which extends the application fields of the nanogenerator and drives it closer to practical applications.

A simple device to illustrate the Einthoven triangle

The Einthoven triangle is central to the field of electrocardiography, but the concept of cardiac vectors is often a difficult notion for students to grasp. To illustrate this principle, we constructed a device that recreates the conditions of an ECG reading using a battery to simulate the electrical vector of the heart and three voltmeters for the main electrocardiographic leads. Requiring minimal construction with low cost, this device provides hands-on practice that enables students to rediscover the principles of the Einthoven triangle, namely, that the direction of the cardiac dipole can be predicted from the deflections in any two leads and that lead I + lead III = lead II independent of the position of heart's electrical vector. We built a total of 6 devices for classes of 30 students and tested them in the first-year Human Physiology course at the University of California-Davis School of Medicine. Combined with traditional demonstrations with ECG machines, this equipment demonstrated its ability to help medical students obtain a solid foundation of the basic principles of electrocardiography.

Heart rate variability - a historical perspective

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval - the heart period, has become a popular clinical and investigational tool. The temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration - the so called respiratory sinus arrhythmia, RSA) and are widely believed to reflect changes in cardiac autonomic regulation. Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease. It is the purpose of this essay to provide an historical overview of the evolution in the concept of HRV. Briefly, pulse rate was first measured by ancient Greek physicians and scientists. However, it was not until the invention of the "Physician's Pulse Watch" (a watch with a second hand that could be stopped) in 1707 that changes in pulse rate could be accurately assessed. The Rev. Stephen Hales (1733) was the first to note that pulse varied with respiration and in 1847 Carl Ludwig was the first to record RSA. With the measurement of the ECG (1895) and advent of digital signal processing techniques in the 1960s, investigation of HRV and its relationship to health and disease has exploded. This essay will conclude with a brief description of time domain, frequency domain, and non-linear dynamic analysis techniques (and their limitations) that are commonly used to measure HRV.

On-chip constructive cell-network study (II): on-chip quasi-in vivo cardiac toxicity assay for ventricular tachycardia/fibrillation measurement using ring-shaped closed circuit microelectrode with lined-up cardiomyocyte cell network

BACKGROUNDS

Conventional in vitro approach using human ether-a-go-go related gene (hERG) assay has been considered worldwide as the first screening assay for cardiac repolarization safety. However, it does not always oredict the potential QT prolongation risk or pro-arrhythmic risk correctly. For adaptable preclinical strategiesto evaluate global cardiac safety, an on-chip quasi-in vivo cardiac toxicity assay for lethal arrhythmia (ventricular tachyarrhythmia) measurement using ring-shaped closed circuit microelectrode chip has been developed.

RESULTS

The ventricular electrocardiogram (ECG)-like field potential data, which includes both the repolarization and the conductance abnormality, was acquired from the self-convolutied extracellular field potentials (FPs) of a lined-up cardiomyocyte network on a circle-shaped microelectrode in an agarose microchamber. When Astemisol applied to the closed-loop cardiomyocyte network, self-convoluted FP profile of normal beating changed into an early afterdepolarization (EAD) like waveform, and then showed ventricular tachyarrhythmias and ventricular fibrilations (VT/Vf). QT-prolongation-like self-convoluted FP duration prolongation and its fluctuation increase was also observed according to the increase of Astemizole concentration.

CONCLUSIONS

The results indicate that the convoluted FPs of the quasi-in vivo cell network assay includes both of the repolarization data and the conductance abnormality of cardiomyocyte networks has the strong potential to prediction lethal arrhythmia.

Demonstration of remote optical measurement configuration that correlates to glucose concentration in blood

An optical approach allowing the extraction and the separation of remote vibration sources has recently been proposed. The approach has also been applied for medical related applications as blood pressure and heart beats monitoring. In this paper we demonstrate its capability to monitor glucose concentration in blood stream. The technique is based on the tracking of temporal changes of reflected secondary speckle produced in human skin (wrist) when being illuminated by a laser beam. A temporal change in skin's vibration profile generated due to blood pulsation is analyzed for estimating the glucose concentration. Experimental tests that were carried out in order to verify the proposed approach showed good match with the change of the glucose level at the positive slope stage as it was obtained from conventional reference measurement.