Neonatal electronic cardiac monitoring in labor and delivery room (DR-ECG) is shown to be sustained at a tertiary care regional perinatal center despite COVID-19 and other complex organizational challenges. In this contemporary cohort, initial increase in chest compressions at birth associated with the introduction of DR-ECG monitoring was mitigated by focused educational interventions on effective ventilation with no difference in neonatal mortality. DR-ECG may help our understanding of human and system factors, identify potential better practices for optimal resuscitation team performance and assess impact of targeted training initiatives on clinical outcomes.

Myocardial infarction is an uncommon manifestation in anomalous coronary artery (ACA). As usual, the electrocardiogram (ECG) was used to detect myocardial infarction. Unfortunately, the ECG’s pattern could not reveal the ACA’s condition. Some investigators tried to describe the pattern in ECG in ACA’s patients. Due to the rare patients of ACA, it was not yet found.

Stress has been identified as one of the major causes of automobile crashes which then lead to high rates of fatalities and injuries each year. Stress can be measured via physiological measurements and in this study the focus will be based on the features that can be extracted by common wearable devices. Hence the study will be mainly focusing on the heart rate variability (HRV). This study is aimed to develop a good predictive model that can accurately classify stress levels from ECG-derived HRV features, obtained from automobile drivers, testing different machine learning methodologies such as K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Random Forest (RF) and Gradient Boosting (GB). Moreover, the models obtained with highest predictive power will be used as reference for the development of a machine learning model that would be used to classify stress from HRV features derived from HRV measurements obtained from wearable devices. We demonstrate that MLP was the ideal stress classifier by achieving a Recall of 80%. The proposed method can be also used on all applications in which is important to monitor the stress level e. g. in physical rehabilitation, anxiety relief or mental wellbeing.

Understanding how cannabis affects our heart is like navigating through a maze of conflicting findings. While it's known for its diverse uses, from medicinal to industrial, cannabis also raises red flags regarding its impact on heart health. This review dives into the intricate relationship between cannabis use and cardiovascular well-being, particularly focusing on changes seen in electrocardiogram (ECG) readings, which serve as vital indicators of heart function and rhythm. When someone tokes up, their heart might race temporarily, but chronic cannabis use paints a darker picture, with potential risks of heart attacks and strokes looming on the horizon. The ECG alterations observed in cannabis users, such as irregular heartbeats and unusual wave patterns, hint at disruptions in the heart's normal workings. Furthermore, cannabis has been linked to prolonging the QT interval in the heart's electrical cycle, potentially setting the stage for dangerous arrhythmias. Amidst this mixed bag of effects, some studies even suggest that tiny amounts of THC, the main psychoactive component of cannabis, might lend a helping hand to the heart. However, caution flags are raised, especially for vulnerable groups. While researchers strive to unravel the mysteries of cannabis and the heart, limitations in current studies remind us that we're still far from having all the answers. Moving forward, future investigations should delve deeper into understanding how cannabis tinkers with our heart's inner workings, especially across different demographics. Armed with this knowledge, we can better tailor interventions to safeguard the heart health of cannabis users.

The electrocardiogram (ECG) was the first biomedical signal where digital signal processing techniques were extensively applied. By its own nature, the ECG is typically a sparse signal, composed of regular activations (the QRS complexes and other waveforms, like the P and T waves) and periods of inactivity (corresponding to isoelectric intervals, like the PQ or ST segments), plus noise and interferences. In this work, we describe an efficient method to construct an overcomplete and multi-scale dictionary for sparse ECG representation using waveforms recorded from real-world patients. Unlike most existing methods (which require multiple alternative iterations of the dictionary learning and sparse representation stages), the proposed approach learns the dictionary first, and then applies an efficient sparse inference algorithm to model the signal using the learnt dictionary. As a result, our method is much more efficient from a computational point of view than other existing methods, thus becoming amenable to deal with long recordings from multiple patients. Regarding the dictionary construction, we locate first all the QRS complexes in the training database, then we compute a single average waveform per patient, and finally we select the most representative waveforms (using a correlation-based approach) as the basic atoms that will be resampled to construct the multi-scale dictionary. Simulations on real-world records from Physionet's PTB database show the good performance of the proposed approach.

This study aimed to evaluate the role of chest radiographs and electrocardiograms in predicting the hemodynamics of congenital heart disease (CHD). This retrospective study included 50 patients with a diagnosis of CHD who had undergone any form of cardiac intervention, either surgical or nonsurgical between September 2019 and September 2020. Chest radiographs and electrocardiograms were evaluated and compared with the diagnostic gold standard echocardiography. Chest radiographs had the highest sensitivity, specificity, and accuracy, with all being 100%, in detecting situs and cardiac position. There was a very good agreement between chest radiographs and echocardiography in the detection of both situs and cardiac position (κ = 1.00, p < 0.001), while moderate agreement was observed for the detection of cardiomegaly, position of aortic knuckle, main pulmonary artery dilation, and right pulmonary artery dilation. Electrocardiograms had a high sensitivity (100.00%), but modest specificity and accuracy for the detection of left ventricle pressure overload. For the detection of left atrial enlargement and left ventricle volume overload, electrocardiograms had high specificity (94.12% and 94.29%, respectively) but low sensitivity and modest accuracy. There was a moderate agreement between electrocardiograms and echocardiography in the detection of right ventricle pressure overload (κ = 0.43, p = 0.002) and left ventricle volume overload (κ = 0.46, p < 0.001). The study findings indicate that chest radiographs and electrocardiograms alone are not adequate for the assessment of hemodynamics of CHD and reinstates the recommendation that in addition to routine chest radiographs and electrocardiograms, echocardiography should be performed.

The present article seeks to study whether there is any relationship between the equation of the rhythmic figures (Lugos Abarca, 2023) and cardiac auscultation using Field's method, which consists of representing cardiac sounds by means of musical notation (Field, 2010). A new meaning is given to the equation, concluding a mathematical equality between the equation of the rhythmic figures and the formula for calculating the RR Interval.

A 57-year-old male with a history of hyperlipidemia and mild pulmonary hypertension presented for a left inguinal hernia repair. Preoperative electrocardiogram showed severe right axis deviation at 177 degrees and abnormal repolarization. Right ventricular hypertrophy secondary to pulmonary hypertension was the primary differential but was quickly ruled out due to the patient being asymptomatic and having great exercise tolerance. Dextrocardia was also ruled out due to normal progression on the precordial leads. The simultaneous severe right axis deviations of P, R, and T waves raised suspicion for erroneous lead placement. The repeat electrocardiogram confirmed the diagnosis.

Background: the study aimed at comparing the prognostic importance of Heart Rate (HR) and of QT corrected (QTc) according to Fridericia, Framingham and Bazett methods, in a large non-selected population. Methods: the analysis of digital Electrocardiograms archived from 2008 to 2022 in the metropolitan area of Modena, Italy, was made. The population under study was divided into three age-groups and survival analysis was performed. Results: 131.627 patients were enrolled and during the follow-up (mean 1641.4 days), all-cause mortality was 8.9%. Both HR and QTc were associated with mortality. All-cause mortality significantly increased from HR values greater than 81 BPM and from QTc values greater than 440 msec in young subjects and 455 msec in old subjects (values of the 75th percentiles/optimal operating point). Cox analysis confirmed the better prognostic value of Bazett’s QTc and of HR in the whole population and in the three age-groups. Conclusion: Bazett’s method performed better than other methods but unexpectedly HR had the same or a better correlation with all-cause mortality. Since HR is simple and readily available, its evaluation should be improved. However, reference values for QTc and for HR are difficult to define causing many confounding factors and further population-study are required.

Background: The use of wearable devices has burgeoned over the past decade, including wearables for infants and young children. Typically, such devices assess a single modality, and few have undergone scientific validation. To address this gap, we developed an infant wearable platform, LittleBeats™, that integrates electrocardiogram (ECG), motion, and audio sensors on a single printed circuit board to permit daylong remote assessments of infants and caregivers in home environments. Objective: Complementing our prior reports focused on LittleBeats™ data collected among infants and children in the home context, our main objective here is to present a technical validation of each sensor modality against established laboratory protocols and gold-standard equipment. Materials and Methods: We conducted five studies, including assessments of (a) interbeat interval (IBI) data obtained from the LittleBeats™ ECG sensor versus the gold-standard BIOPAC among adults (Study 1, N=16) and infants (Study 2, N=5), (b) performance of automated activity recognition (upright vs. walk vs. glide vs. squat ) among adults using accelerometer data obtained from LittleBeats™ versus Google Pixel 1 smartphone (Study 3, N=12), and (c) performance of speech emotion recognition (SER; Study 4, N=8) and automatic speech recognition (ASR; Study 5, N =12) algorithms among adult samples using audio data from LittleBeats™ versus smartphone. Results: Results for IBI data obtained from the LittleBeats™ ECG sensor indicate acceptable mean absolute percent error (MAPE) rates for both the adults (MAPE = 5.29% to 5.97%; Study 1) and infants (MAPE =0.96% to 1.66%; Study 2) across low- and high-challenge sessions, as well as expected patterns of change in respiratory sinus arrythmia (RSA) across sessions. For activity recognition (Study 3), LittleBeats™ data showed good to excellent performance (Accuracy = 89%, F1-score=88%; Cohen’s kappa =.79), although the smartphone outperformed LittleBeats™ by less than 4%. Finally, performance on the SER applied to LittleBeats™ versus smartphone audio data (Study 4) indicated comparable performance, with a matched-pairs test indicating no significant difference in error rates (p=.26). On the ASR task (Study 5), the best performing algorithm yielded relatively low word error rates, although LittleBeats™ (4.16%) versus smartphone (2.73%) error rates are somewhat higher. Discussion: Taken together, results from these controlled laboratory studies conducted predominantly with adults indicate that the LittleBeats™ sensors yield data quality that is largely comparable to those obtained from gold-standard devices and protocols that have been used extensively in prior research. The advantages of the LittleBeats™ platform is the (a) integration of multiple sensors into one platform that was (b) designed specifically for use with infants and young children. Leveraging data from the multiple modalities and fine-tuning postprocessing steps to further increase data quality, combined with assessing data quality among infants and young children in their natural environments, will be key next steps in this research program.

Despite the 12-lead electrocardiogram (ECG) being the gold standard in evaluating clinical presentations of arrythmias of various kinds, several notable limitations, namely in accuracy and localization, have recently garnered concern with respect to administering appropriate therapeutic measures. The following text puts these limitations under the spotlight, highlighting the difficulty and challenging nature of standard ECG analysis and therapeutic measures, while simultaneously showing a novel body surface electrocardiographic mapping system that utilizes 3D spatial imaging and pinpoint localization algorithms in order to compensate for the shortcomings of the standard ECG, thereby providing higher precision data collection. We provide a brief overview of the novel device in a selection of arrhythmic demographics, along with its validity and reliability, while illustrating the benefits and positive outcomes of its utility.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion even early warning for physicians, however, they are mutually-exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. Long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the 3rd China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

(1) Background: Sudden Infant Death Syndrome (SIDS) represents unexplained and sudden deaths during the sleep of infants under one year of age, despite thorough investigation. Screening for prolonged QTc or Long QT Syndrome (LQTS) should be conducted on all newborns to reduce the incidence of SIDS. Neonatal electrocardiogram (ECG) could identify congenital heart defects (CHD) early, especially those not detected at birth. Infants with prolonged QT intervals typically undergo genetic analysis for Long QT Syndrome; (2) Methods: The study involved infants aged 20-40 days, born clinically healthy, with initial ECG screening. Infants with prenatal diagnoses or signs/symptoms of CHD identified immediately after birth, as well as those who underwent ECG or echocardiogram for other clinical or historical indications, were excluded from the study. The data were analyzed using SPSS version 22.0; (3) Results: Of the 42,200 infants involved, 2,245 were enrolled, with 39.9% being males. Subsequently, 164 children (37.8% males) with prolonged QT intervals underwent further diagnostic investigations. Genetic mutations were identified in 11 out of 18 tested children. The most common mutations were LQT1 (54.5%), LQT2 (36.4%), and LQT3 (1 patient). Medications used included propranolol (39.8%), nadolol (22.2%), inderal (11.1%), metoprolol (11.1%), and no treatment (16.7%). The most common abnormalities were focal right bundle branch block (54.5%), left axis deviation (9.2%), and nonspecific ventricular repolarization abnormalities (7.1%). Multiple anomalies were found in 0.47% of children with focal right bundle branch block. Structural abnormalities were associated with specific features in 267 patients (11.9%), primarily isolated patent foramen ovale (PFO) at 61.4%; (4) Conclusions: The screening has proven effective in early diagnosing Long QT Syndrome and other cardiac rhythm anomalies, with additional benefits in detecting mutations and/or QTc prolongations in family members. Identifying other ECG abnormalities and congenital heart malformations further enhances the benefits of the screening.

With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.