Search Results (129)

Background: Obstructive sleep apnea is a sleep disorder that is linked to many health complications and can even be lethal in its severe form. Overnight polysomnography is the gold standard for diagnosing apnea, which is expensive, time-consuming, and requires manual analysis by a sleep expert. Artificial intelligence (AI)-embedded wearable device as a portable and less intrusive monitoring system is a highly desired alternative to polysomnography. However, AI models often require substantial storage capacity and computational power for edge inference which makes it a challenging task to implement the models in hardware with memory and power constraints. Methods: This study demonstrates the implementation of depth-wise separable convolution (DSC) as a resource-efficient alternative to spatial convolution (SC) for real-time detection of apneic activity. Single lead electrocardiogram (ECG) and oxygen saturation (SpO₂) signals were acquired from the PhysioNet databank. Using each type of convolution, three different models were developed using ECG, SpO₂, and model fusion. For both types of convolutions, the fusion models outperformed the models built on individual signals across all the performance metrics. Results: Although the SC-based fusion model performed the best, the DSC-based fusion model was 9.4, 1.85, and 11.3 times more energy efficient than SC-based ECG, SpO₂, and fusion models, respectively. Furthermore, the accuracy, precision, and specificity yielded by the DSC-based fusion model were comparable to those of the SC-based individual models (~95%, ~94%, and ~94%, respectively). Conclusions: DSC is commonly used in mobile vision tasks, but its potential in clinical applications for 1-D signals remains unexplored. While SC-based models outperform DSC in accuracy, the DSC-based model offers a more energy-efficient solution with acceptable performance, making it suitable for AI-embedded apnea detection systems. Full article

Sudden cardiac death (SCD) represents a critical acute cardiovascular event characterized by rapid onset of cardiac and respiratory arrest, posing a significant threat to patients due to its high fatality rate. Monitoring indices related to SCD using wearable devices holds profound implications for preemptive measures aimed at reducing the incidence of such life-threatening events. Hence, this study proposed a predictive algorithm for SCD leveraging single-lead electrocardiogram (ECG) signals featuring low signal-to-noise ratios. Initially, simulated electrode motion artifact noise was introduced to ideal ECG signals to emulate the signal conditions with low signal-to-noise ratios encountered in everyday scenarios. To meet the criteria of simplicity and cost-effectiveness required for wearable devices, the analysis focused exclusively on single-lead signals. The proposed algorithm in this study employed a lightweight machine learning approach to extract 12-dimensional features encompassing ventricular late potentials, T-wave electrical alternation, and corrected QT intervals from the signal. The algorithm achieved an average prediction accuracy of 93.22% within 30 min prior to SCD onset, and 95.43% when utilizing a normal sinus rhythm database as a control, demonstrating robust performance. Additionally, a comprehensive Sudden Cardiac Death Index (SCDI) was devised to quantify the risk of SCD, formulated by integrating pivotal two-dimensional features contributing significantly to the algorithm. This index effectively distinguishes high-risk signals indicative of SCD from normal signals, thereby offering valuable supplementary insights in clinical settings. Full article

Early and highly precise detection is essential for delaying the progression of coronary artery disease (CAD). Previous methods primarily based on single-modal data inherently lack sufficient information that compromises detection precision. This paper proposes a novel multi-modal learning method aimed to enhance CAD detection by integrating ECG, PCG, and coupling signals. A novel coupling signal is initially generated by operating the deconvolution of ECG and PCG. Then, various entropy features are extracted from ECG, PCG, and its coupling signals, as well as recurrence deep features also encoded by integrating recurrence plots and a parallel-input 2-D CNN. After feature reduction and selection, final classification is performed by combining optimal multi-modal features and support vector machine. This method was validated on simultaneously recorded standard lead-II ECG and PCG signals from 199 subjects. The experimental results demonstrate that the proposed multi-modal method by integrating all signals achieved a notable enhancement in detection performance with best accuracy of 95.96%, notably outperforming results of single-modal and joint analysis with accuracies of 80.41%, 86.51%, 91.44%, and 90.42% using ECG, PCG, coupling signal, and joint ECG and PCG, respectively. This indicates that our multi-modal method provides more sufficient information for CAD detection, with the coupling information playing an important role in classification. Full article

Recent studies highlight artificial intelligence’s ability to identify ventricular dysfunction via electrocardiograms (ECGs); however, specific indicative waveforms remain unclear. This study analysed ECG and echocardiography data from 17,422 cases in Japan and Germany. We developed 10-layer convolutional neural networks to detect left ventricular ejection fractions below 50%, using four-fold cross-validation. Model performance, evaluated among different ECG configurations (3 s strips, single-beat, and two-beat overlay) and segments (PQRST, QRST, P, QRS, and PQRS), showed two-beat ECGs performed best, followed by single-beat models, surpassing 3 s models in both internal and external validations. Single-beat models revealed limb leads, particularly I and aVR, as most indicative of dysfunction. An analysis indicated segments from QRS to T-wave were most revealing, with P segments enhancing model performance. This study confirmed that dual-beat ECGs enabled the most precise ventricular function classification, and segments from the P- to T-wave in ECGs were more effective for assessing ventricular dysfunction, with leads I and aVR offering higher diagnostic utility. Full article

Cardiovascular diseases pose a significant global health threat, with atrial fibrillation representing a critical precursor to more severe heart conditions. In this work, a multimodality-based deep learning model has been developed for diagnosing atrial fibrillation using an embedded system consisting of a Raspberry Pi 4B, an ESP8266 microcontroller, and an AD8232 single-lead ECG sensor to capture real-time ECG data. Our approach leverages a deep learning model that is capable of distinguishing atrial fibrillation from normal ECG signals. The proposed method involves real-time ECG signal acquisition and employs a multimodal model trained on the PTB-XL dataset. This model utilizes a multi-step approach combining a CNN–bidirectional LSTM for numerical ECG series tabular data and VGG16 for image-based ECG representations. A fusion layer is incorporated into the multimodal CNN-BiLSTM + VGG16 model to enhance atrial fibrillation detection, achieving state-of-the-art results with a precision of 94.07% and an F1 score of 0.94. This study demonstrates the efficacy of a multimodal approach in improving the real-time diagnosis of cardiovascular diseases. Furthermore, for edge devices, we have distilled knowledge to train a smaller student model, CNN-BiLSTM, using a larger CNN-BiLSTM model as a teacher, which achieves an accuracy of 83.21% with 0.85 s detection latency. Our work represents a significant advancement towards efficient and preventative cardiovascular health management. Full article

Background and Objective: This study aimed to investigate the relationship between tramadol use and cardio electrophysiological imbalance (iCEB/iCEBc) in general surgery patients with complaints of acute postoperative pain (APP). Materials and Methods: In this prospective cross-sectional study, a total of 218 consecutive patients over the age of 18, who underwent surgical procedures in our clinic (postoperative), were included. For analgesic effect, tramadol was administered with an initial total max dose not exceeding 2 mg/kg. A single max dose (100 mg) was given intravenously, infused in 100 cc of saline over 60 min. In all patients requiring analgesia, electrocardiography (ECG) was performed in the supine position with 12 leads at 25 mm/s and 10 mm/mV, immediately before and after tramadol administration. iCEB was calculated as QT/QRS and iCEBc as QTc/QRS. Results: A total of 218 patients were included in this study, with 98 of them being male (45%) and the average age being 46.20 ± 17.19 years. The average tramadol dose for analgesic effect was 98.21 ± 7.62 mg. The QT interval (339.17 ± 36.27 vs. 349.88 ± 30.86, p = 0.001), QTc interval (407.07 ± 26.36 vs. 419.64 ± 31.78, p < 0.001), QRS duration (80.82 ± 11.39 vs. 78.57 ± 9.80, p = 0.018), iCEB (4.26 ± 0.69 vs. 4.52 ± 0.70, p < 0.001), and iCEBc (5.14 ± 0.86 vs. 5.42 ± 0.79, p = 0.001) values significantly increased compared to the baseline immediately after drug administration. Furthermore, the drug dose was identified as an independent predictor that increased iCEBc (β = 0.201, p = 0.003). Conclusions: Even at single and therapeutic doses, tramadol increases iCEB and iCEBc. Additionally, the drug dose is an independent predictor of increased iCEBc. Full article

Background and Objectives: Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). Methods: A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland–Altman plot analysis. Results: The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. Conclusions: Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems. Full article

Background: The aim of this single-center retrospective study was to evaluate the long-term outcomes after the convergent procedure (CP) for treatment of AF. Methods: We analyzed the outcomes of patients that underwent CP from January 2009 until July 2020. A total of 119 patients with paroxysmal AF (23.5%), persistent AF (5.9%), or long-standing persistent AF (70.6%) that attended long-term follow-up were included. The outcomes were assessed 1 year after the CP and at long-term follow-up. At the 1-year follow-up, rhythm and AF burden were assessed for patients with an implantable loop recorder (61.2%). For others, rhythm was assessed by clinical presentation and 12-lead ECG. At long-term follow-up, patients with sinus rhythm (SR) or an unclear history were assessed with a 7-day Holter ECG monitor, and AF burden was determined. Long-term success was defined as freedom from AF/atrial flutter (AFL) with SR on a 12-lead ECG and AF/AFL burden < 1% on the 7-day Holter ECG. Results: At 1-year follow-up, 91.4% of patients had SR and 76.1% of patients had AF/AFL burden < 1%. At long-term follow-up (8.3 ± 2.8 years), 65.5% of patients had SR and 53.8% of patients had AF/AFL burden < 1% on the 7-day Holter ECG. Additional RFAs were performed in 32.8% of patients who had AF or AFL burden < 1%. At long-term follow-up, age, body mass index, and left atrial volume index were associated with an increased risk of AF recurrence. Conclusions: CP resulted in high long-term probability of SR maintenance. During long-term follow-up, additional RFAs were required to maintain SR in a substantial number of patients. Full article

Background: Pulmonary vein isolation (PVI) is an effective management method for paroxysmal atrial fibrillation (PAF). The P wave in the 12-lead electrocardiogram (ECG) represents atrial depolarisation. This study aims to utilise the P wave to predict PVI outcomes for PAF. Methods: This single-centre retrospective study aimed to predict PVI outcomes using P wave parameters. It included 211 consecutive patients with first PVI for PAF between 2018 and 2019 and targeted the pulmonary veins (PVs). Procedure success was defined by freedom of ECG-documented AF at 12 months. Digital 12-lead ECGs with 1–50 hertz bandpass filters were monitored before the procedure. Corrected P wave duration (PWDc), P wave amplitude (PWV), P wave dispersion (PWDisp), intra-atrial block (IAB), P wave area (PWA), and P wave terminal force in V1 (PTFV1) were measured before ablation and correlated with the outcomes. Results: Successful PVI occurred in 154 patients (73%). Demographics were similar between both arms. P wave parameters correlated with PVI failure included increased PWDc in all leads except for lead III, aVR, and V3, decreased PWV in lead I (hazard ratio [HR]: 0.7, 95% confidence interval [CI]: 0.53–0.95), lead II (HR: 0.45, 95% CI: 0.22–0.65), aVL (HR: 0.58, 95% CI: 0.22–0.98), and aVF (HR: 0.67, 95% CI: 0.58–0.87), decreased PWA in lead I (HR: 0.55, 95% CI: 0.21–0.76), lead II (HR: 0.48, 95% CI: 0.34–0.87), aVL (HR: 0.65, 95% CI: 0.45–0.96), and aVF (HR: 0.61, 95% CI: 0.32–0.89), and the presence of IAB (HR: 2, 95% CI: 1.4–4.2, p = 0.02). PWDisp and PTFV1 were not correlated with PVI outcome. Conclusions: PWDc, PWA, PWV, and IAB are valuable predictors for PVI outcome for PAF at 12 months. Full article

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future. Full article

Recently, mobile healthcare is emerging using portable and wearable devices. This article uses short-single-lead ECG signals to develop an automatic AF detection system. Heart rate variability (HRV) and frequency analysis are used for feature extraction. The innovative contribution is to develop a Genetic Optimization Algorithm for detecting atrial fibrillation in short ECG recordings. The validation of the results is carried out with the publicly available dataset which comprises short ECG recordings by the support vector machine algorithm. The accuracy varies from 94.2 to 96.5 for N versus A classification under noise levels ranging up to 30 dB. A maximum accuracy of 82.7% is obtained for N versus A versus O. Full article

Introduction: Atrial fibrillation (AF), apart from non-stenotic supracardiac atherosclerosis and neoplastic disease, is the leading cause of cryptogenic stroke, including embolic stroke of un-determined source (ESUS). The aim of our study was to determine the prevalence of AF in ESUS patients based on 30-day telemetric heart rate monitoring initiated within three months after stroke onset. Another aim was to identify factors that increase the likelihood of detecting subsequent AF among ESUS patients. Material and Methods: patients with first-ever stroke classified as per the ESUS definition were eligible for this study. All patients underwent outpatient 30-day telemetric heart rate monitoring. Results: In the period between 2020 and 2022, 145 patients were included. The mean age of all qualified patients was 54; 40% of eligible patients were female. Six patients (4.14%), mostly male patients (4 vs. 2), were diagnosed with AF within the study period. In each case, the diagnosis related to a patient whose stroke occurred in the course of large vessel occlusion. Episodes of AF were detected between day 1 and 25 after starting ECG monitoring. Out of the analyzed parameters that increase the probability of, A.F.; only supraventricular extrasystoles proved to be an independent factor regarding an increased risk of AF [OR 1.046, CI 95% 1.016–1.071, p-value < 0.01]. Conclusions: The use of telemetry heart rhythm monitoring in an outpatient setting can detect AF in 4% of ESUS patients who have undergone prior diagnostic procedures for cardiogenic embolism. Supraventricular extrasystoles significantly increases the likelihood of AF detection in patients with ESUS within three months following stroke. Comorbid coronary artery disease, diabetes and hypertension, rather than a single-factor clinical burden, increase the likelihood of AF detection in older ESUS patients. ESUS in the course of large vessel occlusion is probably associated with an increased likelihood of cardiogenic embolism. Full article

With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing. Full article

Background: The electrodes of implantable cardiac devices (ICDs) may cause significant problems in cardiac computed tomography (CT) because they are a source of artifacts that obscure surrounding structures and possible pathology. There are a few million patients currently with ICDs, and some of these patients will require cardiac imaging due to coronary artery disease or problems with ICDs. Modern CT scanners can reduce some of the metal artifacts because of MAR software, but in some vendors, it does not work with ECG gating. Introduced in 2008, dual-energy CT scanners can generate virtual monoenergetic images (VMIs), which are much less susceptible to metal artifacts than standard CT images. Objective: This study aimed to evaluate if dual-energy CT can reduce metal artifacts caused by ICD leads by using VMIs. The second objective was to determine how the angle between the electrode and the plane of imaging affects the severity of the artifacts in three planes of imaging. Methods: A 3D-printed model was constructed to obtain a 0–90-degree field at 5-degree intervals between the electrode and each of the planes: axial, coronal, and sagittal. This electrode was scanned in dual-energy and single-energy protocols. VMIs with an energy of 40–140 keV with 10 keV intervals were reconstructed. The length of the two most extended artifacts originating from the tip of the electrode and 2 cm above it—at the point where the thick metallic defibrillating portion of the electrode begins—was measured. Results: For the sagittal plane, these observations were similar for both points of the ICDs that were used as the reference location. VMIs with an energy over 80 keV produce images with fewer artifacts than similar images obtained in the single-energy scanning mode. Conclusions: Virtual monoenergetic imaging techniques may reduce streak artifacts arising from ICD electrodes and improve the quality of the image. Increasing the angle of the electrode as well as the imaging plane can reduce artifacts. The angle between the electrode and the beam of X-rays can be increased by tilting the gantry of the scanner or lifting the upper body of the patient. Full article

With the development of sensor and communication technologies, the 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 emerging. For example, a driver with the smart key in their pocket can push the start button to start a car. At the same time, security issues in the push-to-start scenario are pervasive, such as smart key forgery. In this study, we propose a vehicle Passive Entry Passive Start (PEPS) system that adopts deep learning algorithms to recognize the driver using the electrocardiogram (ECG) signals measured on the driver’s smart watch. ECG signals are used for personal identification. Smart watches, serving as new smart keys of the PEPS system, can improve convenience and security. In the experiment, we consider commercial smart watches capable of sensing ECG signals. The sample rate and precision are typically lower than those of a 12-lead ECG used in hospitals. The experimental results show that Long Short-Term Memory (LSTM) models achieve the best accuracy score for identity recognition (91%) when a single ECG cycle is used. However, it takes at least 30 min for training. The training of a personalized Auto Encoder model takes only 5 min for each subject. When 15 continuous ECG cycles are sensed and used, this can achieve 100% identity accuracy. As the personalized Auto Encoder model is an unsupervised learning one-class recognizer, it can be trained using only the driver’s ECG signal. This will simplify the management of ECG recordings extremely, as well as the integration of the proposed technology into PEPS vehicles. A FIDO (Fast Identify Online)-like environment for the proposed PEPS system is discussed. Public key cryptography is adopted for communication between the smart watch and the PEPS car. The driver is first verified on the smart watch via local ECG biometric authentication, and then identified by the PEPS car. Phishing attacks, MITM (man in the middle) attacks, and replay attacks can be effectively prevented. Full article