Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular repolarization, leading to syncope, ventricular tachycardia, and sudden death. Differentiating LQTS genotypes is crucial for targeted management and treatment, yet conventional genetic testing remains costly and time-consuming. This study aims to improve the distinction between LQTS genotypes, particularly LQT3, through a novel electrocardiogram (ECG)-based approach. Patients with LQT3 are at elevated risk due to arrhythmia triggers associated with rest and sleep. Employing a database of genotyped long QT syndrome E-HOL-03-0480-013 ECG signals, we introduced two innovative parameterization techniques-area under the ECG curve and wave transformation into the unit circle-to classify LQT3 against LQT1 and LQT2 genotypes. Our methodology utilized single-lead ECG data with a 200 Hz sampling frequency. The support vector machine (SVM) model demonstrated the ability to discriminate LQT3 with a recall of 90% and a precision of 81%, achieving an F1-score of 0.85. This parameterization offers a potential substitute for genetic testing and is practical for low frequencies. These single-lead ECG data could enhance smartwatches' functionality and similar cardiovascular monitoring applications. The results underscore the viability of ECG morphology-based genotype classification, promising a significant step towards streamlined diagnosis and improved patient care in LQTS.
- MeSH
- Adult MeSH
- Electrocardiography * methods MeSH
- Genotype MeSH
- Humans MeSH
- Machine Learning * MeSH
- Support Vector Machine MeSH
- Long QT Syndrome * genetics diagnosis physiopathology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Ictal central apnoea is a feature of focal temporal seizures. It is implicated as a risk factor for sudden unexpected death in epilepsy (SUDEP). Here we study seizure-related apnoeas in two different models of experimental seizures, one chronic and one acute, in adult genetically-unmodified rats, to determine mechanisms of seizure-related apnoeas. Under general anaesthesia rats receive sensors for nasal temperature, hippocampal and/or neocortical potentials, and ECG or EMG for subsequent tethered video-telemetry. Tetanus neurotoxin (TeNT), injected into hippocampus during surgery, induces a chronic epileptic focus. Other implanted rats receive intraperitoneal pentylenetetrazol (PTZ) to evoke acute seizures. In chronically epileptic rats, convulsive seizures cause apnoeas (9.9 ± 5.3 s; 331 of 730 convulsive seizures in 15 rats), associated with bradyarrhythmias. Absence of EEG and ECG biomarkers exclude obstructive apnoeas. All eight TeNT-rats with diaphragm EMG have apnoeas with no evidence of obstruction, and have apnoea EMGs significantly closer to expiratory relaxation than inspiratory contraction during pre-apnoeic respiration, which we term "atonic diaphragm". Consistent with atonic diaphragm is that the pre-apnoeic nasal airflow is expiration, as it is in human ictal central apnoea. Two cases of rat sudden death occur. One, with telemetry to the end, reveals a lethal apnoea, the other only has video during the final days, which reveals cessation of breathing shortly after the last clonic epileptic movement. Telemetry following acute systemic PTZ reveals repeated seizures and seizure-related apnoeas, culminating in lethal apnoeas; ictal apnoeas are central - in 8 of 35 cases diaphragms initially contract tonically for 8.5 ± 15.0 s before relaxing, in the 27 remaining cases diaphragms are atonic throughout apnoeas. All terminal apnoeas are atonic. Differences in types of apnoea due to systemic PTZ in rats (mainly atonic) and mice (tonic) are likely species-specific. Certain genetic mouse models have apnoeas caused by tonic contraction, potentially due to expression of epileptogenic mutations throughout the brain, including in respiratory centres, in contrast with acquired focal epilepsies. We conclude that ictal apnoeas in the rat TeNT model result from atonic diaphragms. Relaxed diaphragms could be particularly helpful for therapeutic stimulation of the diaphragm to help restore respiration.
- MeSH
- Apnea physiopathology MeSH
- Diaphragm * physiopathology MeSH
- Chronic Disease MeSH
- Electroencephalography MeSH
- Rats MeSH
- Disease Models, Animal * MeSH
- Pentylenetetrazole toxicity MeSH
- Rats, Sprague-Dawley MeSH
- Muscle Relaxation physiology MeSH
- Tetanus Toxin toxicity MeSH
- Seizures * physiopathology MeSH
- Animals MeSH
- Check Tag
- Rats MeSH
- Male MeSH
- Animals MeSH
- Publication type
- Journal Article MeSH
The paper presents a validation of novel multichannel ballistocardiography (BCG) measuring system, enabling heartbeat detection from information about movements during myocardial contraction and dilatation of arteries due to blood expulsion. The proposed methology includes novel sensory system and signal processing procedure based on Wavelet transform and Hilbert transform. Because there are no existing recommendations for BCG sensor placement, the study focuses on investigation of BCG signal quality measured from eight different locations within the subject's body. The analysis of BCG signals is primarily based on heart rate (HR) calculation, for which a J-wave detection based on decision-making processes was used. Evaluation of the proposed system was made by comparing with electrocardiography (ECG) as a gold standard, when the averaged signal from all sensors reached HR detection sensitivity higher than 95% and two sensors showed a significant difference from ECG measurement.
- MeSH
- Ballistocardiography * methods MeSH
- Adult MeSH
- Electrocardiography * methods MeSH
- Humans MeSH
- Young Adult MeSH
- Signal Processing, Computer-Assisted MeSH
- Heart Rate * physiology MeSH
- Wavelet Analysis MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Advanced signal processing methods are one of the fastest developing scientific and technical areas of biomedical engineering with increasing usage in current clinical practice. This paper presents an extensive literature review of the methods for the digital signal processing of cardiac bioelectrical signals that are commonly applied in today's clinical practice. This work covers the definition of bioelectrical signals. It also covers to the extreme extent of classical and advanced approaches to the alleviation of noise contamination such as digital adaptive and non-adaptive filtering, signal decomposition methods based on blind source separation and wavelet transform.
- MeSH
- Algorithms * MeSH
- Electrocardiography * MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted MeSH
- Heart MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Review MeSH
This paper evaluates variations in solar activity and their impact on the human nervous system, including the manner in which human behavior and decision-making reflect such effects in the context of (symmetrical) social interactions. The relevant research showed that solar activity, manifesting itself through the exposure of the Earth to charged particles from the Sun, affects heart variability. The evaluation methods focused on examining the relationships between selected psychophysiological data and solar activity, which generally causes major alterations in the low-level electromagnetic field. The investigation within this paper revealed that low-level EMF changes are among the factors affecting heart rate variability and, thus, also variations at the spectral level of the rate, in the VLF, (f = 0.01-0.04 Hz), LF (f = 0.04-0.15 Hz), and HF (f = 0.15 až 0.40 Hz) bands. The results of the presented experiments can also be interpreted as an indirect explanation of sudden deaths and heart failures.
- MeSH
- Electrocardiography * MeSH
- Humans MeSH
- Solar Activity MeSH
- Heart MeSH
- Heart Rate MeSH
- Heart Failure * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
In this paper, we describe a technical design of wearable multi-sensor systems for physiological data measurement and wide medical applications, significantly impacted in telehealth. The monitors are composed of three analog front-end (AFE) devices, which assist with interfacing digital electronics to the noise-, time-sensitive physiological sensors for measuring ECG (heart-rate monitor), RR (respiration-rate monitor), SRL (skin resistivity monitor). These three types of sensors can be used separately or together and allow to determine a number of parameters for the assessment of mental and physical condition. The system is designed based on requirements for demanding environments even outside the realm of medical applications, and in accordance with Health and Safety at Work directives (89/391/CE and Seveso-II 96/82/EC) for occupational hygiene, medical, rehabilitation, sports and fitness applications.
- MeSH
- Electronic Data Processing methods instrumentation MeSH
- Biomedical Technology methods instrumentation MeSH
- Biomedical Research MeSH
- Respiratory Rate MeSH
- Mental Health MeSH
- Electrocardiography methods instrumentation MeSH
- Humans MeSH
- Wearable Electronic Devices * classification MeSH
- Heart Rate MeSH
- Telemedicine methods instrumentation MeSH
- Physical Fitness MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Review MeSH
Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.
- MeSH
- Algorithms MeSH
- Electrocardiography * MeSH
- Humans MeSH
- Respiratory Tract Diseases * MeSH
- Neural Networks, Computer MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
The most commonly used method of fetal monitoring is based on heart activity analysis. Computer-aided fetal monitoring system enables extraction of clinically important information hidden for visual interpretation-the instantaneous fetal heart rate (FHR) variability. Today's fetal monitors are based on monitoring of mechanical activity of the fetal heart by means of Doppler ultrasound technique. The FHR is determined using autocorrelation methods, and thus it has a form of evenly spaced-every 250 ms-instantaneous measurements, where some of which are incorrect or duplicate. The parameters describing a beat-to-beat FHR variability calculated from such a signal show significant errors. The aim of our research was to develop new analysis methods that will both improve an accuracy of the FHR determination and provide FHR representation as time series of events. The study was carried out on simultaneously recorded (during labor) Doppler ultrasound signal and the reference direct fetal electrocardiogram Two subranges of Doppler bandwidths were separated to describe heart wall movements and valve motions. After reduction of signal complexity by determining the Doppler ultrasound envelope, the signal was analyzed to determine the FHR. The autocorrelation method supported by a trapezoidal prediction function was used. In the final stage, two different methods were developed to provide signal representation as time series of events: the first using correction of duplicate measurements and the second based on segmentation of instantaneous periodicity measurements. Thus, it ensured the mean heart interval measurement error of only 1.35 ms. In a case of beat-to-beat variability assessment the errors ranged from -1.9% to -10.1%. Comparing the obtained values to other published results clearly confirms that the new methods provides a higher accuracy of an interval measurement and a better reliability of the FHR variability estimation.
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.
- MeSH
- Algorithms MeSH
- Biosensing Techniques * MeSH
- Deep Learning * MeSH
- Respiration MeSH
- Electrocardiography MeSH
- Entropy MeSH
- Humans MeSH
- Sleep Apnea, Obstructive MeSH
- Signal Processing, Computer-Assisted * MeSH
- Polysomnography MeSH
- Sleep Wake Disorders * MeSH
- Heart Rate MeSH
- Wavelet Analysis MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.
- MeSH
- Algorithms MeSH
- Databases, Factual MeSH
- Diagnosis, Computer-Assisted MeSH
- Electrocardiography methods MeSH
- Atrial Fibrillation diagnosis physiopathology MeSH
- Humans MeSH
- Signal Processing, Computer-Assisted MeSH
- Heart Rate physiology MeSH
- Support Vector Machine MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
