China
Africa
Explore chapters and articles related to this topic
Stimulus-Receptive Conductive Polymers for Tissue Engineering
Published in Naznin Sultana, Sanchita Bandyopadhyay-Ghosh, Chin Fhong Soon, Tissue Engineering Strategies for Organ Regeneration, 2020
PANI has been combined with other polymers to form conductive composites or blended to gain the desired and key properties to regenerate biological substitutes to repair or replace the damaged and lost tissues for cardiac, skeletal muscle, nerve and bone tissue engineering in order to mimic as closely as possible the properties of native extracellular matrix (ECM). In view of cardiac tissue engineering, generally, the human heart is made up of electrically conductive tissue. The sinoatrial node, located in the right atrium, sends electrical impulses throughout the rest of myocardium via the atrioventricular node and Purkinje fibers. The propagation of electrical stimulation causes the contraction of cardiac cells, and initiates the heartbeat. Hence, it is important to regenerate cardiac cells in a conductive scaffold which is able to deliver these electrical stimulations to the seeded cells. Some studies have focused on conductive scaffold fabrication using composite or blended PANI for cardio tissue engineering. Cardiomyocytes were shown to adhere to and proliferate on PANI/ PLGA nanofibers (Li et al. 2006), while another study exhibited that PANI/gelatin also successfully supported cell attachment and the proliferation of H9c2.
Biopotentials and Electrophysiology Measurement
Published in John G. Webster, Halit Eren, Measurement, Instrumentation, and Sensors Handbook, 2017
Figure 64.2 illustrates the continuum of electrophysiological signals from the (a) heart cells, (b) myocardium (the heart muscle), and (c) the body surface. Each cell in the heart produces a characteristic action potential [4]. The activity of cells in the sinoatrial node of the heart produces an excitation that propagates from the atria to the ventricles through well-defined pathways and eventually throughout the heart; this electric excitation produces a synchronous contraction of the heart muscle [5]. The associated biopotential is the ECG. Electric excitation of a neuron produces an action potential that travels down its dendrites and axon [4]; activity of a massive number of neurons and their interactions within the cortical mantle results in the EEG signal [6]. Excitation of neurons transmitted via a nerve to a neuromuscular junction produces stimulation of muscle fibers. Constitutive elements of muscle fibers are the single motor units, and their electric activity is called a single motor unit potential [7]. The electric activity of large numbers of single motor unit potentials from groups of muscle fibers manifests on the body surface as the EMG. Contraction and relaxation of muscles is accompanied by proportionate EMG signals. The retina of the eye is a multilayered and rather regularly structured organ containing cells called rods and cones, cells that sense light and color. Motion of the eyeballs inside the conductive contents of the skull alters the electric potentials. Placing the electrode in the vicinity of the eyes (on either side of the eyes on the temples or above and below the eyes) picks up the potentials associated with eye movements called EOGs. Thus, it is clear that biopotentials at the cellular level play an integral role in the function of various vital organs.
Historical Development of HRV Analysis
Published in Herbert F. Jelinek, David J. Cornforth, Ahsan H. Khandoker, ECG Time Series Variability Analysis, 2017
In healthy subjects, the sinoatrial node located at the posterior wall of the right atrium initiates each beat of the heart. Due to the unstable membrane potential of the myocytes located in this region, action potentials are generated periodically at a fairly constant frequency. This relatively constant frequency generated by the autorhythmicity of the sinoatrial node is modulated by many factors that add variability to the HR signal at different frequencies (Stauss 2003; Task Force 1996) and over different scales (Cerutti et al. 2009).
Single-lead VDD pacing: a literature review on short-term and long-term performance
Published in Expert Review of Medical Devices, 2023
Davide Antonio Mei, Jacopo Francesco Imberti, Marco Vitolo, Niccolò Bonini, Luigi Gerra, Giulio Francesco Romiti, Marco Proietti, Gregory Y. H. Lip, Giuseppe Boriani
Sinus node disease is a disorder of sinoatrial node that results in a spectrum of abnormal rhythms which include bradyarrhythmias, atrial tachyarrhythmias and an alternans of the two (the so-called tachy-brady syndrome) [39]. Syncope or pre-syncope are common symptoms in SND, and they are caused by asystolic pauses. Pacing for asymptomatic SND has not been associated with a better prognosis (as opposed to pacing for AVB). Permanent pacing is indicated when SND becomes symptomatic and pacemakers capable of pacing the atria (DDD or AAI) are the cornerstone therapy [40]. VDD system does not have the possibility to pace the RA, so the exclusion of a SND is crucial before deciding to implant such type of device.
Automatic atrial fibrillation detection from short ECG signals: A hybrid deep learning approach
Published in IISE Transactions on Healthcare Systems Engineering, 2022
Xiaodan Wu, Zeyu Sui, Chao-Hsien Chu, Guanjie Huang
Under normal circumstances, the regulation of the human heart rate is mainly controlled by the autonomic rhythm of the sinoatrial node. However, in the occurrence of AF, the action potentials of various parts of the heart are in a state of disorder, and the action potential frequency of the sinoatrial node, atrioventricular node, and ventricle is significantly lower than the atrial frequency (P. Yang et al., 2018). Figure 1 depicts an example of an ECG waveform with annotations, where the ECG with healthy sinus rhythm consists of six bands of P, Q, R, S, T and U, while typical AF symptoms in the ECG are missing the P wave and exhibit absolute irregularity of the RR interval (distance between two adjacent R waves) (Harris et al., 2012; Hennig et al., 2006). AF is usually diagnosed based on the patient's medical history and clinical ECG monitoring. If patients with AF are paroxysmal with short onset time and the ECG data cannot be recorded in time, 24 to 72h of long-term ECG monitoring is required. If a patient is suspected to have AF, the common approach for detecting AF is to place electrodes on 12 different parts the body such as the limbs and sternum, to record the electrical activity on different sides of the heart. After the 12-lead ECG signals are captured, they are then either evaluated by a medical expert, or key features are extracted and then used with a heuristic rule or data mining method to detect AF (Moody & Mark, 1983). Many of the current studies use long-term ECG data from the MIT-BIH Atrial Fibrillation Database (Hamilton & Tompkins,1986) for experiments on AF detection. However, the use of long-term ECG signals for AF detection greatly increase the patient's medical expenses and time costs (Stewart et al., 2004). Therefore, this paper attempts to use the short-term AF dataset from the PhysioNet/CinC Challenges 2017 database (AliveCor, 2017) to develop an algorithm for AF detection, which can improve the detection efficiency and reduce the cost of patients' medical care and algorithm development.