Willem Einthoven--the father of electrocardiography

Cardiovascular waveforms - can we extract more from routine signals?

Cardiovascular waveforms such as blood pressure, ECG and photoplethysmography (PPG), are routinely acquired by specialised monitoring devices. Such devices include bedside monitors, wearables and radiotelemetry which sample at very high fidelity, yet most of this numerical data is disregarded and focus tends to reside on single point averages such as the maxima, minima, amplitude, rate and intervals. Whilst, these measures are undoubtedly of value, we may be missing important information by simplifying the complex waveform signal in this way. This Special Collection showcases recent advances in the appraisal of routine signals. Ultimately, such approaches and technologies may assist in improving the accuracy and sensitivity of detecting physiological change. This, in turn, may assist with identifying efficacy or safety signals for investigational new drugs or aidpatient diagnosis and management, supporting scientific and clinical decision making.

Automated Arrhythmia Detection Based on RR Intervals

Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.

Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

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.

Why does the heart beat? The discovery of the electrical system of the heart

Why does the heart beat? This question--known as the myogenic versus neurogenic theory--dominated cardiac research in the 19th century. In 1839, Jan Evangelista Purkinje discovered gelatinous fibers in the ventricular subendocardium that he thought were muscular. Walter Gaskell, in 1886, demonstrated specialized muscle fibers joining the atria and ventricles that caused "block" when cut and found that the sinus venosus was the area of first excitation of the heart. By examining serial embryologic sections, Wilhelm His, Jr, showed that a connective tissue sheet became a bundle connecting the upper and lower cardiac chambers, the bundle of His. Sunao Tawara traced the atrioventricular (AV) bundle of His backward to find a compact node of fibers at the base of the atrial septum and forward where it connected with the bundles of cells discovered by Purkinje in 1839. Tawara concluded that this "AV connecting system" originated in the AV node, penetrated the septum as the His bundle, and then divided into left and right bundle branches that terminated in the Purkinje fibers. Martin Flack and Arthur Keith studied the conduction system of a mole and found a structure in the sinoauricular junction that histologically resembled the AV node. They felt that this was where "the dominating rhythm of the heart normally begins" and named it the sinoauricular node in 1907. The ECG of Einthoven soon brought a new understanding to the complex electrical system that makes the heart beat. In 2006 and 2007, we celebrate the 100th anniversaries of the publication of the exciting discovery of the AV and sinus nodes, truly landmarks in our understanding of cardiac structure and physiology.

From rebellious palpitations to the discovery of auricular fibrillation: contributions of Mackenzie, Lewis and Einthoven

An irregular pulse, referred to as rebellious palpitations, delirium cordis and pulsus irregularis perpetuus, was a cause of speculation by physicians since early times. It was James Mackenzie, a Scottish general practitioner in Burnley, England, utilizing an ink-writing polygraph to record and label jugular venous pulses, who would pioneer in deciphering normal and abnormal cardiac rhythms. His key observation that the jugular "A wave" was lost in a patient who went from a normal to an irregular rhythm provided the first insight into the mechanism of auricular fibrillation. Similar jugular venous and arterial pulse findings were discovered by Cushny, Edmunds and Lewis in directly observed experimental auricular fibrillation. In 1909 Lewis in England and Rothberger and Winterberg in Vienna, taking advantage of Einthoven's newly developed string galvanometer, were the first to establish electrocardiographically that auricular fibrillation was the cause of pulsus irregularis perpetuus.