Results of measurements of the cardiac micropotential energies in the amplitude-time intervals recorded by the nanosensor-based hardware and software complex

Highlights

• The method for detecting cardiac micropotentials during ECG recording has been developed.

• The method detects micropotentials on normal and pathological ECG.

• The measurement data showed high diagnostic value of the method.

• The micropotentials average energy reduced significantly after the patient’s recovery.

Abstract

The method for automatic analysis of micropotentials over the entire ECG recording has been developed and the results of measuring the energies of micropotentials in various amplitude-time intervals have been analyzed to assess the diagnostic value of the developed method. The paper presents the results of the study of micropotentials in a patient with atrial fibrillation of 4-year duration recorded by the nanosensor-based hardware and software complex in frequency range from 0 to 10 kHz, with a level of higher than 1 μV and a sampling rate of 64 kHz, whose sinus rhythm restored.

The most significant decrease in the energy of micropotentials by an order of magnitude or more was observed in the range of amplitudes (5.1–20.0) μV. The method can be employed for individual use and in hospitals for early detection of asymptomatic ischemia that often leads to sudden cardiac death.

Introduction

Cardiovascular diseases (CVD) are some of the global health matters recognized by scientists around the world [1]. According to Eurostat reports in 2017, the number of patients with CVD in Europe attained 11.3 million people [2]. The reports by the American Society of Cardiology in 2018 predicted an increase in the proportion of people suffering from CVD to 45 % of the total US population [3].

People’s unawareness of the condition of their body and the need to adjust the lifestyle in the early stages of the disease cause high coronary vascular disease (CVD) mortality rate worldwide. According to the World Health Organization, CVD accounts for deaths of 17.5 million people per year (31 % of all deaths per year) [4]. In Europe, according to official statistics, the share of deaths from CVD in 2016 amounted to 35.7 % of all deaths per year [2].

The most serious problem concerning CVD mortality rate is the phenomenon of sudden cardiac death (SCD). SCD refers to non-violent death caused by circulatory dysfunction incompatible with life with no symptoms preceding the event. The disease poses a danger due to its suddenness since its course is free of symptoms that could prevent critical condition of the patient or ensure the patient’s survival during an SCD episode. Thus, a person with CVD unaware of a possible cardiac event is in serious danger. He poses a significantly greater danger if he is involved in activities that hold him responsible for lives of other people. For example, a driver of public transport or an aircraft pilot in case of SCD endangers the lives of passengers.

From 4 to 5 million people annually die from SCD worldwide [5]. Studies by various scientific groups showed that SCD episodes are numerous among deaths from various pathologies of the cardiovascular system. Most of these cases are caused by coronary heart disease, cardiomyopathy and channelopathy [5].

Awareness of one of the SCD causes helped reduce the number of deaths through prevention of the cardiac arrest by an implantable autonomous pacemaker, which proved to be highly effective in the control of the disease [6]. Nevertheless, indications for this type of treatment are still controversial due to ambiguous criteria developed for stratification of people into different risk groups [7].

A total of 80 % of the cardiac arrest cases outside the hospital occurred in people who were not recommended pacemaker implantation [8], [9], [10].

The majority of episodes of cardiac abnormalities, including dangerous arrhythmic episodes that cause SCD, occur in people outside the hospital during their daily activities [11]. Recording of these episodes needs mobile devices designed to perform recording outside the hospital in familiar surroundings.

Among ECG recording mobile devices, ambulatory electrocardiographs are the most widely used [12], [13], [14], [15], [16]. In addition, internal loop recorders [17], [18], [19], [20], [21], [22], activity trackers [23], [24], [25], portable telemetry systems for long-term monitoring of heart activity MCOT (mobile cardiac outpatient telemetry system) are used [26]. A detailed description of devices for long-term cardiac monitoring is provided in Appendix A.

There is a tendency to increase the duration of the cardiac examination procedure and to ensure continuity of recording during the entire examination process.

The scientists of Tomsk Polytechnic University (TPU) and Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, have developed a hardware and software complex (HSC) based on high-resolution nanosensors to implement the method of real-time recording of cardiac micropotentials without filtering and averaging [27], [28], Appendix A. Nanosensors ensure high resolution of the hardware and software complex [27], [28], Appendix A.

The study of cardiac micropotentials carried out using the nanosensor-based hardware and software complex showed that duration of the majority of micropotentials ranges from 0.3 to 5.0 ms and their amplitude varies from 0.5 μV to 5.0 μV [29].

In [29], a pilot study of micropotentials recorded using the nanosensor-based hardware and software complex was carried out on the ST segment. The analysis performed in patients with different severity of myocardial infarction revealed a decrease in the number of micropotentials in patients with poor outcome, which confirms inhibited bioelectric spontaneous activity of the myocardium in contrast to the group of surviving patients.

Simson's method is used to detect the signs of SCD [30] to study late ventricular potentials (LVP) and atria late potentials (ALP). This examination requires a significant number of similar cardiac cycles to calculate the SCD criteria, which is difficult in cases of periodic arrhythmias and requires complex algorithms to determine them automatically. Thus, development of diagnostic tools to obtain a high-resolution ECG is of current relevance for outpatient devices.

To ensure a more accurate assessment of the human cardiovascular system, the developed methods and tools employ high-resolution nanosensors, which operate in extended amplitude and frequency ranges [27], [28].

In [31], [32], approaches and results of the study of SHR (super high resolution) ECG tested on volunteers and experimental animals were reported. ECG was recorded in an extended amplitude and frequency range to detect early signs of myocardial ischemia. Micropotentials were recorded in the range (5.0–20.0) μV; the frequency range was increased to 1 kHz. Simulation of myocardial ischemia in experimental animals proved that ischemia causes high-frequency micropotentials on the ECG recording.

One of the main methods for ECG signal processing is detection of cardiac pulse components, and various approaches and methods are used to determine the location of the teeth or other intervals in cardiac pulses. There is a wide variety of methods for processing ECG signals. These include the signal derivative method [33], when the determined extrema refer to the control points, including the beginning, peak and end of the waves set in accordance with the existing conditions. In addition, when processing ECG signals, digital filters are used to identify time domains of the signal [34], [35], in which the waves of the cardiac pulse are expected to be located. Also, neural networks, which are trained to carry out the search, are used to process the ECG signal data [36]. The hybrid method is employed to process the ECG signal data [37]. This method combines several approaches that allow more accurate recording of the required section of the analyzed ECG signal.

The most accurate detection of micropotentials in the cardiac pulse can be carried out on the isoline. In this case, it is necessary to identify the isoline intervals.

The most even fragments of the ECG signal are gaps between the P and Q, S and T, and T and P waves. Therefore, to analyze these fragments, it is necessary to detect the location of the beginning and end of the waves in the cardiac pulse. When analyzing ECG signals recorded in different patients, detection algorithms can provide the inaccurate location of cardiac pulse components, especially in cases of artifacts, cardiac arrhythmias, and ECG waveform changes. As a result, the micropotential (MP) study will provide inaccurate results. To solve this problem, it is essential to analyze the micropotentials over the entire signal duration.

The development of new approaches to measurement and processing of cardiac micropotentials for dynamic personalized monitoring of cardiac activity is relevant.

The aim of the paper is to present the method for automatic analysis of the dynamics of micropotentials and to analyze the results of measuring the energies of micropotentials in various amplitude-time intervals in order to assess the diagnostic value of the developed method through the example of study of micropotentials recorded in a patient with atrial fibrillation of 4-year duration using the nanosensor-based HSC.