WO2022114987A1 - Ensemble matériel-logiciel de mesures électrocardiographiques - Google Patents
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- WO2022114987A1 WO2022114987A1 PCT/RU2020/000672 RU2020000672W WO2022114987A1 WO 2022114987 A1 WO2022114987 A1 WO 2022114987A1 RU 2020000672 W RU2020000672 W RU 2020000672W WO 2022114987 A1 WO2022114987 A1 WO 2022114987A1
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Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
Definitions
- the invention relates to medical diagnostic equipment, in particular to systems for screening the state of the human cardiovascular system in real time with automatic diagnosis of pathological changes on the electrocardiogram for early detection, prevention and diagnosis of diseases of the human cardiovascular system.
- the position of R-peaks is localized by threshold detection at a level of 0.5, choosing the maximum of peaks from every five adjacent readings and determining the temporal location maximum, after that, atypical cardiocycles are removed, and then information is extracted related to the user's heartbeats.
- the main disadvantage of this method is the diagnosis of only two leads.
- the RITMER device has a number of disadvantages, in particular:
- the time of continuous recording of the ECG signal is less than 15 hours; - the user does not have access to the native ECG signal in the mobile application, except for a short 5-minute segment of the electrocardiosignal, and sees only the already formed conclusion and recommendations;
- Doctor Spyder see, for example: http://spyder-ecg.ni/#about), which is an online ECG recorder for long-term outpatient ECG monitoring and collecting complete information about the state of the heart the patient's vascular system.
- the device allows you to timely detect rhythm and conduction disorders of the heart, for example, such as:
- a wearable electronic device for obtaining electrocardiographic (ECG) measurements (RU197114U1), containing a case fixed on the user's wrist, in which are installed: a display that provides information display; a microcontroller that controls the electronic device;
- ADC which converts the signals coming from the electrodes into a digital form
- data storage means configured to store data
- ECG ECG
- a wireless communication module for transmitting data from the memory means to an external computing device
- the first electrode combined with a capacitive button, providing activation of the ECG function and control of the device
- the second and third electrodes located on the back side of the case and providing, together with the first electrode, ECG registration
- battery connector for connecting external electrodes providing registration
- the well-known prototype solution is a hardware-software complex that allows you to receive ECG sensor signals, process them and transfer them to remote (external) devices: a smartphone, tablet, or personal computer.
- the device described in patent RU197114U1 has the following declared technical characteristics: “the bandwidth of the amplitude-frequency characteristics for both channels of analog interfaces (109)-(110) is 0.67-320Hz, with amplitude tolerances of 0.67Hz-1Hz +/-10 %, 1Hz -320Hz - +/-1%. Sampling frequency of the signal is 1000Hz.”. These features do not allow implement algorithms for diagnosing complex arrhythmias based on high-resolution ECG analysis.
- the objective of the invention was to create a device (hardware-software complex) that makes it possible to implement the technology for analyzing high-resolution ECG data; to automatically detect predictors of sudden cardiac death, to detect late potentials of ventricular excitation, to increase the level of detection of pathological supraventricular rhythms up to 98.7% in automatic diagnostic mode without the participation of an expert doctor.
- the task was to transfer a large amount of data in real time without loss or distortion of the original ECG signal (when transmitting long recordings (more than 24 hours) via wireless communication channels, connection breaks are possible and, as a result, loss of time to restore the connection and retransmit data ).
- an intelligent electrocardiograph i.e. an automated system for decoding electrocardiogram (ECG) signals with a conclusion as close as possible to a medical one.
- a hardware-software complex for electrocardiographic (ECG) measurements containing a housing fixed on the user, in which are installed: a microcontroller that controls the electronic device; an analog-to-digital converter that converts the signals coming from the electrodes into a digital form; a data storage means configured to store ECG data; a wireless communication module for transmitting data from the memory means to an external computing device; the first electrode, combined with a capacitive button, providing activation of the ECG function of the device; the second and third electrodes located on the back side of the case and providing, together with the first electrode, ECG registration; battery; connector for connecting external electrodes that provide ECG recording, in which the frequency band of amplitude-frequency characteristics for all channels of analog interfaces is 0-4000 Hz, with permissible amplitude deviations from 0 to 2000 Hz ⁇ 10%, signal sampling frequency 8000Hz.
- ECG electrocardiographic
- the microcontroller and the data storage means are configured to record, read and transmit ECG data in parallel streams with the priority of the recording process.
- the microcontroller is also configured to implement direct diagnostic algorithms based on rules and decision trees.
- the microcontroller is also configured to connect to diagnostic units that implement machine learning methods.
- ADS1298IPAGR an integrated analog-to-digital converter ADS1298IPAGR, which, in addition to the converter module itself, also contains an analog amplifying part with the ability to flexibly adjust gain factors, a switching module (analogue addition and subtraction of signals) to obtain virtual potential points for taking unipolar electrocardiogram leads and source of accurate reference voltage.
- ADS1298IPAGR an integrated analog-to-digital converter ADS1298IPAGR
- STM32WB55C microcontroller which has an ARM CORTEX-M4 core, which allows to achieve low power consumption with high performance and an ISM (Industrial, Scientific, Medical) radio frequency transceiver built into the crystal, which allows digital communication using standard Bluetooth, ZigBee, Thread. Reduction of noise in combination with an increase in the spectrum of the processed signal gives more accurate results in many methods of automatic examination of electrocardiograms.
- ECG VR ECG electrocardiography
- Enhanced electrocardiography requires registration of ECG signals with a sensitivity of 50...100 mm/mV. Such amplification is required to detect low-amplitude ECG elements. This can provide additional information about the electrical activity of the myocardium and develop new diagnostic criteria that contribute to a more accurate interpretation of ECG changes.
- the frequency bandwidth can be adjusted, including expanding towards lower frequencies to 0 (with manual compensation of differential infra-low frequency interference) and towards higher frequencies up to 2000 Hz;
- High-resolution ECG data analysis technology allows you to automatically identify predictors of sudden cardiac death, detect late potentials of ventricular excitation, and increase the level of detection of pathological supraventricular rhythms up to 98.7% in automatic diagnostic mode, as well as to predict the risk of developing paroxysms of supraventricular arrhythmias.
- microfoci of necrosis and fibrosis that occur in the heart muscle against the background of repeated exacerbations of long-term coronary artery disease can be a pathogenetic substrate for the appearance of PCa.
- Such conditions lead to a delay and fragmentation of electrical signals, a slowdown in the spread of depolarization, and the appearance of late or trace ventricular activity.
- the presence of zones of delayed ventricular depolarization contributes to the occurrence of the “re-entry” phenomenon, which is the main cause of malignant ventricular arrhythmias.
- PVC can be considered as a non-invasive marker of arrhythmogenesis.
- Spectral analysis of cardiosignals - Fourier transform with decomposition of the ECG signal into composite sinusoids with different frequencies and amplitudes which makes it possible to estimate the power spectral density of the components of the cardiosignal, high-frequency components characteristic of the LPG.
- the detection of the high-frequency content of the spectrum indicates the existence of conditions for the fragmentation of the electrical activity of the ventricles and the development of arrhythmias.
- the method of spectral analysis of the LBP is a wavelet transform for compiling a frequency-time signal map, which allows for a large number of cardiocycles to receive registration of the LPT and analyze it.
- the method involves an accurate study of oscillatory processes of various periodicities, provides a sweep of the signal under study, while the frequency and coordinate are considered as independent variables.
- PVC ventricular tachycardias
- PVC can be used as a diagnostic criterion for arrhythmogenic right ventricular cardiomyopathy.
- the problem of transmitting an array of data in real time without loss is solved as follows. Data is first written to a data storage medium (non-volatile memory). If it is necessary to simultaneously write and read data, for example, when conducting a study, including data recorded an arbitrary time ago, writing and reading are performed in parallel streams with priority from the writing process.
- a data storage medium non-volatile memory
- Data during recording are structured as separate files containing short intervals, which allows you to read an arbitrary moment of the recorded ECG with high access speed. Recording is carried out in single intervals in the amount of a multiple of the page size of non-volatile memory.
- non-volatile memory chips of large volumes allow recording only in large blocks and with additional writing to an already partially written page, it is necessary to read the previously written data into the RAM, supplement them with new data, and erase the page (erasing requires time and energy costs).
- a high sampling depth of the ECG signal - 24 bits - it is required to store and transmit large amounts of information.
- the problem is solved by calculating the maximum rate of change of the signal (the first derivative of the function with respect to time) and restoring the signal by calculating the antiderivative function with respect to time.
- the absolute value of the signal in this case can be neglected because it is not involved in any diagnostic algorithm.
- FIG. Figure 2 shows an algorithm that does not require the summation of long data series, using a single Summ register to sequentially restore signal data. Not requiring memorization and calculation of the sum of all previous data.
- This algorithm makes it possible to reduce the coding bit depth of each signal sample from 24 bits (range -2147483648 ... 2147483647, corresponding to the size of a machine word of the "long" type) to 10-12 bits (range -2048 ... 2047), which reduces the amount of recorded and transmitted data by a factor of 2.6, by "packing" or register shifting data over standard memory cells (Fig. 3).
- This algorithm is easily implemented on microcontrollers with RISC a computing core (with a reduced set of instructions for the arithmetic logic unit), in which all the above operations are performed in one machine cycle and do not require the execution of lengthy algorithmic procedures, which makes it possible to realize high energy efficiency and long-term autonomous performance. It also allows you to store and transmit data with high time sampling (up to 8000 Hz).
- FIG. 4 shows an example decision tree for determining if the rhythm is sinus.
- FIG. 5 shows an example decision tree for defining sinus tachycardia and bradycardia.
- FIG. 6 shows an example decision tree for determining the deviation of the electrical axis of the heart.
- the second stage is machine learning methods, including training and testing of software on large arrays of the ECG database and on structured medical reports.
- Machine learning methods were used - algorithms that are automatically built on a large sample of "labeled data", i.e. ECG database with known findings. At the same time, in the methods of machine learning, the algorithm for making a diagnosis (the decisive function) was not laid down explicitly. The model was "adjusted” according to the data of the training sample - a set of characteristics and attributes of the patient's ECG with a known conclusion. A basic version of the "Diagnostics" ECG analysis program was developed, which allows further training.
- the database "Cardiobase” was created, including 36652 records of a standard 12-lead resting ECG in digital EDF format. ECGs were obtained from adult patients aged 17 - 80 years. ECG recordings were independently reviewed by expert physicians (cardiologists and functional diagnostics physicians) with the formation of a structured medical opinion.
- KPT key point detection method
- the algorithm for applying the DCT method included the following steps:
- the program for automatic ECG analysis was trained using machine learning methods on features obtained using VCT (see Fig. 8 (Initial ECG (12 leads) of patient U., 69 years old) and Fig. 9 (ECG with segmentation of patient U., 69 years old) years)).
- PhysioNetPTBDB https://www.physionet.org/physiobank/database/ptbdb
- PhysioNetCompetition 2017 https://physionet.org/challenge/2017.
- a technical result is achieved, which consists in the implementation of automatic pre-medical nosological post-syndromic high-precision diagnosis of pathological ECG abnormalities of various durations.
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- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Cardiology (AREA)
- Heart & Thoracic Surgery (AREA)
- Molecular Biology (AREA)
- Pathology (AREA)
- Engineering & Computer Science (AREA)
- Biomedical Technology (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Biophysics (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Veterinary Medicine (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
L'invention se rapporte aux équipements de diagnostic médical et concerne notamment des systèmes de dépistage d'état du système cardio-vasculaire (SCV) d'une personne en temps réel avec un diagnostic automatique de changements pathologiques sur un électrocardiogramme (ECG) en vue de la détection précoce, de la prévention et du diagnostic de maladies du SCV. Cet ensemble matériel-logiciel comprend un corps que l'on fixe sur l'utilisateur, et dans lequel se trouvent: un microcontrôleur, un convertisseur analogique-numérique, un moyen de stockage de données, un module de communication sans fil, des électrodes, un accumulateur, et un connecteur pour connecter des électrodes externes d'enregistrement d'ECG. La bande de fréquences des caractéristiques d'amplitude-fréquence pour tous les canaux des interfaces analogiques est de 0-4000 Hz avec un écart acceptable des amplitudes de 0 à 2000 Hz ±10%, et une discrétisation de fréquence de 8000 Hz. Le microcontrôleur et le moyen de stockage de données permettent d'enregistrer, de lire et de transmettre des données ECG dans des flux parallèles avec une priorité au processus d'enregistrement. Le microcontrôleur exécute des algorithmes directs de diagnostic qui sont basés sur des règles et des arbres de décision, et vient se connecter à des unités de diagnostic mettant en oeuvre des procédés d'apprentissage machine. Il est ainsi possible de réaliser un diagnostic automatique de premiers secours nosologique posyndromal de haute précision d'écarts pathologiques d'ECG de longueurs diverses.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| RU2020138844A RU2759404C1 (ru) | 2020-11-26 | 2020-11-26 | Аппаратно-программный комплекс электрокардиографических измерений |
| RU2020138844 | 2020-11-26 |
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| Publication Number | Publication Date |
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| WO2022114987A1 true WO2022114987A1 (fr) | 2022-06-02 |
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| Application Number | Title | Priority Date | Filing Date |
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| PCT/RU2020/000672 WO2022114987A1 (fr) | 2020-11-26 | 2020-12-09 | Ensemble matériel-logiciel de mesures électrocardiographiques |
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| Country | Link |
|---|---|
| RU (1) | RU2759404C1 (fr) |
| WO (1) | WO2022114987A1 (fr) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010039385A1 (en) * | 2000-05-10 | 2001-11-08 | Ellenz John D. | Memory loop ECG recorder with continuous recording |
| US20050004481A1 (en) * | 2003-07-01 | 2005-01-06 | Ge Medical Systems Information Technologies, Inc. | Method and apparatus for algorithm fusion of high-resolution electrocardiograms |
| US20110092834A1 (en) * | 2009-09-14 | 2011-04-21 | Imec | Analogue signal processors |
| US20150313489A1 (en) * | 2014-05-01 | 2015-11-05 | Kenergy, Inc. | Wearable Device and Method for Assessing Cardiac Wellness by Analyzing High Definition EKG Signals |
| RU197114U1 (ru) * | 2019-10-24 | 2020-04-01 | Александр Викторович Ежков | Носимое электронное устройство для получения электрокардиографических измерений |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6389308B1 (en) * | 2000-05-30 | 2002-05-14 | Vladimir Shusterman | System and device for multi-scale analysis and representation of electrocardiographic data |
| RU2283024C1 (ru) * | 2005-01-11 | 2006-09-10 | Государственное учреждение Научно-исследовательский институт кардиологии им. В.А. Алмазова Министерства здравоохранения РФ | Способ диагностики риска развития пароксизмальной фибрилляции предсердий у больных ишемической болезнью сердца |
| RU2442529C1 (ru) * | 2010-07-02 | 2012-02-20 | Закрытое акционерное общество "Рязанская радиоэлектронная компания" (ЗАО "РРК") | Способ диагностирования сердечно-сосудистой системы |
| CA2990367C (fr) * | 2015-06-25 | 2019-12-24 | Timothy Burton | Procedes et systemes utilisant une analyse mathematique et l'apprentissage automatique afin de diagnostiquer une maladie |
-
2020
- 2020-11-26 RU RU2020138844A patent/RU2759404C1/ru active
- 2020-12-09 WO PCT/RU2020/000672 patent/WO2022114987A1/fr active Application Filing
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20010039385A1 (en) * | 2000-05-10 | 2001-11-08 | Ellenz John D. | Memory loop ECG recorder with continuous recording |
| US20050004481A1 (en) * | 2003-07-01 | 2005-01-06 | Ge Medical Systems Information Technologies, Inc. | Method and apparatus for algorithm fusion of high-resolution electrocardiograms |
| US20110092834A1 (en) * | 2009-09-14 | 2011-04-21 | Imec | Analogue signal processors |
| US20150313489A1 (en) * | 2014-05-01 | 2015-11-05 | Kenergy, Inc. | Wearable Device and Method for Assessing Cardiac Wellness by Analyzing High Definition EKG Signals |
| RU197114U1 (ru) * | 2019-10-24 | 2020-04-01 | Александр Викторович Ежков | Носимое электронное устройство для получения электрокардиографических измерений |
Non-Patent Citations (1)
| Title |
|---|
| KARPLJUK E.S.: "Optimizatsiya trakta EKG na osnove sigma-delta ATSP. EBiomeditsinskie pribory i sistemy, lektronika i svyaz", TEMATICHESKY VYPUSK ''ELEKTRONIKA I NANOTEKHNOLOGII, 2009, pages 225 - 229 * |
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| RU2759404C1 (ru) | 2021-11-12 |
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